Consumers' willingness to pay for nutritional claims fighting the obesity epidemic: the case of reduced-fat and low salt cheese in Spain.

PubMed

de-Magistris, T; Lopéz-Galán, B

2016-06-01

The aim of this study was to investigate consumers' willingness to pay (WTP) for cheeses bearing reduced-fat and low salt claims in Spain. An experiment with 219 cheese consumers was conducted in the period March-May 2015. We used different versions of cheese bearing reduced-fat and low salt claims. A choice experiment was used to estimate WTP for reduced-fat and/or low salt cheeses. Participants faced eight choice sets, each consisting of two packages of cheese with different combinations of two claims. Individuals chose one of the two packages of cheese in each choice set, or decided not to choose either. Moreover, to consider possible heterogeneity in WTP across consumers, a random parameters logit model (RPL), a Chi-squared test, and analysis of variance tests were used. Spanish cheese consumers were willing to pay a positive premium for packages of cheese with reduced-fat claims (€0.538/100 g), and for cheese with reduced-fat and low salt claims (€1.15/100 g). Conversely, consumers valued low-salt content claims negatively. They preferred to pay €0.38/100 g for a conventional cheese rather than one low in salt content. As there was heterogeneity in consumers' WTP, two different consumer segments were identified. Segment 1 consisted of normal weight and younger consumers with higher incomes and levels of education, who valued low salt cheese more negatively than those individuals in Segment 2, predominantly comprising overweight and older consumers with low income and educational level. This means that individuals in Segment 1 would pay more for conventional cheese (€1/100 g) than those in Segment 2 (€0.50/100 g). However, no difference between the two segments was found in WTP for reduced-fat cheese. The findings suggest that consumers are willing to pay a price premium for a package of cheese with a reduced-fat claim or cheese with reduced-fat and low salt claims appearing together; however, they are not willing to pay for a package of cheese with only a low salt claim. In comparison with overweight people, normal weight consumers would prefer to pay more for conventional cheese than low salt cheese. Finally, the results of this study contribute to insights in the promotion of healthier food choices among consumers. In this regard, outreach activities promoted by food companies could drive consumers to increase their knowledge of the benefits of eating reduced-fat and low salt food products in relation to their health status. Copyright © 2016 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance.

PubMed

Ngo, Tuan Anh; Lu, Zhi; Carneiro, Gustavo

2017-01-01

We introduce a new methodology that combines deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance (MR) data. This combination is relevant for segmentation problems, where the visual object of interest presents large shape and appearance variations, but the annotated training set is small, which is the case for various medical image analysis applications, including the one considered in this paper. In particular, level set methods are based on shape and appearance terms that use small training sets, but present limitations for modelling the visual object variations. Deep learning methods can model such variations using relatively small amounts of annotated training, but they often need to be regularised to produce good generalisation. Therefore, the combination of these methods brings together the advantages of both approaches, producing a methodology that needs small training sets and produces accurate segmentation results. We test our methodology on the MICCAI 2009 left ventricle segmentation challenge database (containing 15 sequences for training, 15 for validation and 15 for testing), where our approach achieves the most accurate results in the semi-automated problem and state-of-the-art results for the fully automated challenge. Crown Copyright © 2016. Published by Elsevier B.V. All rights reserved.

Human body segmentation via data-driven graph cut.

PubMed

Li, Shifeng; Lu, Huchuan; Shao, Xingqing

2014-11-01

Human body segmentation is a challenging and important problem in computer vision. Existing methods usually entail a time-consuming training phase for prior knowledge learning with complex shape matching for body segmentation. In this paper, we propose a data-driven method that integrates top-down body pose information and bottom-up low-level visual cues for segmenting humans in static images within the graph cut framework. The key idea of our approach is first to exploit human kinematics to search for body part candidates via dynamic programming for high-level evidence. Then, by using the body parts classifiers, obtaining bottom-up cues of human body distribution for low-level evidence. All the evidence collected from top-down and bottom-up procedures are integrated in a graph cut framework for human body segmentation. Qualitative and quantitative experiment results demonstrate the merits of the proposed method in segmenting human bodies with arbitrary poses from cluttered backgrounds.

Sentinel lymph node mapping in minimally invasive surgery: Role of imaging with color-segmented fluorescence (CSF).

PubMed

Lopez Labrousse, Maite I; Frumovitz, Michael; Guadalupe Patrono, M; Ramirez, Pedro T

2017-09-01

Sentinel lymph node mapping, alone or in combination with pelvic lymphadenectomy, is considered a standard approach in staging of patients with cervical or endometrial cancer [1-3]. The goal of this video is to demonstrate the use of indocyanine green (ICG) and color-segmented fluorescence when performing lymphatic mapping in patients with gynecologic malignancies. Injection of ICG is performed in two cervical sites using 1mL (0.5mL superficial and deep, respectively) at the 3 and 9 o'clock position. Sentinel lymph nodes are identified intraoperatively using the Pinpoint near-infrared imaging system (Novadaq, Ontario, CA). Color-segmented fluorescence is used to image different levels of ICG uptake demonstrating higher levels of perfusion. A color key on the side of the monitor shows the colors that coordinate with different levels of ICG uptake. Color-segmented fluorescence may help surgeons identify true sentinel nodes from fatty tissue that, although absorbing fluorescent dye, does not contain true nodal tissue. It is not intended to differentiate the primary sentinel node from secondary sentinel nodes. The key ranges from low levels of ICG uptake (gray) to the highest rate of ICG uptake (red). Bilateral sentinel lymph nodes are identified along the external iliac vessels using both standard and color-segmented fluorescence. No evidence of disease was noted after ultra-staging was performed in each of the sentinel nodes. Use of ICG in sentinel lymph node mapping allows for high bilateral detection rates. Color-segmented fluorescence may increase accuracy of sentinel lymph node identification over standard fluorescent imaging. The following are the supplementary data related to this article. Copyright © 2017 Elsevier Inc. All rights reserved.

Knowledge-based low-level image analysis for computer vision systems

NASA Technical Reports Server (NTRS)

Dhawan, Atam P.; Baxi, Himanshu; Ranganath, M. V.

1988-01-01

Two algorithms for entry-level image analysis and preliminary segmentation are proposed which are flexible enough to incorporate local properties of the image. The first algorithm involves pyramid-based multiresolution processing and a strategy to define and use interlevel and intralevel link strengths. The second algorithm, which is designed for selected window processing, extracts regions adaptively using local histograms. The preliminary segmentation and a set of features are employed as the input to an efficient rule-based low-level analysis system, resulting in suboptimal meaningful segmentation.

Environmental Pollution Control: Two Views from the General Population

ERIC Educational Resources Information Center

Althoff, Phillip; Greig, William H.

1977-01-01

Citizens exhibitied concern about pollution, a low level of trust in governmental and industrial efforts, and a low level of dedication to environmental protection. Demands to clean up the environment came from one segment of the population while demands to solve the energy crisis came from other segments. (AJ)

The hybrid UNIX controller for real-time data acquisition

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huesman, R.H.; Klein, G.J.; Fleming, T.K.

1996-06-01

The authors describe a hybrid data acquisition architecture integrating a conventional UNIX workstation with CAMAC-based real-time hardware. The system combines the high-level programming simplicity and user interface of a UNIX workstation with the low-level timing control available from conventional real-time hardware. They detail this architecture as it has been implemented for control of the Donner 600-Crystal Positron Tomograph (PET600). Low-level data acquisition is carried out in this system using eight LeCroy 3588 histogrammers, which together after derandomization, acquire events at rates up to 4 MHz, and two dedicated Motorola 6809 microprocessors, which arbitrate fine timing control during acquisition. A SUNmore » Microsystems UNIX workstation is used for high-level control, allowing an easily extensible user interface in an X-Windows environment, as well as real-time communications to the low-level acquisition units. Communication between the high- and low-level units is carried out via a Jorway 73A SCSI-CAMAC crate controller and a serial interface. For this application, the hybrid configuration segments low from high-level control for ease of maintenance and provided a low-cost upgrade from dated high-level control hardware.« less

VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images.

PubMed

Chen, Hao; Dou, Qi; Yu, Lequan; Qin, Jing; Heng, Pheng-Ann

2018-04-15

Segmentation of key brain tissues from 3D medical images is of great significance for brain disease diagnosis, progression assessment and monitoring of neurologic conditions. While manual segmentation is time-consuming, laborious, and subjective, automated segmentation is quite challenging due to the complicated anatomical environment of brain and the large variations of brain tissues. We propose a novel voxelwise residual network (VoxResNet) with a set of effective training schemes to cope with this challenging problem. The main merit of residual learning is that it can alleviate the degradation problem when training a deep network so that the performance gains achieved by increasing the network depth can be fully leveraged. With this technique, our VoxResNet is built with 25 layers, and hence can generate more representative features to deal with the large variations of brain tissues than its rivals using hand-crafted features or shallower networks. In order to effectively train such a deep network with limited training data for brain segmentation, we seamlessly integrate multi-modality and multi-level contextual information into our network, so that the complementary information of different modalities can be harnessed and features of different scales can be exploited. Furthermore, an auto-context version of the VoxResNet is proposed by combining the low-level image appearance features, implicit shape information, and high-level context together for further improving the segmentation performance. Extensive experiments on the well-known benchmark (i.e., MRBrainS) of brain segmentation from 3D magnetic resonance (MR) images corroborated the efficacy of the proposed VoxResNet. Our method achieved the first place in the challenge out of 37 competitors including several state-of-the-art brain segmentation methods. Our method is inherently general and can be readily applied as a powerful tool to many brain-related studies, where accurate segmentation of brain structures is critical. Copyright © 2017 Elsevier Inc. All rights reserved.

SemVisM: semantic visualizer for medical image

NASA Astrophysics Data System (ADS)

Landaeta, Luis; La Cruz, Alexandra; Baranya, Alexander; Vidal, María.-Esther

2015-01-01

SemVisM is a toolbox that combines medical informatics and computer graphics tools for reducing the semantic gap between low-level features and high-level semantic concepts/terms in the images. This paper presents a novel strategy for visualizing medical data annotated semantically, combining rendering techniques, and segmentation algorithms. SemVisM comprises two main components: i) AMORE (A Modest vOlume REgister) to handle input data (RAW, DAT or DICOM) and to initially annotate the images using terms defined on medical ontologies (e.g., MesH, FMA or RadLex), and ii) VOLPROB (VOlume PRObability Builder) for generating the annotated volumetric data containing the classified voxels that belong to a particular tissue. SemVisM is built on top of the semantic visualizer ANISE.1

Will farmers intend to cultivate Provitamin A genetically modified (GM) cassava in Nigeria? Evidence from a k-means segmentation analysis of beliefs and attitudes.

PubMed

Oparinde, Adewale; Abdoulaye, Tahirou; Mignouna, Djana Babatima; Bamire, Adebayo Simeon

2017-01-01

Analysis of market segments within a population remains critical to agricultural systems and policy processes for targeting new innovations. Patterns in attitudes and intentions toward cultivating Provitamin A GM cassava are examined through the use of a combination of behavioural theory and k-means cluster analysis method, investigating the interrelationship among various behavioural antecedents. Using a state-level sample of smallholder cassava farmers in Nigeria, this paper identifies three distinct classes of attitude and intention denoted as low opposition, medium opposition and high opposition farmers. It was estimated that only 25% of the surveyed population of farmers was highly opposed to cultivating Provitamin A GM cassava.

Will farmers intend to cultivate Provitamin A genetically modified (GM) cassava in Nigeria? Evidence from a k-means segmentation analysis of beliefs and attitudes

PubMed Central

Abdoulaye, Tahirou; Mignouna, Djana Babatima; Bamire, Adebayo Simeon

2017-01-01

Analysis of market segments within a population remains critical to agricultural systems and policy processes for targeting new innovations. Patterns in attitudes and intentions toward cultivating Provitamin A GM cassava are examined through the use of a combination of behavioural theory and k-means cluster analysis method, investigating the interrelationship among various behavioural antecedents. Using a state-level sample of smallholder cassava farmers in Nigeria, this paper identifies three distinct classes of attitude and intention denoted as low opposition, medium opposition and high opposition farmers. It was estimated that only 25% of the surveyed population of farmers was highly opposed to cultivating Provitamin A GM cassava. PMID:28700605

Surgical gesture segmentation and recognition.

PubMed

Tao, Lingling; Zappella, Luca; Hager, Gregory D; Vidal, René

2013-01-01

Automatic surgical gesture segmentation and recognition can provide useful feedback for surgical training in robotic surgery. Most prior work in this field relies on the robot's kinematic data. Although recent work [1,2] shows that the robot's video data can be equally effective for surgical gesture recognition, the segmentation of the video into gestures is assumed to be known. In this paper, we propose a framework for joint segmentation and recognition of surgical gestures from kinematic and video data. Unlike prior work that relies on either frame-level kinematic cues, or segment-level kinematic or video cues, our approach exploits both cues by using a combined Markov/semi-Markov conditional random field (MsM-CRF) model. Our experiments show that the proposed model improves over a Markov or semi-Markov CRF when using video data alone, gives results that are comparable to state-of-the-art methods on kinematic data alone, and improves over state-of-the-art methods when combining kinematic and video data.

Polyimides From BTDA, m-PDA, and HDA

NASA Technical Reports Server (NTRS)

Delano, Chadwick B.; Kiskiras, Charles J.

1987-01-01

Aliphatic segments in polyimide backbones achieve low molding temperatures and resistance to solvents. Low molding temperatures in combination with good solvent resistance make these polymers candidates for use in aerospace applications.

Hierarchical probabilistic Gabor and MRF segmentation of brain tumours in MRI volumes.

PubMed

Subbanna, Nagesh K; Precup, Doina; Collins, D Louis; Arbel, Tal

2013-01-01

In this paper, we present a fully automated hierarchical probabilistic framework for segmenting brain tumours from multispectral human brain magnetic resonance images (MRIs) using multiwindow Gabor filters and an adapted Markov Random Field (MRF) framework. In the first stage, a customised Gabor decomposition is developed, based on the combined-space characteristics of the two classes (tumour and non-tumour) in multispectral brain MRIs in order to optimally separate tumour (including edema) from healthy brain tissues. A Bayesian framework then provides a coarse probabilistic texture-based segmentation of tumours (including edema) whose boundaries are then refined at the voxel level through a modified MRF framework that carefully separates the edema from the main tumour. This customised MRF is not only built on the voxel intensities and class labels as in traditional MRFs, but also models the intensity differences between neighbouring voxels in the likelihood model, along with employing a prior based on local tissue class transition probabilities. The second inference stage is shown to resolve local inhomogeneities and impose a smoothing constraint, while also maintaining the appropriate boundaries as supported by the local intensity difference observations. The method was trained and tested on the publicly available MICCAI 2012 Brain Tumour Segmentation Challenge (BRATS) Database [1] on both synthetic and clinical volumes (low grade and high grade tumours). Our method performs well compared to state-of-the-art techniques, outperforming the results of the top methods in cases of clinical high grade and low grade tumour core segmentation by 40% and 45% respectively.

Differences among consumer segments with regard to perceptions of comparative effectiveness research.

PubMed

Williams, Sunyna S; Frost, Sloane L

2014-11-01

To examine differences among health-related decision-making consumer segments with regard to knowledge, skills, attitudes, and behaviors pertinent to comparative effectiveness research. Data were collected via an online survey from 603 adults with chronic conditions. Consumer segment was determined using a two-item tool. Active consumers (high skills and motivation) reported the highest levels of engagement in various behaviors. Passive consumers (low skills and motivation) reported the lowest levels of engagement in various behaviors. High-effort consumers (low skills, high motivation) reported more positive attitudes and opinions and more engagement in various behaviors than did complacent consumers (high skills, low motivation). Effective translation and dissemination of comparative effectiveness research will require the development of approaches tailored to consumers with varying levels of skills and motivation.

Nutritional Control of Regreening and Degreening in Citrus Peel Segments 1

PubMed Central

Huff, Albert

1983-01-01

A method for reversibly regreening and degreening citrus epicarp in vitro using peel segments was developed. Peel segments from mature degreened fruit promptly regreened when kept in light upon agar medium containing low (15 millimolar) concentrations of sucrose. Higher concentrations of sucrose inhibited this regreening, but NO3− and certain amino acids included in the media overcame the inhibition by sucrose. However, l-serine strongly inhibited regreening. In the presence of nitrogen, sucrose promoted regreening. Peel segments from green fruit remained green on media with low concentrations of sucrose and on media with high concentrations of sucrose and 60 millimolar KNO3, but degreened in response to high concentrations of sucrose in the absence of nitrogen. Nitrate overcame the degreening effects of high sucrose concentrations in both light and dark. Peel segments were reversibly degreened and regreened by transferring the segments between appropriate media. Nitrate in the media markedly reduced the levels of endogenous sugars in the epicarp and increased endogenous amino acid levels. Sucrose in the media increased endogenous sugar levels and, in the presence of nitrate, increased endogenous amino acid levels. In the absence of nitrogen, high sucrose concentrations reduced endogenous amino acid concentrations. PMID:16663202

A combined learning algorithm for prostate segmentation on 3D CT images.

PubMed

Ma, Ling; Guo, Rongrong; Zhang, Guoyi; Schuster, David M; Fei, Baowei

2017-11-01

Segmentation of the prostate on CT images has many applications in the diagnosis and treatment of prostate cancer. Because of the low soft-tissue contrast on CT images, prostate segmentation is a challenging task. A learning-based segmentation method is proposed for the prostate on three-dimensional (3D) CT images. We combine population-based and patient-based learning methods for segmenting the prostate on CT images. Population data can provide useful information to guide the segmentation processing. Because of inter-patient variations, patient-specific information is particularly useful to improve the segmentation accuracy for an individual patient. In this study, we combine a population learning method and a patient-specific learning method to improve the robustness of prostate segmentation on CT images. We train a population model based on the data from a group of prostate patients. We also train a patient-specific model based on the data of the individual patient and incorporate the information as marked by the user interaction into the segmentation processing. We calculate the similarity between the two models to obtain applicable population and patient-specific knowledge to compute the likelihood of a pixel belonging to the prostate tissue. A new adaptive threshold method is developed to convert the likelihood image into a binary image of the prostate, and thus complete the segmentation of the gland on CT images. The proposed learning-based segmentation algorithm was validated using 3D CT volumes of 92 patients. All of the CT image volumes were manually segmented independently three times by two, clinically experienced radiologists and the manual segmentation results served as the gold standard for evaluation. The experimental results show that the segmentation method achieved a Dice similarity coefficient of 87.18 ± 2.99%, compared to the manual segmentation. By combining the population learning and patient-specific learning methods, the proposed method is effective for segmenting the prostate on 3D CT images. The prostate CT segmentation method can be used in various applications including volume measurement and treatment planning of the prostate. © 2017 American Association of Physicists in Medicine.

Active appearance model and deep learning for more accurate prostate segmentation on MRI

NASA Astrophysics Data System (ADS)

Cheng, Ruida; Roth, Holger R.; Lu, Le; Wang, Shijun; Turkbey, Baris; Gandler, William; McCreedy, Evan S.; Agarwal, Harsh K.; Choyke, Peter; Summers, Ronald M.; McAuliffe, Matthew J.

2016-03-01

Prostate segmentation on 3D MR images is a challenging task due to image artifacts, large inter-patient prostate shape and texture variability, and lack of a clear prostate boundary specifically at apex and base levels. We propose a supervised machine learning model that combines atlas based Active Appearance Model (AAM) with a Deep Learning model to segment the prostate on MR images. The performance of the segmentation method is evaluated on 20 unseen MR image datasets. The proposed method combining AAM and Deep Learning achieves a mean Dice Similarity Coefficient (DSC) of 0.925 for whole 3D MR images of the prostate using axial cross-sections. The proposed model utilizes the adaptive atlas-based AAM model and Deep Learning to achieve significant segmentation accuracy.

Current strategies for the restoration of adequate lordosis during lumbar fusion

PubMed Central

Barrey, Cédric; Darnis, Alice

2015-01-01

Not restoring the adequate lumbar lordosis during lumbar fusion surgery may result in mechanical low back pain, sagittal unbalance and adjacent segment degeneration. The objective of this work is to describe the current strategies and concepts for restoration of adequate lordosis during fusion surgery. Theoretical lordosis can be evaluated from the measurement of the pelvic incidence and from the analysis of spatial organization of the lumbar spine with 2/3 of the lordosis given by the L4-S1 segment and 85% by the L3-S1 segment. Technical aspects involve patient positioning on the operating table, release maneuvers, type of instrumentation used (rod, screw-rod connection, interbody cages), surgical sequence and the overall surgical strategy. Spinal osteotomies may be required in case of fixed kyphotic spine. AP combined surgery is particularly efficient in restoring lordosis at L5-S1 level and should be recommended. Finally, not one but several strategies may be used to achieve the need for restoration of adequate lordosis during fusion surgery. PMID:25621216

Towards quantitative imaging: stability of fully automated nodule segmentation across varied dose levels and reconstruction parameters in a low-dose CT screening patient cohort

NASA Astrophysics Data System (ADS)

Wahi-Anwar, M. Wasil; Emaminejad, Nastaran; Hoffman, John; Kim, Grace H.; Brown, Matthew S.; McNitt-Gray, Michael F.

2018-02-01

Quantitative imaging in lung cancer CT seeks to characterize nodules through quantitative features, usually from a region of interest delineating the nodule. The segmentation, however, can vary depending on segmentation approach and image quality, which can affect the extracted feature values. In this study, we utilize a fully-automated nodule segmentation method - to avoid reader-influenced inconsistencies - to explore the effects of varied dose levels and reconstruction parameters on segmentation. Raw projection CT images from a low-dose screening patient cohort (N=59) were reconstructed at multiple dose levels (100%, 50%, 25%, 10%), two slice thicknesses (1.0mm, 0.6mm), and a medium kernel. Fully-automated nodule detection and segmentation was then applied, from which 12 nodules were selected. Dice similarity coefficient (DSC) was used to assess the similarity of the segmentation ROIs of the same nodule across different reconstruction and dose conditions. Nodules at 1.0mm slice thickness and dose levels of 25% and 50% resulted in DSC values greater than 0.85 when compared to 100% dose, with lower dose leading to a lower average and wider spread of DSC values. At 0.6mm, the increased bias and wider spread of DSC values from lowering dose were more pronounced. The effects of dose reduction on DSC for CAD-segmented nodules were similar in magnitude to reducing the slice thickness from 1.0mm to 0.6mm. In conclusion, variation of dose and slice thickness can result in very different segmentations because of noise and image quality. However, there exists some stability in segmentation overlap, as even at 1mm, an image with 25% of the lowdose scan still results in segmentations similar to that seen in a full-dose scan.

Contextually guided very-high-resolution imagery classification with semantic segments

NASA Astrophysics Data System (ADS)

Zhao, Wenzhi; Du, Shihong; Wang, Qiao; Emery, William J.

2017-10-01

Contextual information, revealing relationships and dependencies between image objects, is one of the most important information for the successful interpretation of very-high-resolution (VHR) remote sensing imagery. Over the last decade, geographic object-based image analysis (GEOBIA) technique has been widely used to first divide images into homogeneous parts, and then to assign semantic labels according to the properties of image segments. However, due to the complexity and heterogeneity of VHR images, segments without semantic labels (i.e., semantic-free segments) generated with low-level features often fail to represent geographic entities (such as building roofs usually be partitioned into chimney/antenna/shadow parts). As a result, it is hard to capture contextual information across geographic entities when using semantic-free segments. In contrast to low-level features, "deep" features can be used to build robust segments with accurate labels (i.e., semantic segments) in order to represent geographic entities at higher levels. Based on these semantic segments, semantic graphs can be constructed to capture contextual information in VHR images. In this paper, semantic segments were first explored with convolutional neural networks (CNN) and a conditional random field (CRF) model was then applied to model the contextual information between semantic segments. Experimental results on two challenging VHR datasets (i.e., the Vaihingen and Beijing scenes) indicate that the proposed method is an improvement over existing image classification techniques in classification performance (overall accuracy ranges from 82% to 96%).

Internal curvature signal and noise in low- and high-level vision

PubMed Central

Grabowecky, Marcia; Kim, Yee Joon; Suzuki, Satoru

2011-01-01

How does internal processing contribute to visual pattern perception? By modeling visual search performance, we estimated internal signal and noise relevant to perception of curvature, a basic feature important for encoding of three-dimensional surfaces and objects. We used isolated, sparse, crowded, and face contexts to determine how internal curvature signal and noise depended on image crowding, lateral feature interactions, and level of pattern processing. Observers reported the curvature of a briefly flashed segment, which was presented alone (without lateral interaction) or among multiple straight segments (with lateral interaction). Each segment was presented with no context (engaging low-to-intermediate-level curvature processing), embedded within a face context as the mouth (engaging high-level face processing), or embedded within an inverted-scrambled-face context as a control for crowding. Using a simple, biologically plausible model of curvature perception, we estimated internal curvature signal and noise as the mean and standard deviation, respectively, of the Gaussian-distributed population activity of local curvature-tuned channels that best simulated behavioral curvature responses. Internal noise was increased by crowding but not by face context (irrespective of lateral interactions), suggesting prevention of noise accumulation in high-level pattern processing. In contrast, internal curvature signal was unaffected by crowding but modulated by lateral interactions. Lateral interactions (with straight segments) increased curvature signal when no contextual elements were added, but equivalent interactions reduced curvature signal when each segment was presented within a face. These opposing effects of lateral interactions are consistent with the phenomena of local-feature contrast in low-level processing and global-feature averaging in high-level processing. PMID:21209356

Cellular image segmentation using n-agent cooperative game theory

NASA Astrophysics Data System (ADS)

Dimock, Ian B.; Wan, Justin W. L.

2016-03-01

Image segmentation is an important problem in computer vision and has significant applications in the segmentation of cellular images. Many different imaging techniques exist and produce a variety of image properties which pose difficulties to image segmentation routines. Bright-field images are particularly challenging because of the non-uniform shape of the cells, the low contrast between cells and background, and imaging artifacts such as halos and broken edges. Classical segmentation techniques often produce poor results on these challenging images. Previous attempts at bright-field imaging are often limited in scope to the images that they segment. In this paper, we introduce a new algorithm for automatically segmenting cellular images. The algorithm incorporates two game theoretic models which allow each pixel to act as an independent agent with the goal of selecting their best labelling strategy. In the non-cooperative model, the pixels choose strategies greedily based only on local information. In the cooperative model, the pixels can form coalitions, which select labelling strategies that benefit the entire group. Combining these two models produces a method which allows the pixels to balance both local and global information when selecting their label. With the addition of k-means and active contour techniques for initialization and post-processing purposes, we achieve a robust segmentation routine. The algorithm is applied to several cell image datasets including bright-field images, fluorescent images and simulated images. Experiments show that the algorithm produces good segmentation results across the variety of datasets which differ in cell density, cell shape, contrast, and noise levels.

Hierarchical layered and semantic-based image segmentation using ergodicity map

NASA Astrophysics Data System (ADS)

Yadegar, Jacob; Liu, Xiaoqing

2010-04-01

Image segmentation plays a foundational role in image understanding and computer vision. Although great strides have been made and progress achieved on automatic/semi-automatic image segmentation algorithms, designing a generic, robust, and efficient image segmentation algorithm is still challenging. Human vision is still far superior compared to computer vision, especially in interpreting semantic meanings/objects in images. We present a hierarchical/layered semantic image segmentation algorithm that can automatically and efficiently segment images into hierarchical layered/multi-scaled semantic regions/objects with contextual topological relationships. The proposed algorithm bridges the gap between high-level semantics and low-level visual features/cues (such as color, intensity, edge, etc.) through utilizing a layered/hierarchical ergodicity map, where ergodicity is computed based on a space filling fractal concept and used as a region dissimilarity measurement. The algorithm applies a highly scalable, efficient, and adaptive Peano- Cesaro triangulation/tiling technique to decompose the given image into a set of similar/homogenous regions based on low-level visual cues in a top-down manner. The layered/hierarchical ergodicity map is built through a bottom-up region dissimilarity analysis. The recursive fractal sweep associated with the Peano-Cesaro triangulation provides efficient local multi-resolution refinement to any level of detail. The generated binary decomposition tree also provides efficient neighbor retrieval mechanisms for contextual topological object/region relationship generation. Experiments have been conducted within the maritime image environment where the segmented layered semantic objects include the basic level objects (i.e. sky/land/water) and deeper level objects in the sky/land/water surfaces. Experimental results demonstrate the proposed algorithm has the capability to robustly and efficiently segment images into layered semantic objects/regions with contextual topological relationships.

A Tracker for Broken and Closely-Spaced Lines

DTIC Science & Technology

1997-10-01

to combine the current level flow estimate and the previous level flow estimate. However, the result is still not good enough for some reasons. First...geometric attributes are not good enough to discriminate line segments, when they are crowded, parallel and closely-spaced to each other. On the other...level information [10]. Still, it is not good at dealing with closely-spaced line segments. Because it requires a proper size of square neighborhood to

A novel fully automatic multilevel thresholding technique based on optimized intuitionistic fuzzy sets and tsallis entropy for MR brain tumor image segmentation.

PubMed

Kaur, Taranjit; Saini, Barjinder Singh; Gupta, Savita

2018-03-01

In the present paper, a hybrid multilevel thresholding technique that combines intuitionistic fuzzy sets and tsallis entropy has been proposed for the automatic delineation of the tumor from magnetic resonance images having vague boundaries and poor contrast. This novel technique takes into account both the image histogram and the uncertainty information for the computation of multiple thresholds. The benefit of the methodology is that it provides fast and improved segmentation for the complex tumorous images with imprecise gray levels. To further boost the computational speed, the mutation based particle swarm optimization is used that selects the most optimal threshold combination. The accuracy of the proposed segmentation approach has been validated on simulated, real low-grade glioma tumor volumes taken from MICCAI brain tumor segmentation (BRATS) challenge 2012 dataset and the clinical tumor images, so as to corroborate its generality and novelty. The designed technique achieves an average Dice overlap equal to 0.82010, 0.78610 and 0.94170 for three datasets. Further, a comparative analysis has also been made between the eight existing multilevel thresholding implementations so as to show the superiority of the designed technique. In comparison, the results indicate a mean improvement in Dice by an amount equal to 4.00% (p < 0.005), 9.60% (p < 0.005) and 3.58% (p < 0.005), respectively in contrast to the fuzzy tsallis approach.

The effect of lane line width and contrast upon lanekeeping.

PubMed

McKnight, A S; McKnight, A J; Tippetts, A S

1998-09-01

The combined effect of lane line width and line-pavement contrast upon lanekeeping was studied through simulation. Some 124 subjects, ages 17-79 (x = 56.30), 52% male, each performed 42 trials over road segments representing three levels of width crossed with 14 line-pavement contrast ratios. Lanekeeping performance was recorded in terms of heading error, position error, lane excursions and road excursions. Subjects were stratified into two levels of ability on a combined measure of visual, attentional and psychomotor variables known to decline with age. Contrast and width had a negligible effect upon performance except at very low contrast ratios, ca 1.02 at high pavement luminance levels (e.g. concrete) and 1.04 for very low luminance levels (e.g. asphalt). These ratios are similar to those encountered at night on wet roads. Mean overall performance error at the low contrast ratios increased by a factor of 1.6, 1.8 and 2.2 for 8, 6 and 4" widths, respectively. Lower ability subjects exhibited greater error at almost all contrast ratios, with no consistent relationship between degree of decrement and either width or contrast. The results suggest that lane line width and contrast have a negligible effect upon lanekeeping performance except at extremely low levels of contrast, where both have large effects. Further research in the roadway environment is needed to determine the relationships of line width and contrast ratio to lanekeeping on normal and degraded surface conditions.

Modeling Mental Health Information Preferences During the Early Adult Years: A Discrete Choice Conjoint Experiment

PubMed Central

Cunningham, Charles E.; Walker, John R.; Eastwood, John D.; Westra, Henny; Rimas, Heather; Chen, Yvonne; Marcus, Madalyn; Swinson, Richard P.; Bracken, Keyna

2013-01-01

Although most young adults with mood and anxiety disorders do not seek treatment, those who are better informed about mental health problems are more likely to use services. The authors used conjoint analysis to model strategies for providing information about anxiety and depression to young adults. Participants (N = 1,035) completed 17 choice tasks presenting combinations of 15 four-level attributes of a mental health information strategy. Latent class analysis yielded 3 segments. The virtual segment (28.7%) preferred working independently on the Internet to obtain information recommended by young adults who had experienced anxiety or depression. Self-assessment options and links to service providers were more important to this segment. Conventional participants (30.1%) preferred books or pamphlets recommended by a doctor, endorsed by mental health professionals, and used with a doctor's support. They would devote more time to information acquisition but were less likely to use Internet social networking options. Brief sources of information were more important to the low interest segment (41.2%). All segments preferred information about alternative ways to reduce anxiety or depression rather than psychological approaches or medication. Maximizing the use of information requires active and passive approaches delivered through old-media (e.g. books) and new-media (e.g., Internet) channels. PMID:24266450

Modeling mental health information preferences during the early adult years: a discrete choice conjoint experiment.

PubMed

Cunningham, Charles E; Walker, John R; Eastwood, John D; Westra, Henny; Rimas, Heather; Chen, Yvonne; Marcus, Madalyn; Swinson, Richard P; Bracken, Keyna; The Mobilizing Minds Research Group

2014-04-01

Although most young adults with mood and anxiety disorders do not seek treatment, those who are better informed about mental health problems are more likely to use services. The authors used conjoint analysis to model strategies for providing information about anxiety and depression to young adults. Participants (N = 1,035) completed 17 choice tasks presenting combinations of 15 four-level attributes of a mental health information strategy. Latent class analysis yielded 3 segments. The virtual segment (28.7%) preferred working independently on the Internet to obtain information recommended by young adults who had experienced anxiety or depression. Self-assessment options and links to service providers were more important to this segment. Conventional participants (30.1%) preferred books or pamphlets recommended by a doctor, endorsed by mental health professionals, and used with a doctor's support. They would devote more time to information acquisition but were less likely to use Internet social networking options. Brief sources of information were more important to the low interest segment (41.2%). All segments preferred information about alternative ways to reduce anxiety or depression rather than psychological approaches or medication. Maximizing the use of information requires active and passive approaches delivered through old-media (e.g., books) and new-media (e.g., Internet) channels.

Feasibility and Biomechanics of Multilevel Arthroplasty and Combined Cervical Arthrodesis and Arthroplasty.

PubMed

Safavi-Abbasi, Sam; Reyes, Phillip M; Abjornson, Celeste; Crawford, Neil R

2016-12-01

A new experimental protocol was applied utilizing a simplified postural control model. Multiple constructs were tested nondestructively by interconnecting segmental rods to screws. To investigate how posture and distribution of segmental angles under physiological loads are affected by combined cervical arthroplasty and fusion. Previous studies of biomechanics of multilevel arthroplasty have focused on range of motion and intradiscal pressure. No previous study has investigated postural changes and segmental angle distribution. In 7 human cadaveric C3-T1 specimens, C4-C5, C5-C6, and C6-C7 disks were replaced with ProDisc-C (Synthes). Combinations of fusion (f) adjacent to arthroplasty (A) were simulated at C4-C5, C5-C6, and C6-C7, respectively: fAA, AfA, AAf, ffA, fAf, Aff, fff. C3-C4 and C7-T1 remained intact. A compressive belt apparatus simulated normal muscle cocontraction and gravitational preload; C3-C4, C4-C5, C5-C6, C6-C7, and C7-T1 motions were tracked independently. Parameters studied were segmental postural compensation, neutral buckling, and shift in sagittal plane instantaneous axis of rotation (IAR). With one or more levels unfused, the arthroplasty levels preferentially moved toward upright posture before the intact levels. Neutral buckling was greatest for 3-level arthroplasty, less for 2-level arthroplasty, and least for 1-level arthroplasty. Among the three 1-level arthroplasty groups (ffA, fAf, Aff), arthroplasty at the caudalmost level resulted in significantly greater buckling than with arthroplasty rostralmost or at mid-segment (P<0.04, analysis of variance/Holm-Sidak). Although IAR location was related to buckling, this correlation did not reach significance (P=0.112). Arthroplasty levels provide the "path of least resistance," through which the initial motion is more likely to occur. The tendency for specimens to buckle under vertical compression became greater with more arthroplasty levels. Buckling appeared more severe with arthroplasty more caudal. Buckling only moderately correlated to shifts in IAR, meaning slight malpositioning of the devices would not necessarily cause buckling.

Fully convolutional network with cluster for semantic segmentation

NASA Astrophysics Data System (ADS)

Ma, Xiao; Chen, Zhongbi; Zhang, Jianlin

2018-04-01

At present, image semantic segmentation technology has been an active research topic for scientists in the field of computer vision and artificial intelligence. Especially, the extensive research of deep neural network in image recognition greatly promotes the development of semantic segmentation. This paper puts forward a method based on fully convolutional network, by cluster algorithm k-means. The cluster algorithm using the image's low-level features and initializing the cluster centers by the super-pixel segmentation is proposed to correct the set of points with low reliability, which are mistakenly classified in great probability, by the set of points with high reliability in each clustering regions. This method refines the segmentation of the target contour and improves the accuracy of the image segmentation.

Combined magnitude and phase-based segmentation of the cerebral cortex in 7T MR images of the elderly.

PubMed

Doan, Nhat Trung; van Rooden, Sanneke; Versluis, Maarten J; Webb, Andrew G; van der Grond, Jeroen; van Buchem, Mark A; Reiber, Johan H C; Milles, Julien

2012-07-01

To propose a new method that integrates both magnitude and phase information obtained from magnetic resonance (MR) T*(2) -weighted scans for cerebral cortex segmentation of the elderly. This method makes use of K-means clustering on magnitude and phase images to compute an initial segmentation, which is further refined by means of transformation with reconstruction criteria. The method was evaluated against the manual segmentation of 7T in vivo MR data of 20 elderly subjects (age = 67.7 ± 10.9). The added value of combining magnitude and phase was also evaluated by comparing the performance of the proposed method with the results obtained when limiting the available data to either magnitude or phase. The proposed method shows good overlap agreement, as quantified by the Dice Index (0.79 ± 0.04), limited bias (average relative volume difference = 2.94%), and reasonable volumetric correlation (R = 0.555, p = 0.011). Using the combined magnitude and phase information significantly improves the segmentation accuracy compared with using either magnitude or phase. This study suggests that the proposed method is an accurate and robust approach for cerebral cortex segmentation in datasets presenting low gray/white matter contrast. Copyright © 2012 Wiley Periodicals, Inc.

An automated segmentation methodology for quantifying immunoreactive puncta number and fluorescence intensity in tissue sections.

PubMed

Fish, Kenneth N; Sweet, Robert A; Deo, Anthony J; Lewis, David A

2008-11-13

A number of human brain diseases have been associated with disturbances in the structure and function of cortical synapses. Answering fundamental questions about the synaptic machinery in these disease states requires the ability to image and quantify small synaptic structures in tissue sections and to evaluate protein levels at these major sites of function. We developed a new automated segmentation imaging method specifically to answer such fundamental questions. The method takes advantage of advances in spinning disk confocal microscopy, and combines information from multiple iterations of a fluorescence intensity/morphological segmentation protocol to construct three-dimensional object masks of immunoreactive (IR) puncta. This new methodology is unique in that high- and low-fluorescing IR puncta are equally masked, allowing for quantification of the number of fluorescently-labeled puncta in tissue sections. In addition, the shape of the final object masks highly represents their corresponding original data. Thus, the object masks can be used to extract information about the IR puncta (e.g., average fluorescence intensity of proteins of interest). Importantly, the segmentation method presented can be easily adapted for use with most existing microscopy analysis packages.

Deep reactive ion etching of 4H-SiC via cyclic SF6/O2 segments

NASA Astrophysics Data System (ADS)

Luna, Lunet E.; Tadjer, Marko J.; Anderson, Travis J.; Imhoff, Eugene A.; Hobart, Karl D.; Kub, Fritz J.

2017-10-01

Cycles of inductively coupled SF6/O2 plasma with low (9%) and high (90%) oxygen content etch segments are used to produce up to 46.6 µm-deep trenches with 5.5 µm-wide openings in single-crystalline 4H-SiC substrates. The low oxygen content segment serves to etch deep in SiC whereas the high oxygen content segment serves to etch SiC at a slower rate, targeting carbon-rich residues on the surface as the combination of carbon-rich and fluorinated residues impact sidewall profile. The cycles work in concert to etch past 30 µm at an etch rate of ~0.26 µm min-1 near room temperature, while maintaining close to vertical sidewalls, high aspect ratio, and high mask selectivity. In addition, power ramps during the low oxygen content segment is used to produce a 1:1 ratio of mask opening to trench bottom width. The effect of process parameters such as cycle time and backside substrate cooling on etch depth and micromasking of the electroplated nickel etch mask are investigated.

Lung segmentation from HRCT using united geometric active contours

NASA Astrophysics Data System (ADS)

Liu, Junwei; Li, Chuanfu; Xiong, Jin; Feng, Huanqing

2007-12-01

Accurate lung segmentation from high resolution CT images is a challenging task due to various detail tracheal structures, missing boundary segments and complex lung anatomy. One popular method is based on gray-level threshold, however its results are usually rough. A united geometric active contours model based on level set is proposed for lung segmentation in this paper. Particularly, this method combines local boundary information and region statistical-based model synchronously: 1) Boundary term ensures the integrality of lung tissue.2) Region term makes the level set function evolve with global characteristic and independent on initial settings. A penalizing energy term is introduced into the model, which forces the level set function evolving without re-initialization. The method is found to be much more efficient in lung segmentation than other methods that are only based on boundary or region. Results are shown by 3D lung surface reconstruction, which indicates that the method will play an important role in the design of computer-aided diagnostic (CAD) system.

Design and Experimental Validation of a Simple Controller for a Multi-Segment Magnetic Crawler Robot

DTIC Science & Technology

2015-04-01

Ave, Cambridge, MA USA 02139; bSpace and Naval Warfare (SPAWAR) Systems Center Pacific, San Diego, CA USA 92152 ABSTRACT A novel, multi-segmented...high-level, autonomous control computer. A low-level, embedded microcomputer handles the commands to the driving motors. This paper presents the...to be demonstrated.14 The Unmanned Systems Group at SPAWAR Systems Center Pacific has developed a multi-segment magnetic crawler robot (MSMR

Biomedical image segmentation using geometric deformable models and metaheuristics.

PubMed

Mesejo, Pablo; Valsecchi, Andrea; Marrakchi-Kacem, Linda; Cagnoni, Stefano; Damas, Sergio

2015-07-01

This paper describes a hybrid level set approach for medical image segmentation. This new geometric deformable model combines region- and edge-based information with the prior shape knowledge introduced using deformable registration. Our proposal consists of two phases: training and test. The former implies the learning of the level set parameters by means of a Genetic Algorithm, while the latter is the proper segmentation, where another metaheuristic, in this case Scatter Search, derives the shape prior. In an experimental comparison, this approach has shown a better performance than a number of state-of-the-art methods when segmenting anatomical structures from different biomedical image modalities. Copyright © 2013 Elsevier Ltd. All rights reserved.

Automated diagnosis of interstitial lung diseases and emphysema in MDCT imaging

NASA Astrophysics Data System (ADS)

Fetita, Catalin; Chang Chien, Kuang-Che; Brillet, Pierre-Yves; Prêteux, Françoise

2007-09-01

Diffuse lung diseases (DLD) include a heterogeneous group of non-neoplasic disease resulting from damage to the lung parenchyma by varying patterns of inflammation. Characterization and quantification of DLD severity using MDCT, mainly in interstitial lung diseases and emphysema, is an important issue in clinical research for the evaluation of new therapies. This paper develops a 3D automated approach for detection and diagnosis of diffuse lung diseases such as fibrosis/honeycombing, ground glass and emphysema. The proposed methodology combines multi-resolution 3D morphological filtering (exploiting the sup-constrained connection cost operator) and graph-based classification for a full characterization of the parenchymal tissue. The morphological filtering performs a multi-level segmentation of the low- and medium-attenuated lung regions as well as their classification with respect to a granularity criterion (multi-resolution analysis). The original intensity range of the CT data volume is thus reduced in the segmented data to a number of levels equal to the resolution depth used (generally ten levels). The specificity of such morphological filtering is to extract tissue patterns locally contrasting with their neighborhood and of size inferior to the resolution depth, while preserving their original shape. A multi-valued hierarchical graph describing the segmentation result is built-up according to the resolution level and the adjacency of the different segmented components. The graph nodes are then enriched with the textural information carried out by their associated components. A graph analysis-reorganization based on the nodes attributes delivers the final classification of the lung parenchyma in normal and ILD/emphysematous regions. It also makes possible to discriminate between different types, or development stages, among the same class of diseases.

Line drawing extraction from gray level images by feature integration

NASA Astrophysics Data System (ADS)

Yoo, Hoi J.; Crevier, Daniel; Lepage, Richard; Myler, Harley R.

1994-10-01

We describe procedures that extract line drawings from digitized gray level images, without use of domain knowledge, by modeling preattentive and perceptual organization functions of the human visual system. First, edge points are identified by standard low-level processing, based on the Canny edge operator. Edge points are then linked into single-pixel thick straight- line segments and circular arcs: this operation serves to both filter out isolated and highly irregular segments, and to lump the remaining points into a smaller number of structures for manipulation by later stages of processing. The next stages consist in linking the segments into a set of closed boundaries, which is the system's definition of a line drawing. According to the principles of Gestalt psychology, closure allows us to organize the world by filling in the gaps in a visual stimulation so as to perceive whole objects instead of disjoint parts. To achieve such closure, the system selects particular features or combinations of features by methods akin to those of preattentive processing in humans: features include gaps, pairs of straight or curved parallel lines, L- and T-junctions, pairs of symmetrical lines, and the orientation and length of single lines. These preattentive features are grouped into higher-level structures according to the principles of proximity, similarity, closure, symmetry, and feature conjunction. Achieving closure may require supplying missing segments linking contour concavities. Choices are made between competing structures on the basis of their overall compliance with the principles of closure and symmetry. Results include clean line drawings of curvilinear manufactured objects. The procedures described are part of a system called VITREO (viewpoint-independent 3-D recognition and extraction of objects).

Development and Implementation of a Corriedale Ovine Brain Atlas for Use in Atlas-Based Segmentation.

PubMed

Liyanage, Kishan Andre; Steward, Christopher; Moffat, Bradford Armstrong; Opie, Nicholas Lachlan; Rind, Gil Simon; John, Sam Emmanuel; Ronayne, Stephen; May, Clive Newton; O'Brien, Terence John; Milne, Marjorie Eileen; Oxley, Thomas James

2016-01-01

Segmentation is the process of partitioning an image into subdivisions and can be applied to medical images to isolate anatomical or pathological areas for further analysis. This process can be done manually or automated by the use of image processing computer packages. Atlas-based segmentation automates this process by the use of a pre-labelled template and a registration algorithm. We developed an ovine brain atlas that can be used as a model for neurological conditions such as Parkinson's disease and focal epilepsy. 17 female Corriedale ovine brains were imaged in-vivo in a 1.5T (low-resolution) MRI scanner. 13 of the low-resolution images were combined using a template construction algorithm to form a low-resolution template. The template was labelled to form an atlas and tested by comparing manual with atlas-based segmentations against the remaining four low-resolution images. The comparisons were in the form of similarity metrics used in previous segmentation research. Dice Similarity Coefficients were utilised to determine the degree of overlap between eight independent, manual and atlas-based segmentations, with values ranging from 0 (no overlap) to 1 (complete overlap). For 7 of these 8 segmented areas, we achieved a Dice Similarity Coefficient of 0.5-0.8. The amygdala was difficult to segment due to its variable location and similar intensity to surrounding tissues resulting in Dice Coefficients of 0.0-0.2. We developed a low resolution ovine brain atlas with eight clinically relevant areas labelled. This brain atlas performed comparably to prior human atlases described in the literature and to intra-observer error providing an atlas that can be used to guide further research using ovine brains as a model and is hosted online for public access.

Auto-segmentation of normal and target structures in head and neck CT images: a feature-driven model-based approach.

PubMed

Qazi, Arish A; Pekar, Vladimir; Kim, John; Xie, Jason; Breen, Stephen L; Jaffray, David A

2011-11-01

Intensity modulated radiation therapy (IMRT) allows greater control over dose distribution, which leads to a decrease in radiation related toxicity. IMRT, however, requires precise and accurate delineation of the organs at risk and target volumes. Manual delineation is tedious and suffers from both interobserver and intraobserver variability. State of the art auto-segmentation methods are either atlas-based, model-based or hybrid however, robust fully automated segmentation is often difficult due to the insufficient discriminative information provided by standard medical imaging modalities for certain tissue types. In this paper, the authors present a fully automated hybrid approach which combines deformable registration with the model-based approach to accurately segment normal and target tissues from head and neck CT images. The segmentation process starts by using an average atlas to reliably identify salient landmarks in the patient image. The relationship between these landmarks and the reference dataset serves to guide a deformable registration algorithm, which allows for a close initialization of a set of organ-specific deformable models in the patient image, ensuring their robust adaptation to the boundaries of the structures. Finally, the models are automatically fine adjusted by our boundary refinement approach which attempts to model the uncertainty in model adaptation using a probabilistic mask. This uncertainty is subsequently resolved by voxel classification based on local low-level organ-specific features. To quantitatively evaluate the method, they auto-segment several organs at risk and target tissues from 10 head and neck CT images. They compare the segmentations to the manual delineations outlined by the expert. The evaluation is carried out by estimating two common quantitative measures on 10 datasets: volume overlap fraction or the Dice similarity coefficient (DSC), and a geometrical metric, the median symmetric Hausdorff distance (HD), which is evaluated slice-wise. They achieve an average overlap of 93% for the mandible, 91% for the brainstem, 83% for the parotids, 83% for the submandibular glands, and 74% for the lymph node levels. Our automated segmentation framework is able to segment anatomy in the head and neck region with high accuracy within a clinically-acceptable segmentation time.

Low-Grade Glioma Segmentation Based on CNN with Fully Connected CRF

PubMed Central

Li, Zeju; Shi, Zhifeng; Guo, Yi; Chen, Liang; Mao, Ying

2017-01-01

This work proposed a novel automatic three-dimensional (3D) magnetic resonance imaging (MRI) segmentation method which would be widely used in the clinical diagnosis of the most common and aggressive brain tumor, namely, glioma. The method combined a multipathway convolutional neural network (CNN) and fully connected conditional random field (CRF). Firstly, 3D information was introduced into the CNN which makes more accurate recognition of glioma with low contrast. Then, fully connected CRF was added as a postprocessing step which purposed more delicate delineation of glioma boundary. The method was applied to T2flair MRI images of 160 low-grade glioma patients. With 59 cases of data training and manual segmentation as the ground truth, the Dice similarity coefficient (DSC) of our method was 0.85 for the test set of 101 MRI images. The results of our method were better than those of another state-of-the-art CNN method, which gained the DSC of 0.76 for the same dataset. It proved that our method could produce better results for the segmentation of low-grade gliomas. PMID:29065666

Low-Grade Glioma Segmentation Based on CNN with Fully Connected CRF.

PubMed

Li, Zeju; Wang, Yuanyuan; Yu, Jinhua; Shi, Zhifeng; Guo, Yi; Chen, Liang; Mao, Ying

2017-01-01

This work proposed a novel automatic three-dimensional (3D) magnetic resonance imaging (MRI) segmentation method which would be widely used in the clinical diagnosis of the most common and aggressive brain tumor, namely, glioma. The method combined a multipathway convolutional neural network (CNN) and fully connected conditional random field (CRF). Firstly, 3D information was introduced into the CNN which makes more accurate recognition of glioma with low contrast. Then, fully connected CRF was added as a postprocessing step which purposed more delicate delineation of glioma boundary. The method was applied to T2flair MRI images of 160 low-grade glioma patients. With 59 cases of data training and manual segmentation as the ground truth, the Dice similarity coefficient (DSC) of our method was 0.85 for the test set of 101 MRI images. The results of our method were better than those of another state-of-the-art CNN method, which gained the DSC of 0.76 for the same dataset. It proved that our method could produce better results for the segmentation of low-grade gliomas.

A summary of image segmentation techniques

NASA Technical Reports Server (NTRS)

Spirkovska, Lilly

1993-01-01

Machine vision systems are often considered to be composed of two subsystems: low-level vision and high-level vision. Low level vision consists primarily of image processing operations performed on the input image to produce another image with more favorable characteristics. These operations may yield images with reduced noise or cause certain features of the image to be emphasized (such as edges). High-level vision includes object recognition and, at the highest level, scene interpretation. The bridge between these two subsystems is the segmentation system. Through segmentation, the enhanced input image is mapped into a description involving regions with common features which can be used by the higher level vision tasks. There is no theory on image segmentation. Instead, image segmentation techniques are basically ad hoc and differ mostly in the way they emphasize one or more of the desired properties of an ideal segmenter and in the way they balance and compromise one desired property against another. These techniques can be categorized in a number of different groups including local vs. global, parallel vs. sequential, contextual vs. noncontextual, interactive vs. automatic. In this paper, we categorize the schemes into three main groups: pixel-based, edge-based, and region-based. Pixel-based segmentation schemes classify pixels based solely on their gray levels. Edge-based schemes first detect local discontinuities (edges) and then use that information to separate the image into regions. Finally, region-based schemes start with a seed pixel (or group of pixels) and then grow or split the seed until the original image is composed of only homogeneous regions. Because there are a number of survey papers available, we will not discuss all segmentation schemes. Rather than a survey, we take the approach of a detailed overview. We focus only on the more common approaches in order to give the reader a flavor for the variety of techniques available yet present enough details to facilitate implementation and experimentation.

A Regions of Confidence Based Approach to Enhance Segmentation with Shape Priors.

PubMed

Appia, Vikram V; Ganapathy, Balaji; Abufadel, Amer; Yezzi, Anthony; Faber, Tracy

2010-01-18

We propose an improved region based segmentation model with shape priors that uses labels of confidence/interest to exclude the influence of certain regions in the image that may not provide useful information for segmentation. These could be regions in the image which are expected to have weak, missing or corrupt edges or they could be regions in the image which the user is not interested in segmenting, but are part of the object being segmented. In the training datasets, along with the manual segmentations we also generate an auxiliary map indicating these regions of low confidence/interest. Since, all the training images are acquired under similar conditions, we can train our algorithm to estimate these regions as well. Based on this training we will generate a map which indicates the regions in the image that are likely to contain no useful information for segmentation. We then use a parametric model to represent the segmenting curve as a combination of shape priors obtained by representing the training data as a collection of signed distance functions. We evolve an objective energy functional to evolve the global parameters that are used to represent the curve. We vary the influence each pixel has on the evolution of these parameters based on the confidence/interest label. When we use these labels to indicate the regions with low confidence; the regions containing accurate edges will have a dominant role in the evolution of the curve and the segmentation in the low confidence regions will be approximated based on the training data. Since our model evolves global parameters, it improves the segmentation even in the regions with accurate edges. This is because we eliminate the influence of the low confidence regions which may mislead the final segmentation. Similarly when we use the labels to indicate the regions which are not of importance, we will get a better segmentation of the object in the regions we are interested in.

A variational approach to liver segmentation using statistics from multiple sources

NASA Astrophysics Data System (ADS)

Zheng, Shenhai; Fang, Bin; Li, Laquan; Gao, Mingqi; Wang, Yi

2018-01-01

Medical image segmentation plays an important role in digital medical research, and therapy planning and delivery. However, the presence of noise and low contrast renders automatic liver segmentation an extremely challenging task. In this study, we focus on a variational approach to liver segmentation in computed tomography scan volumes in a semiautomatic and slice-by-slice manner. In this method, one slice is selected and its connected component liver region is determined manually to initialize the subsequent automatic segmentation process. From this guiding slice, we execute the proposed method downward to the last one and upward to the first one, respectively. A segmentation energy function is proposed by combining the statistical shape prior, global Gaussian intensity analysis, and enforced local statistical feature under the level set framework. During segmentation, the shape of the liver shape is estimated by minimization of this function. The improved Chan-Vese model is used to refine the shape to capture the long and narrow regions of the liver. The proposed method was verified on two independent public databases, the 3D-IRCADb and the SLIVER07. Among all the tested methods, our method yielded the best volumetric overlap error (VOE) of 6.5 +/- 2.8 % , the best root mean square symmetric surface distance (RMSD) of 2.1 +/- 0.8 mm, the best maximum symmetric surface distance (MSD) of 18.9 +/- 8.3 mm in 3D-IRCADb dataset, and the best average symmetric surface distance (ASD) of 0.8 +/- 0.5 mm, the best RMSD of 1.5 +/- 1.1 mm in SLIVER07 dataset, respectively. The results of the quantitative comparison show that the proposed liver segmentation method achieves competitive segmentation performance with state-of-the-art techniques.

Pulmonary Lobe Segmentation with Probabilistic Segmentation of the Fissures and a Groupwise Fissure Prior

PubMed Central

Bragman, Felix J.S.; McClelland, Jamie R.; Jacob, Joseph; Hurst, John R.; Hawkes, David J.

2017-01-01

A fully automated, unsupervised lobe segmentation algorithm is presented based on a probabilistic segmentation of the fissures and the simultaneous construction of a population model of the fissures. A two-class probabilistic segmentation segments the lung into candidate fissure voxels and the surrounding parenchyma. This was combined with anatomical information and a groupwise fissure prior to drive non-parametric surface fitting to obtain the final segmentation. The performance of our fissure segmentation was validated on 30 patients from the COPDGene cohort, achieving a high median F1-score of 0.90 and showed general insensitivity to filter parameters. We evaluated our lobe segmentation algorithm on the LOLA11 dataset, which contains 55 cases at varying levels of pathology. We achieved the highest score of 0.884 of the automated algorithms. Our method was further tested quantitatively and qualitatively on 80 patients from the COPDGene study at varying levels of functional impairment. Accurate segmentation of the lobes is shown at various degrees of fissure incompleteness for 96% of all cases. We also show the utility of including a groupwise prior in segmenting the lobes in regions of grossly incomplete fissures. PMID:28436850

Breast mass segmentation in mammography using plane fitting and dynamic programming.

PubMed

Song, Enmin; Jiang, Luan; Jin, Renchao; Zhang, Lin; Yuan, Yuan; Li, Qiang

2009-07-01

Segmentation is an important and challenging task in a computer-aided diagnosis (CAD) system. Accurate segmentation could improve the accuracy in lesion detection and characterization. The objective of this study is to develop and test a new segmentation method that aims at improving the performance level of breast mass segmentation in mammography, which could be used to provide accurate features for classification. This automated segmentation method consists of two main steps and combines the edge gradient, the pixel intensity, as well as the shape characteristics of the lesions to achieve good segmentation results. First, a plane fitting method was applied to a background-trend corrected region-of-interest (ROI) of a mass to obtain the edge candidate points. Second, dynamic programming technique was used to find the "optimal" contour of the mass from the edge candidate points. Area-based similarity measures based on the radiologist's manually marked annotation and the segmented region were employed as criteria to evaluate the performance level of the segmentation method. With the evaluation criteria, the new method was compared with 1) the dynamic programming method developed by Timp and Karssemeijer, and 2) the normalized cut segmentation method, based on 337 ROIs extracted from a publicly available image database. The experimental results indicate that our segmentation method can achieve a higher performance level than the other two methods, and the improvements in segmentation performance level were statistically significant. For instance, the mean overlap percentage for the new algorithm was 0.71, whereas those for Timp's dynamic programming method and the normalized cut segmentation method were 0.63 (P < .001) and 0.61 (P < .001), respectively. We developed a new segmentation method by use of plane fitting and dynamic programming, which achieved a relatively high performance level. The new segmentation method would be useful for improving the accuracy of computerized detection and classification of breast cancer in mammography.

Automatic delineation of tumor volumes by co-segmentation of combined PET/MR data

NASA Astrophysics Data System (ADS)

Leibfarth, S.; Eckert, F.; Welz, S.; Siegel, C.; Schmidt, H.; Schwenzer, N.; Zips, D.; Thorwarth, D.

2015-07-01

Combined PET/MRI may be highly beneficial for radiotherapy treatment planning in terms of tumor delineation and characterization. To standardize tumor volume delineation, an automatic algorithm for the co-segmentation of head and neck (HN) tumors based on PET/MR data was developed. Ten HN patient datasets acquired in a combined PET/MR system were available for this study. The proposed algorithm uses both the anatomical T2-weighted MR and FDG-PET data. For both imaging modalities tumor probability maps were derived, assigning each voxel a probability of being cancerous based on its signal intensity. A combination of these maps was subsequently segmented using a threshold level set algorithm. To validate the method, tumor delineations from three radiation oncologists were available. Inter-observer variabilities and variabilities between the algorithm and each observer were quantified by means of the Dice similarity index and a distance measure. Inter-observer variabilities and variabilities between observers and algorithm were found to be comparable, suggesting that the proposed algorithm is adequate for PET/MR co-segmentation. Moreover, taking into account combined PET/MR data resulted in more consistent tumor delineations compared to MR information only.

A hybrid segmentation method for partitioning the liver based on 4D DCE-MR images

NASA Astrophysics Data System (ADS)

Zhang, Tian; Wu, Zhiyi; Runge, Jurgen H.; Lavini, Cristina; Stoker, Jaap; van Gulik, Thomas; Cieslak, Kasia P.; van Vliet, Lucas J.; Vos, Frans M.

2018-03-01

The Couinaud classification of hepatic anatomy partitions the liver into eight functionally independent segments. Detection and segmentation of the hepatic vein (HV), portal vein (PV) and inferior vena cava (IVC) plays an important role in the subsequent delineation of the liver segments. To facilitate pharmacokinetic modeling of the liver based on the same data, a 4D DCE-MR scan protocol was selected. This yields images with high temporal resolution but low spatial resolution. Since the liver's vasculature consists of many tiny branches, segmentation of these images is challenging. The proposed framework starts with registration of the 4D DCE-MRI series followed by region growing from manually annotated seeds in the main branches of key blood vessels in the liver. It calculates the Pearson correlation between the time intensity curves (TICs) of a seed and all voxels. A maximum correlation map for each vessel is obtained by combining the correlation maps for all branches of the same vessel through a maximum selection per voxel. The maximum correlation map is incorporated in a level set scheme to individually delineate the main vessels. Subsequently, the eight liver segments are segmented based on three vertical intersecting planes fit through the three skeleton branches of HV and IVC's center of mass as well as a horizontal plane fit through the skeleton of PV. Our segmentation regarding delineation of the vessels is more accurate than the results of two state-of-the-art techniques on five subjects in terms of the average symmetric surface distance (ASSD) and modified Hausdorff distance (MHD). Furthermore, the proposed liver partitioning achieves large overlap with manual reference segmentations (expressed in Dice Coefficient) in all but a small minority of segments (mean values between 87% and 94% for segments 2-8). The lower mean overlap for segment 1 (72%) is due to the limited spatial resolution of our DCE-MR scan protocol.

A comparative study of automatic image segmentation algorithms for target tracking in MR-IGRT.

PubMed

Feng, Yuan; Kawrakow, Iwan; Olsen, Jeff; Parikh, Parag J; Noel, Camille; Wooten, Omar; Du, Dongsu; Mutic, Sasa; Hu, Yanle

2016-03-08

On-board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real-time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image-guided radiotherapy (MR-IGRT) system. Manual con-tours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k-means (FKM), k-harmonic means (KHM), and reaction-diffusion level set evolution (RD-LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR-TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR-TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD-LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP-TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high-contrast images (i.e., kidney), the thresholding method provided the best speed (< 1 ms) with a satisfying accuracy (Dice = 0.95). When the image contrast was low, the VR-TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and a combination of different methods for optimal segmentation with the on-board MR-IGRT system.

Automatic segmentation of right ventricle on ultrasound images using sparse matrix transform and level set

NASA Astrophysics Data System (ADS)

Qin, Xulei; Cong, Zhibin; Halig, Luma V.; Fei, Baowei

2013-03-01

An automatic framework is proposed to segment right ventricle on ultrasound images. This method can automatically segment both epicardial and endocardial boundaries from a continuous echocardiography series by combining sparse matrix transform (SMT), a training model, and a localized region based level set. First, the sparse matrix transform extracts main motion regions of myocardium as eigenimages by analyzing statistical information of these images. Second, a training model of right ventricle is registered to the extracted eigenimages in order to automatically detect the main location of the right ventricle and the corresponding transform relationship between the training model and the SMT-extracted results in the series. Third, the training model is then adjusted as an adapted initialization for the segmentation of each image in the series. Finally, based on the adapted initializations, a localized region based level set algorithm is applied to segment both epicardial and endocardial boundaries of the right ventricle from the whole series. Experimental results from real subject data validated the performance of the proposed framework in segmenting right ventricle from echocardiography. The mean Dice scores for both epicardial and endocardial boundaries are 89.1%+/-2.3% and 83.6+/-7.3%, respectively. The automatic segmentation method based on sparse matrix transform and level set can provide a useful tool for quantitative cardiac imaging.

External stent for repair of secondary tracheomalacia.

PubMed

Johnston, M R; Loeber, N; Hillyer, P; Stephenson, L W; Edmunds, L H

1980-09-01

Tracheomalacia was created in anesthetized piglets by submucosal resection of 3 to 5 tracheal cartilages. Measurements of airway pressure and flow showed that expiratory airway resistance is maximal at low lung volumes and is significantly increased by creation of the malacic segment. Cervical flexion increases expiratory airway resistance, whereas hyperextension of the neck reduces resistance toward normal. External stenting of the malacic segment reduces expiratory airway resistance, and the combination of external stenting and hyperextension restores airway resistance to normal except at low lung volume. Two patients with secondary tracheomalacia required tracheostomy and could not be decannulated after the indication for the tracheostomy was corrected. Both were successfully decannulated after external stenting of the malacic segment with rib grafts. Postoperative measurements of expiratory pulmonary resistance show a marked decrease from preoperative measurements. External stenting of symptomatic tracheomalacia reduces expiratory airway resistance by supporting and stretching the malacic segment and is preferable to prolonged internal stenting or tracheal resection.

A sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image

NASA Astrophysics Data System (ADS)

Li, Jing; Xie, Weixin; Pei, Jihong

2018-03-01

Sea-land segmentation is one of the key technologies of sea target detection in remote sensing images. At present, the existing algorithms have the problems of low accuracy, low universality and poor automatic performance. This paper puts forward a sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image removing island. Firstly, the coastline data is extracted and all of land area is labeled by using the geographic information in large-field remote sensing image. Secondly, three features (local entropy, local texture and local gradient mean) is extracted in the sea-land border area, and the three features combine a 3D feature vector. And then the MultiGaussian model is adopted to describe 3D feature vectors of sea background in the edge of the coastline. Based on this multi-gaussian sea background model, the sea pixels and land pixels near coastline are classified more precise. Finally, the coarse segmentation result and the fine segmentation result are fused to obtain the accurate sea-land segmentation. Comparing and analyzing the experimental results by subjective vision, it shows that the proposed method has high segmentation accuracy, wide applicability and strong anti-disturbance ability.

Shape-intensity prior level set combining probabilistic atlas and probability map constrains for automatic liver segmentation from abdominal CT images.

PubMed

Wang, Jinke; Cheng, Yuanzhi; Guo, Changyong; Wang, Yadong; Tamura, Shinichi

2016-05-01

Propose a fully automatic 3D segmentation framework to segment liver on challenging cases that contain the low contrast of adjacent organs and the presence of pathologies from abdominal CT images. First, all of the atlases are weighted in the selected training datasets by calculating the similarities between the atlases and the test image to dynamically generate a subject-specific probabilistic atlas for the test image. The most likely liver region of the test image is further determined based on the generated atlas. A rough segmentation is obtained by a maximum a posteriori classification of probability map, and the final liver segmentation is produced by a shape-intensity prior level set in the most likely liver region. Our method is evaluated and demonstrated on 25 test CT datasets from our partner site, and its results are compared with two state-of-the-art liver segmentation methods. Moreover, our performance results on 10 MICCAI test datasets are submitted to the organizers for comparison with the other automatic algorithms. Using the 25 test CT datasets, average symmetric surface distance is [Formula: see text] mm (range 0.62-2.12 mm), root mean square symmetric surface distance error is [Formula: see text] mm (range 0.97-3.01 mm), and maximum symmetric surface distance error is [Formula: see text] mm (range 12.73-26.67 mm) by our method. Our method on 10 MICCAI test data sets ranks 10th in all the 47 automatic algorithms on the site as of July 2015. Quantitative results, as well as qualitative comparisons of segmentations, indicate that our method is a promising tool to improve the efficiency of both techniques. The applicability of the proposed method to some challenging clinical problems and the segmentation of the liver are demonstrated with good results on both quantitative and qualitative experimentations. This study suggests that the proposed framework can be good enough to replace the time-consuming and tedious slice-by-slice manual segmentation approach.

Diagnostic lumbosacral segmental nerve blocks with local anesthetics: a prospective double-blind study on the variability and interpretation of segmental effects.

PubMed

Wolff, A P; Groen, G J; Crul, B J

2001-01-01

Selective spinal nerve infiltration blocks are used diagnostically in patients with chronic low back pain radiating into the leg. Generally, a segmental nerve block is considered successful if the pain is reduced substantially. Hypesthesia and elicited paresthesias coinciding with the presumed segmental level are used as controls. The interpretation depends on a standard dermatomal map. However, it is not clear if this interpretation is reliable enough, because standard dermatomal maps do not show the overlap of neighboring dermatomes. The goal of the present study is to establish if dissimilarities exist between areas of hypesthesia, spontaneous pain reported by the patient, pain reduction by local anesthetics, and paresthesias elicited by sensory electrostimulation. A secondary goal is to determine to what extent the interpretation is improved when the overlaps of neighboring dermatomes are taken into account. Patients suffering from chronic low back pain with pain radiating into the leg underwent lumbosacral segmental nerve root blocks at subsequent levels on separate days. Lidocaine (2%, 0.5 mL) mixed with radiopaque fluid (0.25 mL) was injected after verifying the target location using sensory and motor electrostimulation. Sensory changes (pinprick method), paresthesias (reported by the patient), and pain reduction (Numeric Rating Scale) were reported. Hypesthesia and paresthesias were registered in a standard dermatomal map and in an adapted map which included overlap of neighboring dermatomes. The relationships between spinal level of injection, extent of hypesthesia, location of paresthesias, and corresponding dermatome were assessed quantitatively. Comparison of the results between both dermatomal maps was done by paired t-tests. After inclusion, data were processed for 40 segmental nerve blocks (L2-S1) performed in 29 patients. Pain reduction was achieved in 43%. Hypesthetic areas showed a large variability in size and location, and also in comparison to paresthesias. Mean hypesthetic area amounted 2.7 +/- 1.4 (+/- SD: range, 0 to 6; standard map) and 3.6 +/- 1.8 (0 to 6; adapted map; P <.001) dermatomes. In these cases, hypesthesia in the corresponding dermatome was found in 80% (standard map) and 88% of the cases (adapted map, not significant). Paresthesias occurring in the corresponding dermatome were found in 80% (standard map) compared with 98% (adapted map, P <.001). In 85% (standard map) and 88% (adapted map), spontaneous pain was present in the dermatome corresponding to the level of local anesthetic injection. In 55% (standard map) versus 75% (adapted map, P <.005), a combination of spontaneous pain, hypesthesia, and paresthesias was found in the corresponding dermatome. Hypesthetic areas determined after lumbosacral segmental nerve blocks show a large variability in size and location compared with elicited paresthesias. Confirmation of an adequately performed segmental nerve block, determined by coexistence of hypesthesia, elicited paresthesias and pain in the presumed dermatome, is more reliable when the overlap of neighboring dermatomes is taken into account.

Detection of bone disease by hybrid SST-watershed x-ray image segmentation

NASA Astrophysics Data System (ADS)

Sanei, Saeid; Azron, Mohammad; Heng, Ong Sim

2001-07-01

Detection of diagnostic features from X-ray images is favorable due to the low cost of these images. Accurate detection of the bone metastasis region greatly assists physicians to monitor the treatment and to remove the cancerous tissue by surgery. A hybrid SST-watershed algorithm, here, efficiently detects the boundary of the diseased regions. Shortest Spanning Tree (SST), based on graph theory, is one of the most powerful tools in grey level image segmentation. The method converts the images into arbitrary-shape closed segments of distinct grey levels. To do that, the image is initially mapped to a tree. Then using RSST algorithm the image is segmented to a certain number of arbitrary-shaped regions. However, in fine segmentation, over-segmentation causes loss of objects of interest. In coarse segmentation, on the other hand, SST-based method suffers from merging the regions belonged to different objects. By applying watershed algorithm, the large segments are divided into the smaller regions based on the number of catchment's basins for each segment. The process exploits bi-level watershed concept to separate each multi-lobe region into a number of areas each corresponding to an object (in our case a cancerous region of the bone,) disregarding their homogeneity in grey level.

Correlated patterns in hydrothermal plume distribution and apparent magmatic budget along 2500 km of the Southeast Indian Ridge

USGS Publications Warehouse

Baker, Edward; Christophe Hémond,; Anne Briais,; Marcia Maia,; Scheirer, Daniel S.; Sharon L. Walker,; Tingting Wang,; Yongshun John Chen,

2014-01-01

Multiple geological processes affect the distribution of hydrothermal venting along a mid-ocean ridge. Deciphering the role of a specific process is often frustrated by simultaneous changes in other influences. Here we take advantage of the almost constant spreading rate (65–71 mm/yr) along 2500 km of the Southeast Indian Ridge (SEIR) between 77°E and 99°E to examine the spatial density of hydrothermal venting relative to regional and segment-scale changes in the apparent magmatic budget. We use 227 vertical profiles of light backscatter and (on 41 profiles) oxidation-reduction potential along 27 first and second-order ridge segments on and adjacent to the Amsterdam-St. Paul (ASP) Plateau to map ph, the fraction of casts detecting a plume. At the regional scale, venting on the five segments crossing the magma-thickened hot spot plateau is almost entirely suppressed (ph = 0.02). Conversely, the combined ph (0.34) from all other segments follows the global trend of ph versus spreading rate. Off the ASP Plateau, multisegment trends in ph track trends in the regional axial depth, high where regional depth increases and low where it decreases. At the individual segment scale, a robust correlation between ph and cross-axis inflation for first-order segments shows that different magmatic budgets among first-order segments are expressed as different levels of hydrothermal spatial density. This correlation is absent among second-order segments. Eighty-five percent of the plumes occur in eight clusters totaling ∼350 km. We hypothesize that these clusters are a minimum estimate of the length of axial melt lenses underlying this section of the SEIR.

Correlated patterns in hydrothermal plume distribution and apparent magmatic budget along 2500 km of the Southeast Indian Ridge

NASA Astrophysics Data System (ADS)

Baker, Edward T.; Hémond, Christophe; Briais, Anne; Maia, Marcia; Scheirer, Daniel S.; Walker, Sharon L.; Wang, Tingting; Chen, Yongshun John

2014-08-01

Multiple geological processes affect the distribution of hydrothermal venting along a mid-ocean ridge. Deciphering the role of a specific process is often frustrated by simultaneous changes in other influences. Here we take advantage of the almost constant spreading rate (65-71 mm/yr) along 2500 km of the Southeast Indian Ridge (SEIR) between 77°E and 99°E to examine the spatial density of hydrothermal venting relative to regional and segment-scale changes in the apparent magmatic budget. We use 227 vertical profiles of light backscatter and (on 41 profiles) oxidation-reduction potential along 27 first and second-order ridge segments on and adjacent to the Amsterdam-St. Paul (ASP) Plateau to map ph, the fraction of casts detecting a plume. At the regional scale, venting on the five segments crossing the magma-thickened hot spot plateau is almost entirely suppressed (ph = 0.02). Conversely, the combined ph (0.34) from all other segments follows the global trend of ph versus spreading rate. Off the ASP Plateau, multisegment trends in ph track trends in the regional axial depth, high where regional depth increases and low where it decreases. At the individual segment scale, a robust correlation between ph and cross-axis inflation for first-order segments shows that different magmatic budgets among first-order segments are expressed as different levels of hydrothermal spatial density. This correlation is absent among second-order segments. Eighty-five percent of the plumes occur in eight clusters totaling ˜350 km. We hypothesize that these clusters are a minimum estimate of the length of axial melt lenses underlying this section of the SEIR.

RHSEG and Subdue: Background and Preliminary Approach for Combining these Technologies for Enhanced Image Data Analysis, Mining and Knowledge Discovery

NASA Technical Reports Server (NTRS)

Tilton, James C.; Cook, Diane J.

2008-01-01

Under a project recently selected for funding by NASA's Science Mission Directorate under the Applied Information Systems Research (AISR) program, Tilton and Cook will design and implement the integration of the Subdue graph based knowledge discovery system, developed at the University of Texas Arlington and Washington State University, with image segmentation hierarchies produced by the RHSEG software, developed at NASA GSFC, and perform pilot demonstration studies of data analysis, mining and knowledge discovery on NASA data. Subdue represents a method for discovering substructures in structural databases. Subdue is devised for general-purpose automated discovery, concept learning, and hierarchical clustering, with or without domain knowledge. Subdue was developed by Cook and her colleague, Lawrence B. Holder. For Subdue to be effective in finding patterns in imagery data, the data must be abstracted up from the pixel domain. An appropriate abstraction of imagery data is a segmentation hierarchy: a set of several segmentations of the same image at different levels of detail in which the segmentations at coarser levels of detail can be produced from simple merges of regions at finer levels of detail. The RHSEG program, a recursive approximation to a Hierarchical Segmentation approach (HSEG), can produce segmentation hierarchies quickly and effectively for a wide variety of images. RHSEG and HSEG were developed at NASA GSFC by Tilton. In this presentation we provide background on the RHSEG and Subdue technologies and present a preliminary analysis on how RHSEG and Subdue may be combined to enhance image data analysis, mining and knowledge discovery.

Hybrid Method of Transvertebral Foraminotomy Combined with Anterior Cervical Decompression and Fusion for Multilevel Cervical Disease.

PubMed

Yamamoto, Yu; Hara, Masahito; Nishimura, Yusuke; Haimoto, Shoichi; Wakabayashi, Toshihiko

2018-03-15

Transvertebral foraminotomy (TVF) combined with anterior cervical decompression and fusion (ACDF) can be used to treat multilevel cervical spondylotic myelopathy and radiculopathy; however, the radiological outcomes and effectiveness of this hybrid procedure are unknown. We retrospectively assessed 22 consecutive patients treated with combined TVF and ACDF between January 2007 and May 2016. The Japanese Orthopedic Association (JOA) score and Odom's criteria were analyzed. Radiological assessment included the C2-7 sagittal Cobb angle (CA) and range of motion (ROM). The tilting angle (TA), TA ROM, and disc height (DH) of segments adjacent to the ACDF were also measured. Adjacent segment degeneration, which includes disc degeneration, was evaluated. The mean postoperative follow-up was 41.7 months. All surgeries were performed at two adjacent segments, with ACDF and TVF of the upper and lower segments, respectively. The JOA scores significantly improved. There were no significant differences in the C2-7 CA, C2-7 ROM, TA, and TA ROM, but there was a statistically significant decrease in DH of the lower adjacent segment to ACDF. Progression of disc degeneration was identified in two patients, with no progression in the criterion of adjacent segment degeneration over the follow-up. The TVF combined with ACDF produced excellent clinical results and maintained spinal alignment, albeit with a reduction in DH. TVF was safely performed at the lower segment adjacent to the ACDF, although this might result in earlier degeneration. In conclusion, this hybrid method is less invasive and beneficial for reduction of the number of fused levels.

Multiatlas segmentation of thoracic and abdominal anatomy with level set-based local search.

PubMed

Schreibmann, Eduard; Marcus, David M; Fox, Tim

2014-07-08

Segmentation of organs at risk (OARs) remains one of the most time-consuming tasks in radiotherapy treatment planning. Atlas-based segmentation methods using single templates have emerged as a practical approach to automate the process for brain or head and neck anatomy, but pose significant challenges in regions where large interpatient variations are present. We show that significant changes are needed to autosegment thoracic and abdominal datasets by combining multi-atlas deformable registration with a level set-based local search. Segmentation is hierarchical, with a first stage detecting bulk organ location, and a second step adapting the segmentation to fine details present in the patient scan. The first stage is based on warping multiple presegmented templates to the new patient anatomy using a multimodality deformable registration algorithm able to cope with changes in scanning conditions and artifacts. These segmentations are compacted in a probabilistic map of organ shape using the STAPLE algorithm. Final segmentation is obtained by adjusting the probability map for each organ type, using customized combinations of delineation filters exploiting prior knowledge of organ characteristics. Validation is performed by comparing automated and manual segmentation using the Dice coefficient, measured at an average of 0.971 for the aorta, 0.869 for the trachea, 0.958 for the lungs, 0.788 for the heart, 0.912 for the liver, 0.884 for the kidneys, 0.888 for the vertebrae, 0.863 for the spleen, and 0.740 for the spinal cord. Accurate atlas segmentation for abdominal and thoracic regions can be achieved with the usage of a multi-atlas and perstructure refinement strategy. To improve clinical workflow and efficiency, the algorithm was embedded in a software service, applying the algorithm automatically on acquired scans without any user interaction.

Improving the clinical correlation of multiple sclerosis black hole volume change by paired-scan analysis.

PubMed

Tam, Roger C; Traboulsee, Anthony; Riddehough, Andrew; Li, David K B

2012-01-01

The change in T 1-hypointense lesion ("black hole") volume is an important marker of pathological progression in multiple sclerosis (MS). Black hole boundaries often have low contrast and are difficult to determine accurately and most (semi-)automated segmentation methods first compute the T 2-hyperintense lesions, which are a superset of the black holes and are typically more distinct, to form a search space for the T 1w lesions. Two main potential sources of measurement noise in longitudinal black hole volume computation are partial volume and variability in the T 2w lesion segmentation. A paired analysis approach is proposed herein that uses registration to equalize partial volume and lesion mask processing to combine T 2w lesion segmentations across time. The scans of 247 MS patients are used to compare a selected black hole computation method with an enhanced version incorporating paired analysis, using rank correlation to a clinical variable (MS functional composite) as the primary outcome measure. The comparison is done at nine different levels of intensity as a previous study suggests that darker black holes may yield stronger correlations. The results demonstrate that paired analysis can strongly improve longitudinal correlation (from -0.148 to -0.303 in this sample) and may produce segmentations that are more sensitive to clinically relevant changes.

Biomechanical aspects of segmented arch mechanics combined with power arm for controlled anterior tooth movement: A three-dimensional finite element study.

PubMed

Ozaki, Hiroya; Tominaga, Jun-Ya; Hamanaka, Ryo; Sumi, Mayumi; Chiang, Pao-Chang; Tanaka, Motohiro; Koga, Yoshiyuki; Yoshida, Noriaki

2015-01-01

The porpose of this study was to determine the optimal length of power arms for achieving controlled anterior tooth movement in segmented arch mechanics combined with power arm. A three-dimensional finite element method was applied for the simulation of en masse anterior tooth retraction in segmented power arm mechanics. The type of tooth movement, namely, the location of center of rotation of the maxillary central incisor in association with power arm length, was calculated after the retraction force was applied. When a 0.017 × 0.022-in archwire was inserted into the 0.018-in slot bracket, bodily movement was obtained at 9.1 mm length of power arm, namely, at the level of 1.8 mm above the center of resistance. In case a 0.018 × 0.025-in full-size archwire was used, bodily movement of the tooth was produced at the power arm length of 7.0 mm, namely, at the level of 0.3 mm below the center of resistance. Segmented arch mechanics required shorter length of power arms for achieving any type of controlled anterior tooth movement as compared to sliding mechanics. Therefore, this space closing mechanics could be widely applied even for the patients whose gingivobuccal fold is shallow. The segmented arch mechanics combined with power arm could provide higher amount of moment-to-force ratio sufficient for controlled anterior tooth movement without generating friction, and vertical forces when applying retraction force parallel to the occlusal plane. It is, therefore, considered that the segmented power arm mechanics has a simple appliance design and allows more efficient and controllable tooth movement.

[RSF model optimization and its application to brain tumor segmentation in MRI].

PubMed

Cheng, Zhaoning; Song, Zhijian

2013-04-01

Magnetic resonance imaging (MRI) is usually obscure and non-uniform in gray, and the tumors inside are poorly circumscribed, hence the automatic tumor segmentation in MRI is very difficult. Region-scalable fitting (RSF) energy model is a new segmentation approach for some uneven grayscale images. However, the level set formulation (LSF) of RSF model is not suitable for the environment with different grey level distribution inside and outside the intial contour, and the complex intensity environment of MRI always makes it hard to get ideal segmentation results. Therefore, we improved the model by a new LSF and combined it with the mean shift method, which can be helpful for tumor segmentation and has better convergence and target direction. The proposed method has been utilized in a series of studies for real MRI images, and the results showed that it could realize fast, accurate and robust segmentations for brain tumors in MRI, which has great clinical significance.

The Asian enigma: predisposition for low adult BMI among people of South Asian descent.

PubMed

Nubé, Maarten

2009-04-01

To investigate the Asian enigma, the phenomenon of relatively high levels of undernutrition among children and adult women in South Asia, despite more favourable records with respect to infant mortality, women's education, food availability or other aspects of living conditions in comparison with, for example, sub-Saharan Africa. Literature has been explored to identify countries outside South Asia that are home to sizeable population segments from different ethnic backgrounds, including people of South Asian and African descent, and to compare prevalence rates of undernutrition in combination with indicators of standard of living between these various population segments. Data on adult undernutrition prevalence rates among population groups of different ethnic descent living in the same country (South Africa, Fiji and the USA) generally reveal the highest prevalence rates of low BMI among adults, males and females, from South Asian background. The relatively high rates of low BMI among adults from South Asian background cannot be explained by less favourable socio-economic characteristics, such as lower income or less access to food. It is hypothesized that there exists among adults of South Asian descent an ethnic predisposition for a low BMI. Other factors that may contribute to high levels of undernutrition in South Asia are discrimination of women and a poor dietary quality of poor households' food composition pattern. The question needs to be addressed whether the commonly used cut-off point for adult underweight (BMI < 18.5 kg/m2) is universally applicable or whether ethnic differences should be taken into account.

Automatic segmentation of right ventricular ultrasound images using sparse matrix transform and a level set

NASA Astrophysics Data System (ADS)

Qin, Xulei; Cong, Zhibin; Fei, Baowei

2013-11-01

An automatic segmentation framework is proposed to segment the right ventricle (RV) in echocardiographic images. The method can automatically segment both epicardial and endocardial boundaries from a continuous echocardiography series by combining sparse matrix transform, a training model, and a localized region-based level set. First, the sparse matrix transform extracts main motion regions of the myocardium as eigen-images by analyzing the statistical information of the images. Second, an RV training model is registered to the eigen-images in order to locate the position of the RV. Third, the training model is adjusted and then serves as an optimized initialization for the segmentation of each image. Finally, based on the initializations, a localized, region-based level set algorithm is applied to segment both epicardial and endocardial boundaries in each echocardiograph. Three evaluation methods were used to validate the performance of the segmentation framework. The Dice coefficient measures the overall agreement between the manual and automatic segmentation. The absolute distance and the Hausdorff distance between the boundaries from manual and automatic segmentation were used to measure the accuracy of the segmentation. Ultrasound images of human subjects were used for validation. For the epicardial and endocardial boundaries, the Dice coefficients were 90.8 ± 1.7% and 87.3 ± 1.9%, the absolute distances were 2.0 ± 0.42 mm and 1.79 ± 0.45 mm, and the Hausdorff distances were 6.86 ± 1.71 mm and 7.02 ± 1.17 mm, respectively. The automatic segmentation method based on a sparse matrix transform and level set can provide a useful tool for quantitative cardiac imaging.

Semi-automatic knee cartilage segmentation

NASA Astrophysics Data System (ADS)

Dam, Erik B.; Folkesson, Jenny; Pettersen, Paola C.; Christiansen, Claus

2006-03-01

Osteo-Arthritis (OA) is a very common age-related cause of pain and reduced range of motion. A central effect of OA is wear-down of the articular cartilage that otherwise ensures smooth joint motion. Quantification of the cartilage breakdown is central in monitoring disease progression and therefore cartilage segmentation is required. Recent advances allow automatic cartilage segmentation with high accuracy in most cases. However, the automatic methods still fail in some problematic cases. For clinical studies, even if a few failing cases will be averaged out in the overall results, this reduces the mean accuracy and precision and thereby necessitates larger/longer studies. Since the severe OA cases are often most problematic for the automatic methods, there is even a risk that the quantification will introduce a bias in the results. Therefore, interactive inspection and correction of these problematic cases is desirable. For diagnosis on individuals, this is even more crucial since the diagnosis will otherwise simply fail. We introduce and evaluate a semi-automatic cartilage segmentation method combining an automatic pre-segmentation with an interactive step that allows inspection and correction. The automatic step consists of voxel classification based on supervised learning. The interactive step combines a watershed transformation of the original scan with the posterior probability map from the classification step at sub-voxel precision. We evaluate the method for the task of segmenting the tibial cartilage sheet from low-field magnetic resonance imaging (MRI) of knees. The evaluation shows that the combined method allows accurate and highly reproducible correction of the segmentation of even the worst cases in approximately ten minutes of interaction.

Fast algorithm for probabilistic bone edge detection (FAPBED)

NASA Astrophysics Data System (ADS)

Scepanovic, Danilo; Kirshtein, Joshua; Jain, Ameet K.; Taylor, Russell H.

2005-04-01

The registration of preoperative CT to intra-operative reality systems is a crucial step in Computer Assisted Orthopedic Surgery (CAOS). The intra-operative sensors include 3D digitizers, fiducials, X-rays and Ultrasound (US). FAPBED is designed to process CT volumes for registration to tracked US data. Tracked US is advantageous because it is real time, noninvasive, and non-ionizing, but it is also known to have inherent inaccuracies which create the need to develop a framework that is robust to various uncertainties, and can be useful in US-CT registration. Furthermore, conventional registration methods depend on accurate and absolute segmentation. Our proposed probabilistic framework addresses the segmentation-registration duality, wherein exact segmentation is not a prerequisite to achieve accurate registration. In this paper, we develop a method for fast and automatic probabilistic bone surface (edge) detection in CT images. Various features that influence the likelihood of the surface at each spatial coordinate are combined using a simple probabilistic framework, which strikes a fair balance between a high-level understanding of features in an image and the low-level number crunching of standard image processing techniques. The algorithm evaluates different features for detecting the probability of a bone surface at each voxel, and compounds the results of these methods to yield a final, low-noise, probability map of bone surfaces in the volume. Such a probability map can then be used in conjunction with a similar map from tracked intra-operative US to achieve accurate registration. Eight sample pelvic CT scans were used to extract feature parameters and validate the final probability maps. An un-optimized fully automatic Matlab code runs in five minutes per CT volume on average, and was validated by comparison against hand-segmented gold standards. The mean probability assigned to nonzero surface points was 0.8, while nonzero non-surface points had a mean value of 0.38 indicating clear identification of surface points on average. The segmentation was also sufficiently crisp, with a full width at half maximum (FWHM) value of 1.51 voxels.

Image segmentation using local shape and gray-level appearance models

NASA Astrophysics Data System (ADS)

Seghers, Dieter; Loeckx, Dirk; Maes, Frederik; Suetens, Paul

2006-03-01

A new generic model-based segmentation scheme is presented, which can be trained from examples akin to the Active Shape Model (ASM) approach in order to acquire knowledge about the shape to be segmented and about the gray-level appearance of the object in the image. Because in the ASM approach the intensity and shape models are typically applied alternately during optimizing as first an optimal target location is selected for each landmark separately based on local gray-level appearance information only to which the shape model is fitted subsequently, the ASM may be misled in case of wrongly selected landmark locations. Instead, the proposed approach optimizes for shape and intensity characteristics simultaneously. Local gray-level appearance information at the landmark points extracted from feature images is used to automatically detect a number of plausible candidate locations for each landmark. The shape information is described by multiple landmark-specific statistical models that capture local dependencies between adjacent landmarks on the shape. The shape and intensity models are combined in a single cost function that is optimized non-iteratively using dynamic programming which allows to find the optimal landmark positions using combined shape and intensity information, without the need for initialization.

Automatized spleen segmentation in non-contrast-enhanced MR volume data using subject-specific shape priors

NASA Astrophysics Data System (ADS)

Gloger, Oliver; Tönnies, Klaus; Bülow, Robin; Völzke, Henry

2017-07-01

To develop the first fully automated 3D spleen segmentation framework derived from T1-weighted magnetic resonance (MR) imaging data and to verify its performance for spleen delineation and volumetry. This approach considers the issue of low contrast between spleen and adjacent tissue in non-contrast-enhanced MR images. Native T1-weighted MR volume data was performed on a 1.5 T MR system in an epidemiological study. We analyzed random subsamples of MR examinations without pathologies to develop and verify the spleen segmentation framework. The framework is modularized to include different kinds of prior knowledge into the segmentation pipeline. Classification by support vector machines differentiates between five different shape types in computed foreground probability maps and recognizes characteristic spleen regions in axial slices of MR volume data. A spleen-shape space generated by training produces subject-specific prior shape knowledge that is then incorporated into a final 3D level set segmentation method. Individually adapted shape-driven forces as well as image-driven forces resulting from refined foreground probability maps steer the level set successfully to the segment the spleen. The framework achieves promising segmentation results with mean Dice coefficients of nearly 0.91 and low volumetric mean errors of 6.3%. The presented spleen segmentation approach can delineate spleen tissue in native MR volume data. Several kinds of prior shape knowledge including subject-specific 3D prior shape knowledge can be used to guide segmentation processes achieving promising results.

Easy-going, rational, susceptible and struggling eaters: A segmentation study based on eating behaviour tendencies.

PubMed

Pentikäinen, Saara; Arvola, Anne; Karhunen, Leila; Pennanen, Kyösti

2018-01-01

Eating behaviour tendencies, emotional eating (EE), uncontrolled eating (UE) and cognitive restraint (CR), are associated with various indicators of physical and mental health. Therefore, it is important to understand these tendencies in order to design interventions to improve health. Previous research has mostly examined eating behaviour tendencies individually, without considering typical combinations of these tendencies or their manifestation in well-being and food choices. This study aimed to understand the interactive occurrence of EE, UE and CR in two independent populations. Finnish (n = 1060) and German (n = 1070) samples were segmented on the basis of their responses to a modified Three-Factor Eating Questionnaire (TFEQ-R15). Well-being, coping strategies and food consumption habits of the segments were studied. Segmentation revealed four segments: "Susceptible", "Easy-going", "Rational" and "Struggling". These segments were similar in both countries with regard to well-being, coping strategies and food choices. EE and UE co-occurred, and these tendencies were mainly responsible for differentiating the segments. Members of the "Rational" and "Easy-going" segments, who had low scores for EE and UE, tended to experience vitality and positive emotions in life, and contentment with their eating. By contrast, the "Susceptible" and "Struggling" segments, with more pronounced tendencies towards EE and UE, experienced lower levels of vitality and less frequently positive emotions, applied less adaptive coping strategies and experienced more discontent with eating. The results of the current study suggest that it is possible to identify segments, with differing eating habits, coping strategies and well-being on the basis of the eating behaviour tendencies EE, UE and CR. We discuss possible viewpoints for the design of interventions and food products to help people towards psychologically and physiologically healthier eating styles. Copyright © 2017 Elsevier Ltd. All rights reserved.

Automated vessel segmentation using cross-correlation and pooled covariance matrix analysis.

PubMed

Du, Jiang; Karimi, Afshin; Wu, Yijing; Korosec, Frank R; Grist, Thomas M; Mistretta, Charles A

2011-04-01

Time-resolved contrast-enhanced magnetic resonance angiography (CE-MRA) provides contrast dynamics in the vasculature and allows vessel segmentation based on temporal correlation analysis. Here we present an automated vessel segmentation algorithm including automated generation of regions of interest (ROIs), cross-correlation and pooled sample covariance matrix analysis. The dynamic images are divided into multiple equal-sized regions. In each region, ROIs for artery, vein and background are generated using an iterative thresholding algorithm based on the contrast arrival time map and contrast enhancement map. Region-specific multi-feature cross-correlation analysis and pooled covariance matrix analysis are performed to calculate the Mahalanobis distances (MDs), which are used to automatically separate arteries from veins. This segmentation algorithm is applied to a dual-phase dynamic imaging acquisition scheme where low-resolution time-resolved images are acquired during the dynamic phase followed by high-frequency data acquisition at the steady-state phase. The segmented low-resolution arterial and venous images are then combined with the high-frequency data in k-space and inverse Fourier transformed to form the final segmented arterial and venous images. Results from volunteer and patient studies demonstrate the advantages of this automated vessel segmentation and dual phase data acquisition technique. Copyright © 2011 Elsevier Inc. All rights reserved.

Computing multiple aggregation levels and contextual features for road facilities recognition using mobile laser scanning data

NASA Astrophysics Data System (ADS)

Yang, Bisheng; Dong, Zhen; Liu, Yuan; Liang, Fuxun; Wang, Yongjun

2017-04-01

In recent years, updating the inventory of road infrastructures based on field work is labor intensive, time consuming, and costly. Fortunately, vehicle-based mobile laser scanning (MLS) systems provide an efficient solution to rapidly capture three-dimensional (3D) point clouds of road environments with high flexibility and precision. However, robust recognition of road facilities from huge volumes of 3D point clouds is still a challenging issue because of complicated and incomplete structures, occlusions and varied point densities. Most existing methods utilize point or object based features to recognize object candidates, and can only extract limited types of objects with a relatively low recognition rate, especially for incomplete and small objects. To overcome these drawbacks, this paper proposes a semantic labeling framework by combing multiple aggregation levels (point-segment-object) of features and contextual features to recognize road facilities, such as road surfaces, road boundaries, buildings, guardrails, street lamps, traffic signs, roadside-trees, power lines, and cars, for highway infrastructure inventory. The proposed method first identifies ground and non-ground points, and extracts road surfaces facilities from ground points. Non-ground points are segmented into individual candidate objects based on the proposed multi-rule region growing method. Then, the multiple aggregation levels of features and the contextual features (relative positions, relative directions, and spatial patterns) associated with each candidate object are calculated and fed into a SVM classifier to label the corresponding candidate object. The recognition performance of combining multiple aggregation levels and contextual features was compared with single level (point, segment, or object) based features using large-scale highway scene point clouds. Comparative studies demonstrated that the proposed semantic labeling framework significantly improves road facilities recognition precision (90.6%) and recall (91.2%), particularly for incomplete and small objects.

Hair Follicle Bulb as a Biodosimeter for Low-Level VX Vapor Exposure: Initial Studies Validating the Presence of Potential Protein Biomarkers of Exposure in the Sprague-Dawley Rat Whisker Follicle

DTIC Science & Technology

2006-10-01

lead to false positive segmental hair analysis results.13 Due to the increased risk of false positives associated with segmental hair analysis ...to 200 mg of hair (to allow confirmation testing). 7 The segments are typically washed to remove external contaminants and the chemicals in the hair ...further confirmation. The method overcomes the false positives associated with traditional segmental hair analysis such. By measuring the

GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation.

PubMed

Bakas, Spyridon; Zeng, Ke; Sotiras, Aristeidis; Rathore, Saima; Akbari, Hamed; Gaonkar, Bilwaj; Rozycki, Martin; Pati, Sarthak; Davatzikos, Christos

2016-01-01

We present an approach for segmenting low- and high-grade gliomas in multimodal magnetic resonance imaging volumes. The proposed approach is based on a hybrid generative-discriminative model. Firstly, a generative approach based on an Expectation-Maximization framework that incorporates a glioma growth model is used to segment the brain scans into tumor, as well as healthy tissue labels. Secondly, a gradient boosting multi-class classification scheme is used to refine tumor labels based on information from multiple patients. Lastly, a probabilistic Bayesian strategy is employed to further refine and finalize the tumor segmentation based on patient-specific intensity statistics from the multiple modalities. We evaluated our approach in 186 cases during the training phase of the BRAin Tumor Segmentation (BRATS) 2015 challenge and report promising results. During the testing phase, the algorithm was additionally evaluated in 53 unseen cases, achieving the best performance among the competing methods.

Age-Related Incidence of Cervical Spondylosis in Residents of Jeju Island

PubMed Central

Yoon, Min-Geun; Park, Bong-Keun; Park, Min-Suk

2016-01-01

Study Design Cervical spine radiograms of 460 Jeju islanders. Purpose To investigate the age-matched incidences and severity of the cervical disc degeneration and associated pathologic findings. Overview of Literature Several related studies on the incidences of disc and Luschka's and facet joint degeneration have provided some basic data for clinicians. Methods Cervical radiographs of 460 (220 males and 240 females) patients in their fourth to ninth decade were analyzed. Ninety patients in their third decade were excluded because of absence of spondylotic findings. Results Overall incidence of cervical spondylosis was 47.8% (220 of 460 patients). The percentile incidences of spondylosis in the fourth, fifth, sixth, seventh, eighth and ninth decade was 13.2% (10 of 76 patients), 34.6% (37 of 107 patients), 58.9% (66 of 112 patients), 58.8% (50 of 85 patients), 70.3% (45 of 64 patients) and 75.0% (12 of 16 patients), respectively. The percentile incidences of one, two, three, four and five level spondylosis among 220 spondylosis patients was 45.5% (n=100), 34.1% (n=75), 15.0% (n=33), 4.5% (n=10), and 0.9% (n=2). Severity of disc degeneration ranged from ± to ++++, and was ± in 6.0% (24 segments), + in 49.6% (198 segments), ++ in 35.3% (141 segments), +++ in 9.0% (36 segments) and ++++ in 0.25% (one segment). Spurs and anterior ligament ossicle formed at the spondylotic segments, mostly at C4~6. The rate of posterior corporal spurs formation was very low. Olisthesis and ossification of the posterior longitudinal ligament were rarely combined with spondylosis. Cervical lordotic curve decreased gradually according to the progress of severity of spondylosis. Conclusions The incidence of cervical spondylosis and number of spondylotic segments increase, and degeneration gradually becomes more severe with age. PMID:27790313

Automatic Cell Segmentation Using a Shape-Classification Model in Immunohistochemically Stained Cytological Images

NASA Astrophysics Data System (ADS)

Shah, Shishir

This paper presents a segmentation method for detecting cells in immunohistochemically stained cytological images. A two-phase approach to segmentation is used where an unsupervised clustering approach coupled with cluster merging based on a fitness function is used as the first phase to obtain a first approximation of the cell locations. A joint segmentation-classification approach incorporating ellipse as a shape model is used as the second phase to detect the final cell contour. The segmentation model estimates a multivariate density function of low-level image features from training samples and uses it as a measure of how likely each image pixel is to be a cell. This estimate is constrained by the zero level set, which is obtained as a solution to an implicit representation of an ellipse. Results of segmentation are presented and compared to ground truth measurements.

Combined radiogrammetry and texture analysis for early diagnosis of osteoporosis using Indian and Swiss data.

PubMed

Areeckal, Anu Shaju; Kamath, Jagannath; Zawadynski, Sophie; Kocher, Michel; S, Sumam David

2018-05-26

Osteoporosis is a bone disorder characterized by bone loss and decreased bone strength. The most widely used technique for detection of osteoporosis is the measurement of bone mineral density (BMD) using dual energy X-ray absorptiometry (DXA). But DXA scans are expensive and not widely available in low-income economies. In this paper, we propose a low cost pre-screening tool for the detection of low bone mass, using cortical radiogrammetry of third metacarpal bone and trabecular texture analysis of distal radius from hand and wrist radiographs. An automatic segmentation algorithm to automatically locate and segment the third metacarpal bone and distal radius region of interest (ROI) is proposed. Cortical measurements such as combined cortical thickness (CCT), cortical area (CA), percent cortical area (PCA) and Barnett Nordin index (BNI) were taken from the shaft of third metacarpal bone. Texture analysis of trabecular network at the distal radius was performed using features obtained from histogram, gray level Co-occurrence matrix (GLCM) and morphological gradient method (MGM). The significant cortical and texture features were selected using independent sample t-test and used to train classifiers to classify healthy subjects and people with low bone mass. The proposed pre-screening tool was validated on two ethnic groups, Indian sample population and Swiss sample population. Data of 134 subjects from Indian sample population and 65 subjects from Swiss sample population were analysed. The proposed automatic segmentation approach shows a detection accuracy of 86% in detecting the third metacarpal bone shaft and 90% in accurately locating the distal radius ROI. Comparison of the automatic radiogrammetry to the ground truth provided by experts show a mean absolute error of 0.04 mm for cortical width of healthy group, 0.12 mm for cortical width of low bone mass group, 0.22 mm for medullary width of healthy group, and 0.26 mm for medullary width of low bone mass group. Independent sample t-test was used to select the most discriminant features, to be used as input for training the classifiers. Pearson correlation analysis of the extracted features with DXA-BMD of lumbar spine (DXA-LS) shows significantly high correlation values. Classifiers were trained with the most significant features in the Indian and Swiss sample data. Weighted KNN classifier shows the best test accuracy of 78% for Indian sample data and 100% for Swiss sample data. Hence, combined automatic radiogrammetry and texture analysis is shown to be an effective low cost pre-screening tool for early diagnosis of osteoporosis. Copyright © 2018 Elsevier Ltd. All rights reserved.

Hybrid approach for detection of dental caries based on the methods FCM and level sets

NASA Astrophysics Data System (ADS)

Chaabene, Marwa; Ben Ali, Ramzi; Ejbali, Ridha; Zaied, Mourad

2017-03-01

This paper presents a new technique for detection of dental caries that is a bacterial disease that destroys the tooth structure. In our approach, we have achieved a new segmentation method that combines the advantages of fuzzy C mean algorithm and level set method. The results obtained by the FCM algorithm will be used by Level sets algorithm to reduce the influence of the noise effect on the working of each of these algorithms, to facilitate level sets manipulation and to lead to more robust segmentation. The sensitivity and specificity confirm the effectiveness of proposed method for caries detection.

Automated diagnosis of Alzheimer's disease with multi-atlas based whole brain segmentations

NASA Astrophysics Data System (ADS)

Luo, Yuan; Tang, Xiaoying

2017-03-01

Voxel-based analysis is widely used in quantitative analysis of structural brain magnetic resonance imaging (MRI) and automated disease detection, such as Alzheimer's disease (AD). However, noise at the voxel level may cause low sensitivity to AD-induced structural abnormalities. This can be addressed with the use of a whole brain structural segmentation approach which greatly reduces the dimension of features (the number of voxels). In this paper, we propose an automatic AD diagnosis system that combines such whole brain segmen- tations with advanced machine learning methods. We used a multi-atlas segmentation technique to parcellate T1-weighted images into 54 distinct brain regions and extract their structural volumes to serve as the features for principal-component-analysis-based dimension reduction and support-vector-machine-based classification. The relationship between the number of retained principal components (PCs) and the diagnosis accuracy was systematically evaluated, in a leave-one-out fashion, based on 28 AD subjects and 23 age-matched healthy subjects. Our approach yielded pretty good classification results with 96.08% overall accuracy being achieved using the three foremost PCs. In addition, our approach yielded 96.43% specificity, 100% sensitivity, and 0.9891 area under the receiver operating characteristic curve.

Hybrid Pixel-Based Method for Cardiac Ultrasound Fusion Based on Integration of PCA and DWT.

PubMed

Mazaheri, Samaneh; Sulaiman, Puteri Suhaiza; Wirza, Rahmita; Dimon, Mohd Zamrin; Khalid, Fatimah; Moosavi Tayebi, Rohollah

2015-01-01

Medical image fusion is the procedure of combining several images from one or multiple imaging modalities. In spite of numerous attempts in direction of automation ventricle segmentation and tracking in echocardiography, due to low quality images with missing anatomical details or speckle noises and restricted field of view, this problem is a challenging task. This paper presents a fusion method which particularly intends to increase the segment-ability of echocardiography features such as endocardial and improving the image contrast. In addition, it tries to expand the field of view, decreasing impact of noise and artifacts and enhancing the signal to noise ratio of the echo images. The proposed algorithm weights the image information regarding an integration feature between all the overlapping images, by using a combination of principal component analysis and discrete wavelet transform. For evaluation, a comparison has been done between results of some well-known techniques and the proposed method. Also, different metrics are implemented to evaluate the performance of proposed algorithm. It has been concluded that the presented pixel-based method based on the integration of PCA and DWT has the best result for the segment-ability of cardiac ultrasound images and better performance in all metrics.

Reproducibility of F18-FDG PET radiomic features for different cervical tumor segmentation methods, gray-level discretization, and reconstruction algorithms.

PubMed

Altazi, Baderaldeen A; Zhang, Geoffrey G; Fernandez, Daniel C; Montejo, Michael E; Hunt, Dylan; Werner, Joan; Biagioli, Matthew C; Moros, Eduardo G

2017-11-01

Site-specific investigations of the role of radiomics in cancer diagnosis and therapy are emerging. We evaluated the reproducibility of radiomic features extracted from 18 Flourine-fluorodeoxyglucose ( 18 F-FDG) PET images for three parameters: manual versus computer-aided segmentation methods, gray-level discretization, and PET image reconstruction algorithms. Our cohort consisted of pretreatment PET/CT scans from 88 cervical cancer patients. Two board-certified radiation oncologists manually segmented the metabolic tumor volume (MTV 1 and MTV 2 ) for each patient. For comparison, we used a graphical-based method to generate semiautomated segmented volumes (GBSV). To address any perturbations in radiomic feature values, we down-sampled the tumor volumes into three gray-levels: 32, 64, and 128 from the original gray-level of 256. Finally, we analyzed the effect on radiomic features on PET images of eight patients due to four PET 3D-reconstruction algorithms: maximum likelihood-ordered subset expectation maximization (OSEM) iterative reconstruction (IR) method, fourier rebinning-ML-OSEM (FOREIR), FORE-filtered back projection (FOREFBP), and 3D-Reprojection (3DRP) analytical method. We extracted 79 features from all segmentation method, gray-levels of down-sampled volumes, and PET reconstruction algorithms. The features were extracted using gray-level co-occurrence matrices (GLCM), gray-level size zone matrices (GLSZM), gray-level run-length matrices (GLRLM), neighborhood gray-tone difference matrices (NGTDM), shape-based features (SF), and intensity histogram features (IHF). We computed the Dice coefficient between each MTV and GBSV to measure segmentation accuracy. Coefficient values close to one indicate high agreement, and values close to zero indicate low agreement. We evaluated the effect on radiomic features by calculating the mean percentage differences (d¯) between feature values measured from each pair of parameter elements (i.e. segmentation methods: MTV 1 -MTV 2 , MTV 1 -GBSV, MTV 2 -GBSV; gray-levels: 64-32, 64-128, and 64-256; reconstruction algorithms: OSEM-FORE-OSEM, OSEM-FOREFBP, and OSEM-3DRP). We used |d¯| as a measure of radiomic feature reproducibility level, where any feature scored |d¯| ±SD ≤ |25|% ± 35% was considered reproducible. We used Bland-Altman analysis to evaluate the mean, standard deviation (SD), and upper/lower reproducibility limits (U/LRL) for radiomic features in response to variation in each testing parameter. Furthermore, we proposed U/LRL as a method to classify the level of reproducibility: High- ±1% ≤ U/LRL ≤ ±30%; Intermediate- ±30% < U/LRL ≤ ±45%; Low- ±45 < U/LRL ≤ ±50%. We considered any feature below the low level as nonreproducible (NR). Finally, we calculated the interclass correlation coefficient (ICC) to evaluate the reliability of radiomic feature measurements for each parameter. The segmented volumes of 65 patients (81.3%) scored Dice coefficient >0.75 for all three volumes. The result outcomes revealed a tendency of higher radiomic feature reproducibility among segmentation pair MTV 1 -GBSV than MTV 2 -GBSV, gray-level pairs of 64-32 and 64-128 than 64-256, and reconstruction algorithm pairs of OSEM-FOREIR and OSEM-FOREFBP than OSEM-3DRP. Although the choice of cervical tumor segmentation method, gray-level value, and reconstruction algorithm may affect radiomic features, some features were characterized by high reproducibility through all testing parameters. The number of radiomic features that showed insensitivity to variations in segmentation methods, gray-level discretization, and reconstruction algorithms was 10 (13%), 4 (5%), and 1 (1%), respectively. These results suggest that a careful analysis of the effects of these parameters is essential prior to any radiomics clinical application. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Health behaviour modelling for prenatal diagnosis in Australia: a geodemographic framework for health service utilisation and policy development

PubMed Central

Muggli, Evelyne E; McCloskey, David; Halliday, Jane L

2006-01-01

Background Despite the wide availability of prenatal screening and diagnosis, a number of studies have reported no decrease in the rate of babies born with Down syndrome. The objective of this study was to investigate the geodemographic characteristics of women who have prenatal diagnosis in Victoria, Australia, by applying a novel consumer behaviour modelling technique in the analysis of health data. Methods A descriptive analysis of data on all prenatal diagnostic tests, births (1998 and 2002) and births of babies with Down syndrome (1998 to 2002) was undertaken using a Geographic Information System and socioeconomic lifestyle segmentation classifications. Results Most metropolitan women in Victoria have average or above State average levels of uptake of prenatal diagnosis. Inner city women residing in high socioeconomic lifestyle segments who have high rates of prenatal diagnosis spend 20% more on specialist physician's fees when compared to those whose rates are average. Rates of prenatal diagnosis are generally low amongst women in rural Victoria, with the lowest rates observed in farming districts. Reasons for this are likely to be a combination of lack of access to services (remoteness) and individual opportunity (lack of transportation, low levels of support and income). However, there are additional reasons for low uptake rates in farming areas that could not be explained by the behaviour modelling. These may relate to women's attitudes and choices. Conclusion A lack of statewide geodemographic consistency in uptake of prenatal diagnosis implies that there is a need to target health professionals and pregnant women in specific areas to ensure there is increased equity of access to services and that all pregnant women can make informed choices that are best for them. Equally as important is appropriate health service provision for families of children with Down syndrome. Our findings show that these potential interventions are particularly relevant in rural areas. Classifying data to lifestyle segments allowed for practical comparisons of the geodemographic characteristics of women having prenatal diagnosis in Australia at a population level. This methodology may in future be a feasible and cost-effective tool for service planners and policy developers. PMID:16945156

Effect of the chemical structure of the polymer matrix on the properties of foam polyurethanes at low temperatures

NASA Astrophysics Data System (ADS)

Yakushin, V. A.; Stirna, U. K.; Zhmud', N. P.

1999-07-01

The dependence of physical and mechanical properties of oligoether-based foam polyurethanes on the molecular mass (Mc) of polymer chains between the nodes of the polymer network and on the content of rigid segments in the polymer is investigated at 293 and 98K. The values of Mc at which the foam plastics have the best mechanical properties at low temperatures are determined. The content of rigid segments in the polymer at which foam polyurethanes have the best combination of the linear thermal expansion coefficient and mechanical properties in tension at a temperature of 98K is found.

Practical Aspects of Access Network Indoor Extensions Using Multimode Glass and Plastic Optical Fibers

NASA Astrophysics Data System (ADS)

Keiser, Gerd; Liu, Hao-Yu; Lu, Shao-Hsi; Devi Pukhrambam, Puspa

2012-07-01

Low-cost multimode glass and plastic optical fibers are attractive for high-capacity indoor telecom networks. Many existing buildings already have glass multimode fibers installed for local area network applications. Future indoor applications will use combinations of glass multimode fibers with plastic optical fibers that have low losses in the 850-nm-1,310-nm range. This article examines real-world link losses when randomly interconnecting glass and plastic fiber segments having factory-installed connectors. Potential interconnection issues include large variations in connector losses among randomly selected fiber segments, asymmetric link losses in bidirectional links, and variations in bandwidths among different types of fibers.

A comparative study of automatic image segmentation algorithms for target tracking in MR-IGRT.

PubMed

Feng, Yuan; Kawrakow, Iwan; Olsen, Jeff; Parikh, Parag J; Noel, Camille; Wooten, Omar; Du, Dongsu; Mutic, Sasa; Hu, Yanle

2016-03-01

On-board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real-time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image-guided radiotherapy (MR-IGRT) system. Manual contours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k-means (FKM), k-harmonic means (KHM), and reaction-diffusion level set evolution (RD-LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR-TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR-TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD-LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP-TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high-contrast images (i.e., kidney), the thresholding method provided the best speed (<1 ms) with a satisfying accuracy (Dice=0.95). When the image contrast was low, the VR-TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and a combination of different methods for optimal segmentation with the on-board MR-IGRT system. PACS number(s): 87.57.nm, 87.57.N-, 87.61.Tg. © 2016 The Authors.

A comparative study of automatic image segmentation algorithms for target tracking in MR‐IGRT

PubMed Central

Feng, Yuan; Kawrakow, Iwan; Olsen, Jeff; Parikh, Parag J.; Noel, Camille; Wooten, Omar; Du, Dongsu; Mutic, Sasa

2016-01-01

On‐board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real‐time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image‐guided radiotherapy (MR‐IGRT) system. Manual contours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k‐means (FKM), k‐harmonic means (KHM), and reaction‐diffusion level set evolution (RD‐LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR‐TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR‐TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD‐LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP‐TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high‐contrast images (i.e., kidney), the thresholding method provided the best speed (<1 ms) with a satisfying accuracy (Dice=0.95). When the image contrast was low, the VR‐TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and a combination of different methods for optimal segmentation with the on‐board MR‐IGRT system. PACS number(s): 87.57.nm, 87.57.N‐, 87.61.Tg

Zoster-associated segmental paresis in a patient with cervical spinal stenosis.

PubMed

Kang, Sung-Hee; Song, Ho-Kyung; Jang, Yeon

2013-06-01

Segmental zoster paresis is a rare complication of herpes zoster, characterized by focal motor weakness that does not always present simultaneously with skin lesions. Zoster paresis can be easily confused with other neuromuscular or spinal diseases. This case report describes the case of a 72-year-old woman with herpes zoster and cervical spinal stenosis at the same spinal level, where it was difficult to distinguish segmental zoster paresis from cervical radiculopathy combined with motor neuropathy. Although segmental zoster paresis in the upper extremity is rare, it should be included in the differential diagnosis of segmental pain and weakness in the extremities, especially in older or immunocompromised patients. Correct diagnosis is required, to avoid unnecessary surgery and allow timely antiviral treatment.

Interactive lesion segmentation with shape priors from offline and online learning.

PubMed

Shepherd, Tony; Prince, Simon J D; Alexander, Daniel C

2012-09-01

In medical image segmentation, tumors and other lesions demand the highest levels of accuracy but still call for the highest levels of manual delineation. One factor holding back automatic segmentation is the exemption of pathological regions from shape modelling techniques that rely on high-level shape information not offered by lesions. This paper introduces two new statistical shape models (SSMs) that combine radial shape parameterization with machine learning techniques from the field of nonlinear time series analysis. We then develop two dynamic contour models (DCMs) using the new SSMs as shape priors for tumor and lesion segmentation. From training data, the SSMs learn the lower level shape information of boundary fluctuations, which we prove to be nevertheless highly discriminant. One of the new DCMs also uses online learning to refine the shape prior for the lesion of interest based on user interactions. Classification experiments reveal superior sensitivity and specificity of the new shape priors over those previously used to constrain DCMs. User trials with the new interactive algorithms show that the shape priors are directly responsible for improvements in accuracy and reductions in user demand.

Image segmentation via foreground and background semantic descriptors

NASA Astrophysics Data System (ADS)

Yuan, Ding; Qiang, Jingjing; Yin, Jihao

2017-09-01

In the field of image processing, it has been a challenging task to obtain a complete foreground that is not uniform in color or texture. Unlike other methods, which segment the image by only using low-level features, we present a segmentation framework, in which high-level visual features, such as semantic information, are used. First, the initial semantic labels were obtained by using the nonparametric method. Then, a subset of the training images, with a similar foreground to the input image, was selected. Consequently, the semantic labels could be further refined according to the subset. Finally, the input image was segmented by integrating the object affinity and refined semantic labels. State-of-the-art performance was achieved in experiments with the challenging MSRC 21 dataset.

a Fast Segmentation Algorithm for C-V Model Based on Exponential Image Sequence Generation

NASA Astrophysics Data System (ADS)

Hu, J.; Lu, L.; Xu, J.; Zhang, J.

2017-09-01

For the island coastline segmentation, a fast segmentation algorithm for C-V model method based on exponential image sequence generation is proposed in this paper. The exponential multi-scale C-V model with level set inheritance and boundary inheritance is developed. The main research contributions are as follows: 1) the problems of the "holes" and "gaps" are solved when extraction coastline through the small scale shrinkage, low-pass filtering and area sorting of region. 2) the initial value of SDF (Signal Distance Function) and the level set are given by Otsu segmentation based on the difference of reflection SAR on land and sea, which are finely close to the coastline. 3) the computational complexity of continuous transition are successfully reduced between the different scales by the SDF and of level set inheritance. Experiment results show that the method accelerates the acquisition of initial level set formation, shortens the time of the extraction of coastline, at the same time, removes the non-coastline body part and improves the identification precision of the main body coastline, which automates the process of coastline segmentation.

Beam hardening artifact reduction using dual energy computed tomography: implications for myocardial perfusion studies

PubMed Central

Carrascosa, Patricia; Cipriano, Silvina; De Zan, Macarena; Deviggiano, Alejandro; Capunay, Carlos; Cury, Ricardo C.

2015-01-01

Background Myocardial computed tomography perfusion (CTP) using conventional single energy (SE) imaging is influenced by the presence of beam hardening artifacts (BHA), occasionally resembling perfusion defects and commonly observed at the left ventricular posterobasal wall (PB). We therefore sought to explore the ability of dual energy (DE) CTP to attenuate the presence of BHA. Methods Consecutive patients without history of coronary artery disease who were referred for computed tomography coronary angiography (CTCA) due to atypical chest pain and a normal stress-rest SPECT and had absence or mild coronary atherosclerosis constituted the study population. The study group was acquired using DE and the control group using SE imaging. Results Demographical characteristics were similar between groups, as well as the heart rate and the effective radiation dose. Myocardial signal density (SD) levels were evaluated in 280 basal segments among the DE group (140 PB segments for each energy level from 40 to 100 keV; and 140 reference segments), and in 40 basal segments (at the same locations) among the SE group. Among the DE group, myocardial SD levels and myocardial SD ratio evaluated at the reference segment were higher at low energy levels, with significantly lower SD levels at increasing energy levels. Myocardial signal-to-noise ratio was not significantly influenced by the energy level applied, although 70 keV was identified as the energy level with the best overall signal-to-noise ratio. Significant differences were identified between the PB segment and the reference segment among the lower energy levels, whereas at ≥70 keV myocardial SD levels were similar. Compared to DE reconstructions at the best energy level (70 keV), SE acquisitions showed no significant differences overall regarding myocardial SD levels among the reference segments. Conclusions BHA that influence the assessment of myocardial perfusion can be attenuated using DE at 70 keV or higher. PMID:25774354

Impact of low level of high-density lipoprotein-cholesterol sampled in overnight fasting state on the clinical outcomes in patients with acute myocardial infarction (difference between ST-segment and non-ST-segment-elevation myocardial infarction).

PubMed

Ji, Mi Seon; Jeong, Myung Ho; Ahn, Young Keun; Kim, Young Jo; Chae, Shung Chull; Hong, Taek Jong; Seong, In Whan; Chae, Jei Keon; Kim, Chong Jin; Cho, Myeong Chan; Rha, Seung-Woon; Bae, Jang Ho; Seung, Ki Bae; Park, Seung Jung

2015-01-01

Despite good treatment, there are residual risks in acute myocardial infarction (AMI) patients, and low level of high-density lipoprotein-cholesterol (HDL) has drawn attention as a possible cause. However, the impact of low HDL on ST-segment-elevation myocardial infarction (STEMI) compared with non-ST-segment-elevation myocardial infarction (NSTEMI) is not clear. Our aim was to evaluate the impact of low HDL on clinical outcomes in patients with STEMI or NSTEMI. We included 9270 AMI patients undergoing successful percutaneous coronary intervention. They were grouped into STEMI and NSTEMI, and subdivided into two groups according to HDL level sampled in overnight fasting state. Primary end point was in-hospital death. Secondary end point was a composite of major adverse cardiac events (MACE) in hospital survivors during one-year follow-up. In the STEMI population, low HDL group showed significantly higher in-hospital death rate [4.6% vs. 1.4%, hazard ratio (HR): 2.380, 95% confidence interval (CI): 1.143-4.956, p=0.020] than normal HDL group. In NSTEMI population, there was no significant difference between two groups (1.8% vs. 0.9%, HR: 1.231, 95% CI: 0.649-2.335, p=0.525), but in subgroup analysis, very low HDL subgroup showed higher in-hospital mortality rate compared with normal HDL group (4.0% vs. 0.9%, respectively, p=0.009). In 12-month MACE rates, there was no significant difference between two groups in both populations. Low HDL was associated with significantly higher risk of in-hospital mortality in STEMI patients, but not in NSTEMI patients. Thus, more aggressive treatment should be considered in STEMI patients with low HDL. Copyright © 2014. Published by Elsevier Ltd.

Starch digestibility and glycemic index of Paranthas supplemented with Citrus maxima (Burm.) Merr. fruit segments.

PubMed

Reshmi, S K; Sudha, M L; Shashirekha, M N

2017-12-01

The present investigation was undertaken to develop paranthas suiting diabetic population with added health benefits. Paranthas were prepared using fresh and dry segments of pomelo. The increase in the concentration of segments decreased the texture value from 1080 to 1022 g force (fresh segments) and 1005 to 870 g force (dry segments). Naringin along with other bioactive compounds were retained to a greater extent in Paranthas containing dry pomelo fruit segments. Paranthas prepared using 20% (fresh) and 5% (dry) were sensorily acceptable. The pomelo incorporated paranthas had higher levels of resistance starch fractions (12.94%) with low predicted glycemic index (49.89%) compared to control Paranthas at 5.54 and 58.64% respectively. The fortified paranthas with an considerable content of bioactive compounds and low glycemic index indicate the possibility of using it as a dietary supplement. Thus utilization of pomelo fortification helps in improving the nutritional and functional property of paranthas suiting diabetes as well as general population.

Low back pain in young female gymnasts and the effect of specific segmental muscle control exercises of the lumbar spine: a prospective controlled intervention study.

PubMed

Harringe, M L; Nordgren, J S; Arvidsson, I; Werner, S

2007-10-01

Prospective controlled intervention study. To evaluate a specific segmental muscle training program of the lumbar spine in order to prevent and reduce low back pain in young female teamgym gymnasts. Teamgym is a team sport comprising three events: trampette, tumbling and floor programme. In a recent study, it was found that teamgym gymnasts practice and compete despite suffering from back pain. Specific muscle control exercises of the lumbar spine have shown good results in reducing pain intensity and functional disability levels in patients with low back pain. To our knowledge, this type of training has not been studied in an adolescent athletic population before. Fifty-one gymnasts, with and without LBP, 11-16 years old, from three top-level gymnastics team participated in the study comprising 12 weeks. Every day the gymnasts answered a questionnaire regarding low back pain. After baseline (4 weeks) the intervention group performed a specific segmental muscle training program. Twenty-four gymnasts (47%) reported low back pain during baseline. Nine gymnasts failed to answer the questionnaire every day and the following results are based on 42 gymnasts (intervention group, n = 30, and control group, n=12). Gymnasts in the intervention group reported significantly less number of days with low back pain at completion compared to baseline (P=0.02). Gymnasts in the control group showed no difference in terms of days with low back pain or intensity of low back pain between baseline and completion. Eight gymnasts (out of 15) with LBP in the intervention group became pain free. Specific segmental muscle control exercises of the lumbar spine may be of value in preventing and reducing low back pain in young teamgym gymnasts.

A deep learning approach for the analysis of masses in mammograms with minimal user intervention.

PubMed

Dhungel, Neeraj; Carneiro, Gustavo; Bradley, Andrew P

2017-04-01

We present an integrated methodology for detecting, segmenting and classifying breast masses from mammograms with minimal user intervention. This is a long standing problem due to low signal-to-noise ratio in the visualisation of breast masses, combined with their large variability in terms of shape, size, appearance and location. We break the problem down into three stages: mass detection, mass segmentation, and mass classification. For the detection, we propose a cascade of deep learning methods to select hypotheses that are refined based on Bayesian optimisation. For the segmentation, we propose the use of deep structured output learning that is subsequently refined by a level set method. Finally, for the classification, we propose the use of a deep learning classifier, which is pre-trained with a regression to hand-crafted feature values and fine-tuned based on the annotations of the breast mass classification dataset. We test our proposed system on the publicly available INbreast dataset and compare the results with the current state-of-the-art methodologies. This evaluation shows that our system detects 90% of masses at 1 false positive per image, has a segmentation accuracy of around 0.85 (Dice index) on the correctly detected masses, and overall classifies masses as malignant or benign with sensitivity (Se) of 0.98 and specificity (Sp) of 0.7. Copyright © 2017 Elsevier B.V. All rights reserved.

Automatical and accurate segmentation of cerebral tissues in fMRI dataset with combination of image processing and deep learning

NASA Astrophysics Data System (ADS)

Kong, Zhenglun; Luo, Junyi; Xu, Shengpu; Li, Ting

2018-02-01

Image segmentation plays an important role in medical science. One application is multimodality imaging, especially the fusion of structural imaging with functional imaging, which includes CT, MRI and new types of imaging technology such as optical imaging to obtain functional images. The fusion process require precisely extracted structural information, in order to register the image to it. Here we used image enhancement, morphometry methods to extract the accurate contours of different tissues such as skull, cerebrospinal fluid (CSF), grey matter (GM) and white matter (WM) on 5 fMRI head image datasets. Then we utilized convolutional neural network to realize automatic segmentation of images in deep learning way. Such approach greatly reduced the processing time compared to manual and semi-automatic segmentation and is of great importance in improving speed and accuracy as more and more samples being learned. The contours of the borders of different tissues on all images were accurately extracted and 3D visualized. This can be used in low-level light therapy and optical simulation software such as MCVM. We obtained a precise three-dimensional distribution of brain, which offered doctors and researchers quantitative volume data and detailed morphological characterization for personal precise medicine of Cerebral atrophy/expansion. We hope this technique can bring convenience to visualization medical and personalized medicine.

Optimizing Scoring and Sampling Methods for Assessing Built Neighborhood Environment Quality in Residential Areas.

PubMed

Adu-Brimpong, Joel; Coffey, Nathan; Ayers, Colby; Berrigan, David; Yingling, Leah R; Thomas, Samantha; Mitchell, Valerie; Ahuja, Chaarushi; Rivers, Joshua; Hartz, Jacob; Powell-Wiley, Tiffany M

2017-03-08

Optimization of existing measurement tools is necessary to explore links between aspects of the neighborhood built environment and health behaviors or outcomes. We evaluate a scoring method for virtual neighborhood audits utilizing the Active Neighborhood Checklist (the Checklist), a neighborhood audit measure, and assess street segment representativeness in low-income neighborhoods. Eighty-two home neighborhoods of Washington, D.C. Cardiovascular Health/Needs Assessment (NCT01927783) participants were audited using Google Street View imagery and the Checklist (five sections with 89 total questions). Twelve street segments per home address were assessed for (1) Land-Use Type; (2) Public Transportation Availability; (3) Street Characteristics; (4) Environment Quality and (5) Sidewalks/Walking/Biking features. Checklist items were scored 0-2 points/question. A combinations algorithm was developed to assess street segments' representativeness. Spearman correlations were calculated between built environment quality scores and Walk Score ® , a validated neighborhood walkability measure. Street segment quality scores ranged 10-47 (Mean = 29.4 ± 6.9) and overall neighborhood quality scores, 172-475 (Mean = 352.3 ± 63.6). Walk scores ® ranged 0-91 (Mean = 46.7 ± 26.3). Street segment combinations' correlation coefficients ranged 0.75-1.0. Significant positive correlations were found between overall neighborhood quality scores, four of the five Checklist subsection scores, and Walk Scores ® ( r = 0.62, p < 0.001). This scoring method adequately captures neighborhood features in low-income, residential areas and may aid in delineating impact of specific built environment features on health behaviors and outcomes.

A new approach to modeling the influence of image features on fixation selection in scenes

PubMed Central

Nuthmann, Antje; Einhäuser, Wolfgang

2015-01-01

Which image characteristics predict where people fixate when memorizing natural images? To answer this question, we introduce a new analysis approach that combines a novel scene-patch analysis with generalized linear mixed models (GLMMs). Our method allows for (1) directly describing the relationship between continuous feature value and fixation probability, and (2) assessing each feature's unique contribution to fixation selection. To demonstrate this method, we estimated the relative contribution of various image features to fixation selection: luminance and luminance contrast (low-level features); edge density (a mid-level feature); visual clutter and image segmentation to approximate local object density in the scene (higher-level features). An additional predictor captured the central bias of fixation. The GLMM results revealed that edge density, clutter, and the number of homogenous segments in a patch can independently predict whether image patches are fixated or not. Importantly, neither luminance nor contrast had an independent effect above and beyond what could be accounted for by the other predictors. Since the parcellation of the scene and the selection of features can be tailored to the specific research question, our approach allows for assessing the interplay of various factors relevant for fixation selection in scenes in a powerful and flexible manner. PMID:25752239

PSOFuzzySVM-TMH: identification of transmembrane helix segments using ensemble feature space by incorporated fuzzy support vector machine.

PubMed

Hayat, Maqsood; Tahir, Muhammad

2015-08-01

Membrane protein is a central component of the cell that manages intra and extracellular processes. Membrane proteins execute a diversity of functions that are vital for the survival of organisms. The topology of transmembrane proteins describes the number of transmembrane (TM) helix segments and its orientation. However, owing to the lack of its recognized structures, the identification of TM helix and its topology through experimental methods is laborious with low throughput. In order to identify TM helix segments reliably, accurately, and effectively from topogenic sequences, we propose the PSOFuzzySVM-TMH model. In this model, evolutionary based information position specific scoring matrix and discrete based information 6-letter exchange group are used to formulate transmembrane protein sequences. The noisy and extraneous attributes are eradicated using an optimization selection technique, particle swarm optimization, from both feature spaces. Finally, the selected feature spaces are combined in order to form ensemble feature space. Fuzzy-support vector Machine is utilized as a classification algorithm. Two benchmark datasets, including low and high resolution datasets, are used. At various levels, the performance of the PSOFuzzySVM-TMH model is assessed through 10-fold cross validation test. The empirical results reveal that the proposed framework PSOFuzzySVM-TMH outperforms in terms of classification performance in the examined datasets. It is ascertained that the proposed model might be a useful and high throughput tool for academia and research community for further structure and functional studies on transmembrane proteins.

Segmentation of neuronal structures using SARSA (λ)-based boundary amendment with reinforced gradient-descent curve shape fitting.

PubMed

Zhu, Fei; Liu, Quan; Fu, Yuchen; Shen, Bairong

2014-01-01

The segmentation of structures in electron microscopy (EM) images is very important for neurobiological research. The low resolution neuronal EM images contain noise and generally few features are available for segmentation, therefore application of the conventional approaches to identify the neuron structure from EM images is not successful. We therefore present a multi-scale fused structure boundary detection algorithm in this study. In the algorithm, we generate an EM image Gaussian pyramid first, then at each level of the pyramid, we utilize Laplacian of Gaussian function (LoG) to attain structure boundary, we finally assemble the detected boundaries by using fusion algorithm to attain a combined neuron structure image. Since the obtained neuron structures usually have gaps, we put forward a reinforcement learning-based boundary amendment method to connect the gaps in the detected boundaries. We use a SARSA (λ)-based curve traveling and amendment approach derived from reinforcement learning to repair the incomplete curves. Using this algorithm, a moving point starts from one end of the incomplete curve and walks through the image where the decisions are supervised by the approximated curve model, with the aim of minimizing the connection cost until the gap is closed. Our approach provided stable and efficient structure segmentation. The test results using 30 EM images from ISBI 2012 indicated that both of our approaches, i.e., with or without boundary amendment, performed better than six conventional boundary detection approaches. In particular, after amendment, the Rand error and warping error, which are the most important performance measurements during structure segmentation, were reduced to very low values. The comparison with the benchmark method of ISBI 2012 and the recent developed methods also indicates that our method performs better for the accurate identification of substructures in EM images and therefore useful for the identification of imaging features related to brain diseases.

Segmentation of Neuronal Structures Using SARSA (λ)-Based Boundary Amendment with Reinforced Gradient-Descent Curve Shape Fitting

PubMed Central

Zhu, Fei; Liu, Quan; Fu, Yuchen; Shen, Bairong

2014-01-01

The segmentation of structures in electron microscopy (EM) images is very important for neurobiological research. The low resolution neuronal EM images contain noise and generally few features are available for segmentation, therefore application of the conventional approaches to identify the neuron structure from EM images is not successful. We therefore present a multi-scale fused structure boundary detection algorithm in this study. In the algorithm, we generate an EM image Gaussian pyramid first, then at each level of the pyramid, we utilize Laplacian of Gaussian function (LoG) to attain structure boundary, we finally assemble the detected boundaries by using fusion algorithm to attain a combined neuron structure image. Since the obtained neuron structures usually have gaps, we put forward a reinforcement learning-based boundary amendment method to connect the gaps in the detected boundaries. We use a SARSA (λ)-based curve traveling and amendment approach derived from reinforcement learning to repair the incomplete curves. Using this algorithm, a moving point starts from one end of the incomplete curve and walks through the image where the decisions are supervised by the approximated curve model, with the aim of minimizing the connection cost until the gap is closed. Our approach provided stable and efficient structure segmentation. The test results using 30 EM images from ISBI 2012 indicated that both of our approaches, i.e., with or without boundary amendment, performed better than six conventional boundary detection approaches. In particular, after amendment, the Rand error and warping error, which are the most important performance measurements during structure segmentation, were reduced to very low values. The comparison with the benchmark method of ISBI 2012 and the recent developed methods also indicates that our method performs better for the accurate identification of substructures in EM images and therefore useful for the identification of imaging features related to brain diseases. PMID:24625699

Pancreas and cyst segmentation

NASA Astrophysics Data System (ADS)

Dmitriev, Konstantin; Gutenko, Ievgeniia; Nadeem, Saad; Kaufman, Arie

2016-03-01

Accurate segmentation of abdominal organs from medical images is an essential part of surgical planning and computer-aided disease diagnosis. Many existing algorithms are specialized for the segmentation of healthy organs. Cystic pancreas segmentation is especially challenging due to its low contrast boundaries, variability in shape, location and the stage of the pancreatic cancer. We present a semi-automatic segmentation algorithm for pancreata with cysts. In contrast to existing automatic segmentation approaches for healthy pancreas segmentation which are amenable to atlas/statistical shape approaches, a pancreas with cysts can have even higher variability with respect to the shape of the pancreas due to the size and shape of the cyst(s). Hence, fine results are better attained with semi-automatic steerable approaches. We use a novel combination of random walker and region growing approaches to delineate the boundaries of the pancreas and cysts with respective best Dice coefficients of 85.1% and 86.7%, and respective best volumetric overlap errors of 26.0% and 23.5%. Results show that the proposed algorithm for pancreas and pancreatic cyst segmentation is accurate and stable.

Safe extensive tumescent liposuction with segmental infiltration of lower concentration lidocaine under monitored anesthesia care.

PubMed

Wang, Gang; Cao, Wei-Gang; Li, Sheng-Li; Liu, Li-Na; Jiang, Zhao-Hua

2015-01-01

Tumescent anesthesia makes it feasible to perform liposuction in an office setting. There are often patients who desire extensive liposuction on approximately 30% of total body surface area, which means the lidocaine total dose might be over the dosing recommendation. So the segmental infiltration is applied, although the concentration of lidocaine in tumescent fluid is gradually reduced to 0.0252%. Moreover, supplemental intravenous (IV) sedation using monitored anesthesia care is usually applied concurrently to help alleviate discomfort and pain of the patients during tumescent anesthetic infusion and fat extraction which in turn increases the risks of potential lidocaine toxicity due to possible drug interactions. This study was to demonstrate the safety of segmental infiltration of tumescent fluid with lower lidocaine concentration combined with IV sedation in extensive liposuction and determine whether the risk of lidocaine toxicity is increased in this protocol. Ten female patients who requested the extensive liposuction participated in the study. The targeted areas were divided into 2 segments and treated in turn in 1 session. Lidocaine (1600 mg) was infiltrated into the first segment, and approximately 928 mg lidocaine was subsequently infiltrated after accomplishment of the first segment operation. Serum levels of lidocaine were taken every 4 hours during the first 24 hours after the second infiltration. The average time of the procedure is 222 (33) minutes. The dose and total amount of lidocaine injected are 40.7 (5.8) mg/kg and 2528.2 (155.2) mg, respectively. The total volume of the infusates and aspirates are 9918.1 (494) and 6325 (1461.6) mL, respectively, the ratio of total infusates to total aspirates is 1.66 (0.45). The total aspirated fat and fluids are 3280 (1051.8) and 3045 (824.1) mL, respectively. The peak lidocaine levels [2.18 (0.63) μg/mL] occurred after 12 to 20 hours [16.4 (2.27) hours]. No significant correlation between dose per kilogram body weight or total dose of lidocaine infiltrated and its peak levels or time existed. The extensive liposuction covering the 30% total body surface areas was well tolerated by the patients under tumescent anesthesia in combination with the supplemental IV sedation. Our previous study on the fluid management has demonstrated the risk of hypovolemia or fluid overload is very low with this technique, although the patients who received only maintenance fluid (500 mL) in the operating room and could discharge and resume oral intake after 6 hours of recovery room stay. The adequate anesthesia support is available in our office-based setting with adequate recovery facilities in place. It has a high margin of safety, without increasing of lidocaine toxicity or adverse cardiopulmonary sequelae while using a segmental tumescent infiltration with lower concentration of lidocaine.

WE-E-213CD-08: A Novel Level Set Active Contour Algorithm Using the Jensen-Renyi Divergence for Tumor Segmentation in PET.

PubMed

Markel, D; Naqa, I El

2012-06-01

Positron emission tomography (PET) presents a valuable resource for delineating the biological tumor volume (BTV) for image-guided radiotherapy. However, accurate and consistent image segmentation is a significant challenge within the context of PET, owing to its low spatial resolution and high levels of noise. Active contour methods based on the level set methods can be sensitive to noise and susceptible to failing in low contrast regions. Therefore, this work evaluates a novel active contour algorithm applied to the task of PET tumor segmentation. A novel active contour segmentation algorithm based on maximizing the Jensen-Renyi Divergence between regions of interest was applied to the task of segmenting lesions in 7 patients with T3-T4 pharyngolaryngeal squamous cell carcinoma. The algorithm was implemented on an NVidia GEFORCE GTV 560M GPU. The cases were taken from the Louvain database, which includes contours of the macroscopically defined BTV drawn using histology of resected tissue. The images were pre-processed using denoising/deconvolution. The segmented volumes agreed well with the macroscopic contours, with an average concordance index and classification error of 0.6 ± 0.09 and 55 ± 16.5%, respectively. The algorithm in its present implementation requires approximately 0.5-1.3 sec per iteration and can reach convergence within 10-30 iterations. The Jensen-Renyi active contour method was shown to come close to and in terms of concordance, outperforms a variety of PET segmentation methods that have been previously evaluated using the same data. Further evaluation on a larger dataset along with performance optimization is necessary before clinical deployment. © 2012 American Association of Physicists in Medicine.

A Novel Gradient Vector Flow Snake Model Based on Convex Function for Infrared Image Segmentation

PubMed Central

Zhang, Rui; Zhu, Shiping; Zhou, Qin

2016-01-01

Infrared image segmentation is a challenging topic because infrared images are characterized by high noise, low contrast, and weak edges. Active contour models, especially gradient vector flow, have several advantages in terms of infrared image segmentation. However, the GVF (Gradient Vector Flow) model also has some drawbacks including a dilemma between noise smoothing and weak edge protection, which decrease the effect of infrared image segmentation significantly. In order to solve this problem, we propose a novel generalized gradient vector flow snakes model combining GGVF (Generic Gradient Vector Flow) and NBGVF (Normally Biased Gradient Vector Flow) models. We also adopt a new type of coefficients setting in the form of convex function to improve the ability of protecting weak edges while smoothing noises. Experimental results and comparisons against other methods indicate that our proposed snakes model owns better ability in terms of infrared image segmentation than other snakes models. PMID:27775660

Segmentation of heterogeneous blob objects through voting and level set formulation

PubMed Central

Chang, Hang; Yang, Qing; Parvin, Bahram

2009-01-01

Blob-like structures occur often in nature, where they aid in cueing and the pre-attentive process. These structures often overlap, form perceptual boundaries, and are heterogeneous in shape, size, and intensity. In this paper, voting, Voronoi tessellation, and level set methods are combined to delineate blob-like structures. Voting and subsequent Voronoi tessellation provide the initial condition and the boundary constraints for each blob, while curve evolution through level set formulation provides refined segmentation of each blob within the Voronoi region. The paper concludes with the application of the proposed method to a dataset produced from cell based fluorescence assays and stellar data. PMID:19774202

[Effect of urapidil combined with phentolamine on hypertension during extracorporeal circulation].

PubMed

Wang, Fangjun; Chen, Bin; Liu, Yang; Tu, Faping

2014-08-01

To study the effect of urapidil combined with phentolamine in the management of hypertension during extracorporeal circulation. Ninety patients undergoing aortic and mitral valve replacement were randomly divided into 3 equal groups to receive treatment with phentolamine (group A), urapidil (group B), or both (group C) during extracorporeal circulation. The mean arterial pressure (MAP) before and after drug administration, time interval of two administrations, spontaneous recovery of heart beat after aorta unclamping, ventricular arrhythmia, changes of ST-segment 1 min after the recovery of heart beat, ante-parallel cycle time, aorta clamping time, post-parallel cycle time, dopamine dose after cardiac resuscitation, and perioperative changes of plasma TNF-α and IL-6 levels were recorded. There was no significant difference in MAP between the 3 groups before or after hypotensive drug administration (P>0.05). The time interval of two hypotensive drug administrations was longer in group C than in groups A and B (P<0.05). The incidence of spontaneous recovery of heart beat after aorta unclamping, incidence of ventricular arrhythmia, changes of ST-segment 1 min after the recovery of heart beat, ante-parallel cycle time, aorta clamping time, and post-parallel cycle time were all comparable between the 3 groups. The dose of dopamine administered after cardiac resuscitation was significantly larger in group B than in groups A or group C (P<0.05). The plasma levels of TNF-α and IL-6 were significantly increased after CPB and after the operation in all the groups, but were lowed in group C than in groups A and B at the end of CPB and at 2 h and 12 after the operation. Urapidil combined with phentolamine can control hypertension during extracorporeal circulation without causing hypotension.

Automated volumetric segmentation of retinal fluid on optical coherence tomography

PubMed Central

Wang, Jie; Zhang, Miao; Pechauer, Alex D.; Liu, Liang; Hwang, Thomas S.; Wilson, David J.; Li, Dengwang; Jia, Yali

2016-01-01

We propose a novel automated volumetric segmentation method to detect and quantify retinal fluid on optical coherence tomography (OCT). The fuzzy level set method was introduced for identifying the boundaries of fluid filled regions on B-scans (x and y-axes) and C-scans (z-axis). The boundaries identified from three types of scans were combined to generate a comprehensive volumetric segmentation of retinal fluid. Then, artefactual fluid regions were removed using morphological characteristics and by identifying vascular shadowing with OCT angiography obtained from the same scan. The accuracy of retinal fluid detection and quantification was evaluated on 10 eyes with diabetic macular edema. Automated segmentation had good agreement with manual segmentation qualitatively and quantitatively. The fluid map can be integrated with OCT angiogram for intuitive clinical evaluation. PMID:27446676

Cardiorespiratory fitness and exercise-induced ST segment depression in assessing the risk of sudden cardiac death in men.

PubMed

Hagnäs, Magnus J; Lakka, Timo A; Kurl, Sudhir; Rauramaa, Rainer; Mäkikallio, Timo H; Savonen, Kai; Laukkanen, Jari A

2017-03-01

The aim of this study was to investigate whether information on both cardiorespiratory fitness (CRF) and exercise-induced ST segment depression improves the prediction of sudden cardiac death (SCD) in men. The study was based on a population sample of 2328 men aged 42-60 years, who were followed up for on average 19 years. CRF was assessed with maximal exercise test using respiratory gas analysis, expressed in metabolic equivalents (METs) and dichotomised at eight METs. Exercise-induced ST segment depression was defined as 1 mm ST segment depression in ECG. Altogether 165 SCDs occurred during the follow-up. Men with low CRF (<8 METs) and exercise-induced ST segment depression had 4.8-fold (95% CI 2.9 to 7.9) higher risk of SCD than men with high CRF and without exercise-induced ST segment depression (p=0.013 for interaction) after adjustment for other cardiovascular risk factors. Men with high CRF and exercise-induced ST segment depression did not have a statistically significantly higher risk of SCD (HR 1.9, 95% CI 0.9 to 3.8) than men with high CRF and without exercise-induced ST segment depression. The combination of low CRF and exercise-induced ST segment depression was associated with a markedly increased risk of SCD in men. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Improved Estimation of Cardiac Function Parameters Using a Combination of Independent Automated Segmentation Results in Cardiovascular Magnetic Resonance Imaging.

PubMed

Lebenberg, Jessica; Lalande, Alain; Clarysse, Patrick; Buvat, Irene; Casta, Christopher; Cochet, Alexandre; Constantinidès, Constantin; Cousty, Jean; de Cesare, Alain; Jehan-Besson, Stephanie; Lefort, Muriel; Najman, Laurent; Roullot, Elodie; Sarry, Laurent; Tilmant, Christophe; Frouin, Frederique; Garreau, Mireille

2015-01-01

This work aimed at combining different segmentation approaches to produce a robust and accurate segmentation result. Three to five segmentation results of the left ventricle were combined using the STAPLE algorithm and the reliability of the resulting segmentation was evaluated in comparison with the result of each individual segmentation method. This comparison was performed using a supervised approach based on a reference method. Then, we used an unsupervised statistical evaluation, the extended Regression Without Truth (eRWT) that ranks different methods according to their accuracy in estimating a specific biomarker in a population. The segmentation accuracy was evaluated by estimating six cardiac function parameters resulting from the left ventricle contour delineation using a public cardiac cine MRI database. Eight different segmentation methods, including three expert delineations and five automated methods, were considered, and sixteen combinations of the automated methods using STAPLE were investigated. The supervised and unsupervised evaluations demonstrated that in most cases, STAPLE results provided better estimates than individual automated segmentation methods. Overall, combining different automated segmentation methods improved the reliability of the segmentation result compared to that obtained using an individual method and could achieve the accuracy of an expert.

Improved Estimation of Cardiac Function Parameters Using a Combination of Independent Automated Segmentation Results in Cardiovascular Magnetic Resonance Imaging

PubMed Central

Lebenberg, Jessica; Lalande, Alain; Clarysse, Patrick; Buvat, Irene; Casta, Christopher; Cochet, Alexandre; Constantinidès, Constantin; Cousty, Jean; de Cesare, Alain; Jehan-Besson, Stephanie; Lefort, Muriel; Najman, Laurent; Roullot, Elodie; Sarry, Laurent; Tilmant, Christophe

2015-01-01

This work aimed at combining different segmentation approaches to produce a robust and accurate segmentation result. Three to five segmentation results of the left ventricle were combined using the STAPLE algorithm and the reliability of the resulting segmentation was evaluated in comparison with the result of each individual segmentation method. This comparison was performed using a supervised approach based on a reference method. Then, we used an unsupervised statistical evaluation, the extended Regression Without Truth (eRWT) that ranks different methods according to their accuracy in estimating a specific biomarker in a population. The segmentation accuracy was evaluated by estimating six cardiac function parameters resulting from the left ventricle contour delineation using a public cardiac cine MRI database. Eight different segmentation methods, including three expert delineations and five automated methods, were considered, and sixteen combinations of the automated methods using STAPLE were investigated. The supervised and unsupervised evaluations demonstrated that in most cases, STAPLE results provided better estimates than individual automated segmentation methods. Overall, combining different automated segmentation methods improved the reliability of the segmentation result compared to that obtained using an individual method and could achieve the accuracy of an expert. PMID:26287691

Reducing consumption of confectionery foods: A post-hoc segmentation analysis using a social cognition approach.

PubMed

Naughton, Paul; McCarthy, Mary; McCarthy, Sinéad

2017-10-01

Considering confectionary consumption behaviour this cross-sectional study used social cognition variables to identify distinct segments in terms of their motivation and efforts to decrease their consumption of such foods with the aim of informing targeted social marketing campaigns. Using Latent Class analysis on a sample of 500 adults four segments were identified: unmotivated, triers, successful actors, and thrivers. The unmotivated and triers segments reported low levels of perceived need and perceived behavioural control (PBC) in addition to high levels of habit and hedonic hunger with regards their consumption of confectionery foods. Being a younger adult was associated with higher odds of being in the unmotivated and triers segments and being female was associated with higher odds of being in the triers and successful actors segments. The findings indicate that in the absence of strong commitment to eating low amounts of confectionery foods (i.e. perceived need) people will continue to overconsume free sugars regardless of motivation to change. It is therefore necessary to identify relevant messages or 'triggers' related to sugar consumption that resonate with young adults in particular. For those motivated to change, counteracting unhealthy eating habits and the effects of hedonic hunger may necessitate changes to food environments in order to make the healthy choice more appealing and accessible. Copyright © 2017 Elsevier Ltd. All rights reserved.

Segmenting the human genome based on states of neutral genetic divergence.

PubMed

Kuruppumullage Don, Prabhani; Ananda, Guruprasad; Chiaromonte, Francesca; Makova, Kateryna D

2013-09-03

Many studies have demonstrated that divergence levels generated by different mutation types vary and covary across the human genome. To improve our still-incomplete understanding of the mechanistic basis of this phenomenon, we analyze several mutation types simultaneously, anchoring their variation to specific regions of the genome. Using hidden Markov models on insertion, deletion, nucleotide substitution, and microsatellite divergence estimates inferred from human-orangutan alignments of neutrally evolving genomic sequences, we segment the human genome into regions corresponding to different divergence states--each uniquely characterized by specific combinations of divergence levels. We then parsed the mutagenic contributions of various biochemical processes associating divergence states with a broad range of genomic landscape features. We find that high divergence states inhabit guanine- and cytosine (GC)-rich, highly recombining subtelomeric regions; low divergence states cover inner parts of autosomes; chromosome X forms its own state with lowest divergence; and a state of elevated microsatellite mutability is interspersed across the genome. These general trends are mirrored in human diversity data from the 1000 Genomes Project, and departures from them highlight the evolutionary history of primate chromosomes. We also find that genes and noncoding functional marks [annotations from the Encyclopedia of DNA Elements (ENCODE)] are concentrated in high divergence states. Our results provide a powerful tool for biomedical data analysis: segmentations can be used to screen personal genome variants--including those associated with cancer and other diseases--and to improve computational predictions of noncoding functional elements.

Three-Dimensions Segmentation of Pulmonary Vascular Trees for Low Dose CT Scans

NASA Astrophysics Data System (ADS)

Lai, Jun; Huang, Ying; Wang, Ying; Wang, Jun

2016-12-01

Due to the low contrast and the partial volume effects, providing an accurate and in vivo analysis for pulmonary vascular trees from low dose CT scans is a challenging task. This paper proposes an automatic integration segmentation approach for the vascular trees in low dose CT scans. It consists of the following steps: firstly, lung volumes are acquired by the knowledge based method from the CT scans, and then the data are smoothed by the 3D Gaussian filter; secondly, two or three seeds are gotten by the adaptive 2D segmentation and the maximum area selecting from different position scans; thirdly, each seed as the start voxel is inputted for a quick multi-seeds 3D region growing to get vascular trees; finally, the trees are refined by the smooth filter. Through skeleton analyzing for the vascular trees, the results show that the proposed method can provide much better and lower level vascular branches.

Tectono-stratigraphic evolution of normal fault zones: Thal Fault Zone, Suez Rift, Egypt

NASA Astrophysics Data System (ADS)

Leppard, Christopher William

The evolution of linkage of normal fault populations to form continuous, basin bounding normal fault zones is recognised as an important control on the stratigraphic evolution of rift-basins. This project aims to investigate the temporal and spatial evolution of normal fault populations and associated syn-rift deposits from the initiation of early-formed, isolated normal faults (rift-initiation) to the development of a through-going fault zone (rift-climax) by documenting the tectono-stratigraphic evolution of the Sarbut EI Gamal segment of the exceptionally well-exposed Thai fault zone, Suez Rift, Egypt. A number of dated stratal surfaces mapped around the syn-rift depocentre of the Sarbut El Gamal segment allow constraints to be placed on the timing and style of deformation, and the spatial variability of facies along this segment of the fault zone. Data collected indicates that during the first 3.5 My of rifting the structural style was characterised by numerous, closely spaced, short (< 3 km), low displacement (< 200 m) synthetic and antithetic normal faults within 1 - 2 km of the present-day fault segment trace, accommodating surface deformation associated with the development of a fault propagation monocline above the buried, pre-cursor strands of the Sarbut El Gamal fault segment. The progressive localisation of displacement onto the fault segment during rift-climax resulted in the development of a major, surface-breaking fault 3.5 - 5 My after the onset of rifting and is recorded by the death of early-formed synthetic and antithetic faults up-section, and thickening of syn-rift strata towards the fault segment. The influence of intrabasinal highs at the tips of the Sarbut EI Gamal fault segment on the pre-rift sub-crop level, combined with observations from the early-formed structures and coeval deposits suggest that the overall length of the fault segment was fixed from an early stage. The fault segment is interpreted to have grown through rapid lateral propagation and early linkage of the precursor fault strands at depth before the fault segment broke surface, followed by the accumulation of displacement on the linked fault segment with minimal lateral propagation. This style of fault growth contrasts conventional fault growth models by which growth occurs through incremental increases in both displacement and length through time. The evolution of normal fault populations and fault zones exerts a first- order control on basin physiography and sediment supply, and therefore, the architecture and distribution of coeval syn-rift stratigraphy. The early syn-rift continental, Abu Zenima Formation, to shallow marine, Nukhul Formation show a pronounced westward increase in thickness controlled by the series of synthetic and antithetic faults up to 3 km west of present day Thai fault. The orientation of these faults controlled the location of fluvial conglomerates, sandstones and mudstones that shifted to the topographic lows created. The progressive localisation of displacement onto the Sarbut El Gamal fault segment during rift-climax resulted in an overall change in basin geometry. Accelerated subsidence rates led to sedimentation rates being outpaced by subsidence resulting in the development of a marine, sediment-starved, underfilled hangingwall depocentre characterised by slope-to-basinal depositional environments, with a laterally continuous slope apron in the immediate hangingwall, and point-sourced submarine fans. Controls on the spatial distribution, three dimensional architecture, and facies stacking patterns of coeval syn-rift deposits are identified as: I) structural style of the evolution and linkage of normal fault populations, ii) basin physiography, iii) evolution of drainage catchments, iv) bedrock lithology, and v) variations in sea/lake level.

Automatic segmentation of the prostate on CT images using deep learning and multi-atlas fusion

NASA Astrophysics Data System (ADS)

Ma, Ling; Guo, Rongrong; Zhang, Guoyi; Tade, Funmilayo; Schuster, David M.; Nieh, Peter; Master, Viraj; Fei, Baowei

2017-02-01

Automatic segmentation of the prostate on CT images has many applications in prostate cancer diagnosis and therapy. However, prostate CT image segmentation is challenging because of the low contrast of soft tissue on CT images. In this paper, we propose an automatic segmentation method by combining a deep learning method and multi-atlas refinement. First, instead of segmenting the whole image, we extract the region of interesting (ROI) to delete irrelevant regions. Then, we use the convolutional neural networks (CNN) to learn the deep features for distinguishing the prostate pixels from the non-prostate pixels in order to obtain the preliminary segmentation results. CNN can automatically learn the deep features adapting to the data, which are different from some handcrafted features. Finally, we select some similar atlases to refine the initial segmentation results. The proposed method has been evaluated on a dataset of 92 prostate CT images. Experimental results show that our method achieved a Dice similarity coefficient of 86.80% as compared to the manual segmentation. The deep learning based method can provide a useful tool for automatic segmentation of the prostate on CT images and thus can have a variety of clinical applications.

Correlation-based discrimination between cardiac tissue and blood for segmentation of 3D echocardiographic images

NASA Astrophysics Data System (ADS)

Saris, Anne E. C. M.; Nillesen, Maartje M.; Lopata, Richard G. P.; de Korte, Chris L.

2013-03-01

Automated segmentation of 3D echocardiographic images in patients with congenital heart disease is challenging, because the boundary between blood and cardiac tissue is poorly defined in some regions. Cardiologists mentally incorporate movement of the heart, using temporal coherence of structures to resolve ambiguities. Therefore, we investigated the merit of temporal cross-correlation for automated segmentation over the entire cardiac cycle. Optimal settings for maximum cross-correlation (MCC) calculation, based on a 3D cross-correlation based displacement estimation algorithm, were determined to obtain the best contrast between blood and myocardial tissue over the entire cardiac cycle. Resulting envelope-based as well as RF-based MCC values were used as additional external force in a deformable model approach, to segment the left-ventricular cavity in entire systolic phase. MCC values were tested against, and combined with, adaptive filtered, demodulated RF-data. Segmentation results were compared with manually segmented volumes using a 3D Dice Similarity Index (3DSI). Results in 3D pediatric echocardiographic images sequences (n = 4) demonstrate that incorporation of temporal information improves segmentation. The use of MCC values, either alone or in combination with adaptive filtered, demodulated RF-data, resulted in an increase of the 3DSI in 75% of the cases (average 3DSI increase: 0.71 to 0.82). Results might be further improved by optimizing MCC-contrast locally, in regions with low blood-tissue contrast. Reducing underestimation of the endocardial volume due to MCC processing scheme (choice of window size) and consequential border-misalignment, could also lead to more accurate segmentations. Furthermore, increasing the frame rate will also increase MCC-contrast and thus improve segmentation.

Analytical Modeling of Variable Density Multilayer Insulation for Cryogenic Storage

NASA Technical Reports Server (NTRS)

Hedayat, A.; Hastings, L. J.; Brown, T.; Cruit, Wendy (Technical Monitor)

2001-01-01

A unique foam/Multilayer Insulation (MLI) combination concept for orbital cryogenic storage was experimentally evaluated at NASA Marshall Space Flight Center (MSFC) using the Multipurpose Hydrogen Test Bed (MHTB). The MLI was designed for an on-orbit storage period of 45 days and included several unique features such as: a variable layer density and larger but fewer perforations for venting during ascent to orbit. Test results with liquid hydrogen indicated that the MLI weight or heat leak is reduced by about half in comparison with standard MLI. The focus of this paper is on analytical modeling of the Variable Density MLI (VD-MLI) on-orbit performance (i.e. vacuum/low pressure environment). The foam/VD-MLI combination model is considered to have five segments. The first segment represents the optional foam layer. The second, third, and fourth segments represent three MLI segments with different layer densities. The last segment is considered to be a shroud that surrounds the last MLI layer. Two approaches are considered. In the first approach, the variable density MLI is modeled layer by layer while in the second approach, a semi-empirical model is applied. Both models account for thermal radiation between shields, gas conduction, and solid conduction through the layer separator materials.

Hybrid Pixel-Based Method for Cardiac Ultrasound Fusion Based on Integration of PCA and DWT

PubMed Central

Sulaiman, Puteri Suhaiza; Wirza, Rahmita; Dimon, Mohd Zamrin; Khalid, Fatimah; Moosavi Tayebi, Rohollah

2015-01-01

Medical image fusion is the procedure of combining several images from one or multiple imaging modalities. In spite of numerous attempts in direction of automation ventricle segmentation and tracking in echocardiography, due to low quality images with missing anatomical details or speckle noises and restricted field of view, this problem is a challenging task. This paper presents a fusion method which particularly intends to increase the segment-ability of echocardiography features such as endocardial and improving the image contrast. In addition, it tries to expand the field of view, decreasing impact of noise and artifacts and enhancing the signal to noise ratio of the echo images. The proposed algorithm weights the image information regarding an integration feature between all the overlapping images, by using a combination of principal component analysis and discrete wavelet transform. For evaluation, a comparison has been done between results of some well-known techniques and the proposed method. Also, different metrics are implemented to evaluate the performance of proposed algorithm. It has been concluded that the presented pixel-based method based on the integration of PCA and DWT has the best result for the segment-ability of cardiac ultrasound images and better performance in all metrics. PMID:26089965

Black strings, low viscosity fluids, and violation of cosmic censorship.

PubMed

Lehner, Luis; Pretorius, Frans

2010-09-03

We describe the behavior of 5-dimensional black strings, subject to the Gregory-Laflamme instability. Beyond the linear level, the evolving strings exhibit a rich dynamics, where at intermediate stages the horizon can be described as a sequence of 3-dimensional spherical black holes joined by black string segments. These segments are themselves subject to a Gregory-Laflamme instability, resulting in a self-similar cascade, where ever-smaller satellite black holes form connected by ever-thinner string segments. This behavior is akin to satellite formation in low-viscosity fluid streams subject to the Rayleigh-Plateau instability. The simulation results imply that the string segments will reach zero radius in finite asymptotic time, whence the classical space-time terminates in a naked singularity. Since no fine-tuning is required to excite the instability, this constitutes a generic violation of cosmic censorship.

A spectral k-means approach to bright-field cell image segmentation.

PubMed

Bradbury, Laura; Wan, Justin W L

2010-01-01

Automatic segmentation of bright-field cell images is important to cell biologists, but difficult to complete due to the complex nature of the cells in bright-field images (poor contrast, broken halo, missing boundaries). Standard approaches such as level set segmentation and active contours work well for fluorescent images where cells appear as round shape, but become less effective when optical artifacts such as halo exist in bright-field images. In this paper, we present a robust segmentation method which combines the spectral and k-means clustering techniques to locate cells in bright-field images. This approach models an image as a matrix graph and segment different regions of the image by computing the appropriate eigenvectors of the matrix graph and using the k-means algorithm. We illustrate the effectiveness of the method by segmentation results of C2C12 (muscle) cells in bright-field images.

White blood cell counting analysis of blood smear images using various segmentation strategies

NASA Astrophysics Data System (ADS)

Safuan, Syadia Nabilah Mohd; Tomari, Razali; Zakaria, Wan Nurshazwani Wan; Othman, Nurmiza

2017-09-01

In white blood cell (WBC) diagnosis, the most crucial measurement parameter is the WBC counting. Such information is widely used to evaluate the effectiveness of cancer therapy and to diagnose several hidden infection within human body. The current practice of manual WBC counting is laborious and a very subjective assessment which leads to the invention of computer aided system (CAS) with rigorous image processing solution. In the CAS counting work, segmentation is the crucial step to ensure the accuracy of the counted cell. The optimal segmentation strategy that can work under various blood smeared image acquisition conditions is remain a great challenge. In this paper, a comparison between different segmentation methods based on color space analysis to get the best counting outcome is elaborated. Initially, color space correction is applied to the original blood smeared image to standardize the image color intensity level. Next, white blood cell segmentation is performed by using combination of several color analysis subtraction which are RGB, CMYK and HSV, and Otsu thresholding. Noises and unwanted regions that present after the segmentation process is eliminated by applying a combination of morphological and Connected Component Labelling (CCL) filter. Eventually, Circle Hough Transform (CHT) method is applied to the segmented image to estimate the number of WBC including the one under the clump region. From the experiment, it is found that G-S yields the best performance.

Joint multi-object registration and segmentation of left and right cardiac ventricles in 4D cine MRI

NASA Astrophysics Data System (ADS)

Ehrhardt, Jan; Kepp, Timo; Schmidt-Richberg, Alexander; Handels, Heinz

2014-03-01

The diagnosis of cardiac function based on cine MRI requires the segmentation of cardiac structures in the images, but the problem of automatic cardiac segmentation is still open, due to the imaging characteristics of cardiac MR images and the anatomical variability of the heart. In this paper, we present a variational framework for joint segmentation and registration of multiple structures of the heart. To enable the simultaneous segmentation and registration of multiple objects, a shape prior term is introduced into a region competition approach for multi-object level set segmentation. The proposed algorithm is applied for simultaneous segmentation of the myocardium as well as the left and right ventricular blood pool in short axis cine MRI images. Two experiments are performed: first, intra-patient 4D segmentation with a given initial segmentation for one time-point in a 4D sequence, and second, a multi-atlas segmentation strategy is applied to unseen patient data. Evaluation of segmentation accuracy is done by overlap coefficients and surface distances. An evaluation based on clinical 4D cine MRI images of 25 patients shows the benefit of the combined approach compared to sole registration and sole segmentation.

A correlative approach to segmenting phases and ferrite morphologies in transformation-induced plasticity steel using electron back-scattering diffraction and energy dispersive X-ray spectroscopy.

PubMed

Gazder, Azdiar A; Al-Harbi, Fayez; Spanke, Hendrik Th; Mitchell, David R G; Pereloma, Elena V

2014-12-01

Using a combination of electron back-scattering diffraction and energy dispersive X-ray spectroscopy data, a segmentation procedure was developed to comprehensively distinguish austenite, martensite, polygonal ferrite, ferrite in granular bainite and bainitic ferrite laths in a thermo-mechanically processed low-Si, high-Al transformation-induced plasticity steel. The efficacy of the ferrite morphologies segmentation procedure was verified by transmission electron microscopy. The variation in carbon content between the ferrite in granular bainite and bainitic ferrite laths was explained on the basis of carbon partitioning during their growth. Copyright © 2014 Elsevier B.V. All rights reserved.

Optic disc segmentation: level set methods and blood vessels inpainting

NASA Astrophysics Data System (ADS)

Almazroa, A.; Sun, Weiwei; Alodhayb, Sami; Raahemifar, Kaamran; Lakshminarayanan, Vasudevan

2017-03-01

Segmenting the optic disc (OD) is an important and essential step in creating a frame of reference for diagnosing optic nerve head (ONH) pathology such as glaucoma. Therefore, a reliable OD segmentation technique is necessary for automatic screening of ONH abnormalities. The main contribution of this paper is in presenting a novel OD segmentation algorithm based on applying a level set method on a localized OD image. To prevent the blood vessels from interfering with the level set process, an inpainting technique is applied. The algorithm is evaluated using a new retinal fundus image dataset called RIGA (Retinal Images for Glaucoma Analysis). In the case of low quality images, a double level set is applied in which the first level set is considered to be a localization for the OD. Five hundred and fifty images are used to test the algorithm accuracy as well as its agreement with manual markings by six ophthalmologists. The accuracy of the algorithm in marking the optic disc area and centroid is 83.9%, and the best agreement is observed between the results of the algorithm and manual markings in 379 images.

Whole abdominal wall segmentation using augmented active shape models (AASM) with multi-atlas label fusion and level set

NASA Astrophysics Data System (ADS)

Xu, Zhoubing; Baucom, Rebeccah B.; Abramson, Richard G.; Poulose, Benjamin K.; Landman, Bennett A.

2016-03-01

The abdominal wall is an important structure differentiating subcutaneous and visceral compartments and intimately involved with maintaining abdominal structure. Segmentation of the whole abdominal wall on routinely acquired computed tomography (CT) scans remains challenging due to variations and complexities of the wall and surrounding tissues. In this study, we propose a slice-wise augmented active shape model (AASM) approach to robustly segment both the outer and inner surfaces of the abdominal wall. Multi-atlas label fusion (MALF) and level set (LS) techniques are integrated into the traditional ASM framework. The AASM approach globally optimizes the landmark updates in the presence of complicated underlying local anatomical contexts. The proposed approach was validated on 184 axial slices of 20 CT scans. The Hausdorff distance against the manual segmentation was significantly reduced using proposed approach compared to that using ASM, MALF, and LS individually. Our segmentation of the whole abdominal wall enables the subcutaneous and visceral fat measurement, with high correlation to the measurement derived from manual segmentation. This study presents the first generic algorithm that combines ASM, MALF, and LS, and demonstrates practical application for automatically capturing visceral and subcutaneous fat volumes.

Mixture of Segmenters with Discriminative Spatial Regularization and Sparse Weight Selection*

PubMed Central

Chen, Ting; Rangarajan, Anand; Eisenschenk, Stephan J.

2011-01-01

This paper presents a novel segmentation algorithm which automatically learns the combination of weak segmenters and builds a strong one based on the assumption that the locally weighted combination varies w.r.t. both the weak segmenters and the training images. We learn the weighted combination during the training stage using a discriminative spatial regularization which depends on training set labels. A closed form solution to the cost function is derived for this approach. In the testing stage, a sparse regularization scheme is imposed to avoid overfitting. To the best of our knowledge, such a segmentation technique has never been reported in literature and we empirically show that it significantly improves on the performances of the weak segmenters. After showcasing the performance of the algorithm in the context of atlas-based segmentation, we present comparisons to the existing weak segmenter combination strategies on a hippocampal data set. PMID:22003748

Model-based video segmentation for vision-augmented interactive games

NASA Astrophysics Data System (ADS)

Liu, Lurng-Kuo

2000-04-01

This paper presents an architecture and algorithms for model based video object segmentation and its applications to vision augmented interactive game. We are especially interested in real time low cost vision based applications that can be implemented in software in a PC. We use different models for background and a player object. The object segmentation algorithm is performed in two different levels: pixel level and object level. At pixel level, the segmentation algorithm is formulated as a maximizing a posteriori probability (MAP) problem. The statistical likelihood of each pixel is calculated and used in the MAP problem. Object level segmentation is used to improve segmentation quality by utilizing the information about the spatial and temporal extent of the object. The concept of an active region, which is defined based on motion histogram and trajectory prediction, is introduced to indicate the possibility of a video object region for both background and foreground modeling. It also reduces the overall computation complexity. In contrast with other applications, the proposed video object segmentation system is able to create background and foreground models on the fly even without introductory background frames. Furthermore, we apply different rate of self-tuning on the scene model so that the system can adapt to the environment when there is a scene change. We applied the proposed video object segmentation algorithms to several prototype virtual interactive games. In our prototype vision augmented interactive games, a player can immerse himself/herself inside a game and can virtually interact with other animated characters in a real time manner without being constrained by helmets, gloves, special sensing devices, or background environment. The potential applications of the proposed algorithms including human computer gesture interface and object based video coding such as MPEG-4 video coding.

Automatic CT Brain Image Segmentation Using Two Level Multiresolution Mixture Model of EM

NASA Astrophysics Data System (ADS)

Jiji, G. Wiselin; Dehmeshki, Jamshid

2014-04-01

Tissue classification in computed tomography (CT) brain images is an important issue in the analysis of several brain dementias. A combination of different approaches for the segmentation of brain images is presented in this paper. A multi resolution algorithm is proposed along with scaled versions using Gaussian filter and wavelet analysis that extends expectation maximization (EM) algorithm. It is found that it is less sensitive to noise and got more accurate image segmentation than traditional EM. Moreover the algorithm has been applied on 20 sets of CT of the human brain and compared with other works. The segmentation results show the advantages of the proposed work have achieved more promising results and the results have been tested with Doctors.

Marker-Based Hierarchical Segmentation and Classification Approach for Hyperspectral Imagery

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.; Benediktsson, Jon Atli; Chanussot, Jocelyn

2011-01-01

The Hierarchical SEGmentation (HSEG) algorithm, which is a combination of hierarchical step-wise optimization and spectral clustering, has given good performances for hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. First, pixelwise classification is performed and the most reliably classified pixels are selected as markers, with the corresponding class labels. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. The experimental results show that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for hyperspectral image analysis.

Importance of envelope modulations during consonants and vowels in segmentally interrupted sentencesa)

PubMed Central

Fogerty, Daniel

2014-01-01

The present study investigated the importance of overall segment amplitude and intrinsic segment amplitude modulation of consonants and vowels to sentence intelligibility. Sentences were processed according to three conditions that replaced consonant or vowel segments with noise matched to the long-term average speech spectrum. Segments were replaced with (1) low-level noise that distorted the overall sentence envelope, (2) segment-level noise that restored the overall syllabic amplitude modulation of the sentence, and (3) segment-modulated noise that further restored faster temporal envelope modulations during the vowel. Results from the first experiment demonstrated an incremental benefit with increasing resolution of the vowel temporal envelope. However, amplitude modulations of replaced consonant segments had a comparatively minimal effect on overall sentence intelligibility scores. A second experiment selectively noise-masked preserved vowel segments in order to equate overall performance of consonant-replaced sentences to that of the vowel-replaced sentences. Results demonstrated no significant effect of restoring consonant modulations during the interrupting noise when existing vowel cues were degraded. A third experiment demonstrated greater perceived sentence continuity with the preservation or addition of vowel envelope modulations. Overall, results support previous investigations demonstrating the importance of vowel envelope modulations to the intelligibility of interrupted sentences. PMID:24606291

Segmentation of dermatoscopic images by frequency domain filtering and k-means clustering algorithms.

PubMed

Rajab, Maher I

2011-11-01

Since the introduction of epiluminescence microscopy (ELM), image analysis tools have been extended to the field of dermatology, in an attempt to algorithmically reproduce clinical evaluation. Accurate image segmentation of skin lesions is one of the key steps for useful, early and non-invasive diagnosis of coetaneous melanomas. This paper proposes two image segmentation algorithms based on frequency domain processing and k-means clustering/fuzzy k-means clustering. The two methods are capable of segmenting and extracting the true border that reveals the global structure irregularity (indentations and protrusions), which may suggest excessive cell growth or regression of a melanoma. As a pre-processing step, Fourier low-pass filtering is applied to reduce the surrounding noise in a skin lesion image. A quantitative comparison of the techniques is enabled by the use of synthetic skin lesion images that model lesions covered with hair to which Gaussian noise is added. The proposed techniques are also compared with an established optimal-based thresholding skin-segmentation method. It is demonstrated that for lesions with a range of different border irregularity properties, the k-means clustering and fuzzy k-means clustering segmentation methods provide the best performance over a range of signal to noise ratios. The proposed segmentation techniques are also demonstrated to have similar performance when tested on real skin lesions representing high-resolution ELM images. This study suggests that the segmentation results obtained using a combination of low-pass frequency filtering and k-means or fuzzy k-means clustering are superior to the result that would be obtained by using k-means or fuzzy k-means clustering segmentation methods alone. © 2011 John Wiley & Sons A/S.

Image processing pipeline for segmentation and material classification based on multispectral high dynamic range polarimetric images.

PubMed

Martínez-Domingo, Miguel Ángel; Valero, Eva M; Hernández-Andrés, Javier; Tominaga, Shoji; Horiuchi, Takahiko; Hirai, Keita

2017-11-27

We propose a method for the capture of high dynamic range (HDR), multispectral (MS), polarimetric (Pol) images of indoor scenes using a liquid crystal tunable filter (LCTF). We have included the adaptive exposure estimation (AEE) method to fully automatize the capturing process. We also propose a pre-processing method which can be applied for the registration of HDR images after they are already built as the result of combining different low dynamic range (LDR) images. This method is applied to ensure a correct alignment of the different polarization HDR images for each spectral band. We have focused our efforts in two main applications: object segmentation and classification into metal and dielectric classes. We have simplified the segmentation using mean shift combined with cluster averaging and region merging techniques. We compare the performance of our segmentation with that of Ncut and Watershed methods. For the classification task, we propose to use information not only in the highlight regions but also in their surrounding area, extracted from the degree of linear polarization (DoLP) maps. We present experimental results which proof that the proposed image processing pipeline outperforms previous techniques developed specifically for MSHDRPol image cubes.

Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion.

PubMed

Zhou, Feng; De la Torre, Fernando; Hodgins, Jessica K

2013-03-01

Temporal segmentation of human motion into plausible motion primitives is central to understanding and building computational models of human motion. Several issues contribute to the challenge of discovering motion primitives: the exponential nature of all possible movement combinations, the variability in the temporal scale of human actions, and the complexity of representing articulated motion. We pose the problem of learning motion primitives as one of temporal clustering, and derive an unsupervised hierarchical bottom-up framework called hierarchical aligned cluster analysis (HACA). HACA finds a partition of a given multidimensional time series into m disjoint segments such that each segment belongs to one of k clusters. HACA combines kernel k-means with the generalized dynamic time alignment kernel to cluster time series data. Moreover, it provides a natural framework to find a low-dimensional embedding for time series. HACA is efficiently optimized with a coordinate descent strategy and dynamic programming. Experimental results on motion capture and video data demonstrate the effectiveness of HACA for segmenting complex motions and as a visualization tool. We also compare the performance of HACA to state-of-the-art algorithms for temporal clustering on data of a honey bee dance. The HACA code is available online.

Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion

PubMed Central

Zhao, Yuanshen; Gong, Liang; Huang, Yixiang; Liu, Chengliang

2016-01-01

Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the  a*-component image and the I-component image, were extracted from the L*a*b* color space and luminance, in-phase, quadrature-phase (YIQ) color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost. PMID:26840313

Level dependence of distortion product otoacoustic emissions in the leopard frog, Rana pipiens pipiens.

PubMed

Meenderink, Sebastiaan W F; van Dijk, Pim

2004-06-01

The inner ear of frogs holds two papillae specialized in detecting airborne sound, the amphibian papilla (AP) and the basilar papilla (BP). We measured input-output (I/O) curves of distortion product otoacoustic emissions (DPOAEs) from both papillae, and compared their properties. As in other vertebrates, DPOAE I/O curves showed two distinct segments, separated by a notch or kneepoint. The slope of the low-level segment was conspicuously different between the AP and the BP. For DPOAE I/O curves from the AP, slopes were < or = 1 dB/dB, similar to what is found in mammals, birds and some lizards. For DPOAE I/O curves from the BP these slopes were much steeper (approximately 2 dB/dB). Slopes found at high stimulus levels were similar in the AP and the BP (approximately 2 dB/dB). This quantitative difference between the low-level slopes for DPOAEs from the AP and the BP may signify the involvement of different mechanisms in low-level DPOAE generation for the two papillae, respectively.

Computer aided system for segmentation and visualization of microcalcifications in digital mammograms.

PubMed

Reljin, Branimir; Milosević, Zorica; Stojić, Tomislav; Reljin, Irini

2009-01-01

Two methods for segmentation and visualization of microcalcifications in digital or digitized mammograms are described. First method is based on modern mathematical morphology, while the second one uses the multifractal approach. In the first method, by using an appropriate combination of some morphological operations, high local contrast enhancement, followed by significant suppression of background tissue, irrespective of its radiology density, is obtained. By iterative procedure, this method highly emphasizes only small bright details, possible microcalcifications. In a multifractal approach, from initial mammogram image, a corresponding multifractal "images" are created, from which a radiologist has a freedom to change the level of segmentation. An appropriate user friendly computer aided visualization (CAV) system with embedded two methods is realized. The interactive approach enables the physician to control the level and the quality of segmentation. Suggested methods were tested through mammograms from MIAS database as a gold standard, and from clinical praxis, using digitized films and digital images from full field digital mammograph.

Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters.

PubMed

Galavis, Paulina E; Hollensen, Christian; Jallow, Ngoneh; Paliwal, Bhudatt; Jeraj, Robert

2010-10-01

Characterization of textural features (spatial distributions of image intensity levels) has been considered as a tool for automatic tumor segmentation. The purpose of this work is to study the variability of the textural features in PET images due to different acquisition modes and reconstruction parameters. Twenty patients with solid tumors underwent PET/CT scans on a GE Discovery VCT scanner, 45-60 minutes post-injection of 10 mCi of [(18)F]FDG. Scans were acquired in both 2D and 3D modes. For each acquisition the raw PET data was reconstructed using five different reconstruction parameters. Lesions were segmented on a default image using the threshold of 40% of maximum SUV. Fifty different texture features were calculated inside the tumors. The range of variations of the features were calculated with respect to the average value. Fifty textural features were classified based on the range of variation in three categories: small, intermediate and large variability. Features with small variability (range ≤ 5%) were entropy-first order, energy, maximal correlation coefficient (second order feature) and low-gray level run emphasis (high-order feature). The features with intermediate variability (10% ≤ range ≤ 25%) were entropy-GLCM, sum entropy, high gray level run emphsis, gray level non-uniformity, small number emphasis, and entropy-NGL. Forty remaining features presented large variations (range > 30%). Textural features such as entropy-first order, energy, maximal correlation coefficient, and low-gray level run emphasis exhibited small variations due to different acquisition modes and reconstruction parameters. Features with low level of variations are better candidates for reproducible tumor segmentation. Even though features such as contrast-NGTD, coarseness, homogeneity, and busyness have been previously used, our data indicated that these features presented large variations, therefore they could not be considered as a good candidates for tumor segmentation.

Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters

PubMed Central

GALAVIS, PAULINA E.; HOLLENSEN, CHRISTIAN; JALLOW, NGONEH; PALIWAL, BHUDATT; JERAJ, ROBERT

2014-01-01

Background Characterization of textural features (spatial distributions of image intensity levels) has been considered as a tool for automatic tumor segmentation. The purpose of this work is to study the variability of the textural features in PET images due to different acquisition modes and reconstruction parameters. Material and methods Twenty patients with solid tumors underwent PET/CT scans on a GE Discovery VCT scanner, 45–60 minutes post-injection of 10 mCi of [18F]FDG. Scans were acquired in both 2D and 3D modes. For each acquisition the raw PET data was reconstructed using five different reconstruction parameters. Lesions were segmented on a default image using the threshold of 40% of maximum SUV. Fifty different texture features were calculated inside the tumors. The range of variations of the features were calculated with respect to the average value. Results Fifty textural features were classified based on the range of variation in three categories: small, intermediate and large variability. Features with small variability (range ≤ 5%) were entropy-first order, energy, maximal correlation coefficient (second order feature) and low-gray level run emphasis (high-order feature). The features with intermediate variability (10% ≤ range ≤ 25%) were entropy-GLCM, sum entropy, high gray level run emphsis, gray level non-uniformity, small number emphasis, and entropy-NGL. Forty remaining features presented large variations (range > 30%). Conclusion Textural features such as entropy-first order, energy, maximal correlation coefficient, and low-gray level run emphasis exhibited small variations due to different acquisition modes and reconstruction parameters. Features with low level of variations are better candidates for reproducible tumor segmentation. Even though features such as contrast-NGTD, coarseness, homogeneity, and busyness have been previously used, our data indicated that these features presented large variations, therefore they could not be considered as a good candidates for tumor segmentation. PMID:20831489

A method for smoothing segmented lung boundary in chest CT images

NASA Astrophysics Data System (ADS)

Yim, Yeny; Hong, Helen

2007-03-01

To segment low density lung regions in chest CT images, most of methods use the difference in gray-level value of pixels. However, radiodense pulmonary vessels and pleural nodules that contact with the surrounding anatomy are often excluded from the segmentation result. To smooth lung boundary segmented by gray-level processing in chest CT images, we propose a new method using scan line search. Our method consists of three main steps. First, lung boundary is extracted by our automatic segmentation method. Second, segmented lung contour is smoothed in each axial CT slice. We propose a scan line search to track the points on lung contour and find rapidly changing curvature efficiently. Finally, to provide consistent appearance between lung contours in adjacent axial slices, 2D closing in coronal plane is applied within pre-defined subvolume. Our method has been applied for performance evaluation with the aspects of visual inspection, accuracy and processing time. The results of our method show that the smoothness of lung contour was considerably increased by compensating for pulmonary vessels and pleural nodules.

A fully convolutional networks (FCN) based image segmentation algorithm in binocular imaging system

NASA Astrophysics Data System (ADS)

Long, Zourong; Wei, Biao; Feng, Peng; Yu, Pengwei; Liu, Yuanyuan

2018-01-01

This paper proposes an image segmentation algorithm with fully convolutional networks (FCN) in binocular imaging system under various circumstance. Image segmentation is perfectly solved by semantic segmentation. FCN classifies the pixels, so as to achieve the level of image semantic segmentation. Different from the classical convolutional neural networks (CNN), FCN uses convolution layers instead of the fully connected layers. So it can accept image of arbitrary size. In this paper, we combine the convolutional neural network and scale invariant feature matching to solve the problem of visual positioning under different scenarios. All high-resolution images are captured with our calibrated binocular imaging system and several groups of test data are collected to verify this method. The experimental results show that the binocular images are effectively segmented without over-segmentation. With these segmented images, feature matching via SURF method is implemented to obtain regional information for further image processing. The final positioning procedure shows that the results are acceptable in the range of 1.4 1.6 m, the distance error is less than 10mm.

Innovative visualization and segmentation approaches for telemedicine

NASA Astrophysics Data System (ADS)

Nguyen, D.; Roehrig, Hans; Borders, Marisa H.; Fitzpatrick, Kimberly A.; Roveda, Janet

2014-09-01

In health care applications, we obtain, manage, store and communicate using high quality, large volume of image data through integrated devices. In this paper we propose several promising methods that can assist physicians in image data process and communication. We design a new semi-automated segmentation approach for radiological images, such as CT and MRI to clearly identify the areas of interest. This approach combines the advantages from both the region-based method and boundary-based methods. It has three key steps compose: coarse segmentation by using fuzzy affinity and homogeneity operator, image division and reclassification using the Voronoi Diagram, and refining boundary lines using the level set model.

Boundary overlap for medical image segmentation evaluation

NASA Astrophysics Data System (ADS)

Yeghiazaryan, Varduhi; Voiculescu, Irina

2017-03-01

All medical image segmentation algorithms need to be validated and compared, and yet no evaluation framework is widely accepted within the imaging community. Collections of segmentation results often need to be compared and ranked by their effectiveness. Evaluation measures which are popular in the literature are based on region overlap or boundary distance. None of these are consistent in the way they rank segmentation results: they tend to be sensitive to one or another type of segmentation error (size, location, shape) but no single measure covers all error types. We introduce a new family of measures, with hybrid characteristics. These measures quantify similarity/difference of segmented regions by considering their overlap around the region boundaries. This family is more sensitive than other measures in the literature to combinations of segmentation error types. We compare measure performance on collections of segmentation results sourced from carefully compiled 2D synthetic data, and also on 3D medical image volumes. We show that our new measure: (1) penalises errors successfully, especially those around region boundaries; (2) gives a low similarity score when existing measures disagree, thus avoiding overly inflated scores; and (3) scores segmentation results over a wider range of values. We consider a representative measure from this family and the effect of its only free parameter on error sensitivity, typical value range, and running time.

Combining deep learning with anatomical analysis for segmentation of the portal vein for liver SBRT planning

NASA Astrophysics Data System (ADS)

Ibragimov, Bulat; Toesca, Diego; Chang, Daniel; Koong, Albert; Xing, Lei

2017-12-01

Automated segmentation of the portal vein (PV) for liver radiotherapy planning is a challenging task due to potentially low vasculature contrast, complex PV anatomy and image artifacts originated from fiducial markers and vasculature stents. In this paper, we propose a novel framework for automated segmentation of the PV from computed tomography (CT) images. We apply convolutional neural networks (CNNs) to learn the consistent appearance patterns of the PV using a training set of CT images with reference annotations and then enhance the PV in previously unseen CT images. Markov random fields (MRFs) were further used to smooth the results of the enhancement of the CNN enhancement and remove isolated mis-segmented regions. Finally, CNN-MRF-based enhancement was augmented with PV centerline detection that relied on PV anatomical properties such as tubularity and branch composition. The framework was validated on a clinical database with 72 CT images of patients scheduled for liver stereotactic body radiation therapy. The obtained accuracy of the segmentation was DSC= 0.83 and \

Multi-phase simultaneous segmentation of tumor in lung 4D-CT data with context information.

PubMed

Shen, Zhengwen; Wang, Huafeng; Xi, Weiwen; Deng, Xiaogang; Chen, Jin; Zhang, Yu

2017-01-01

Lung 4D computed tomography (4D-CT) plays an important role in high-precision radiotherapy because it characterizes respiratory motion, which is crucial for accurate target definition. However, the manual segmentation of a lung tumor is a heavy workload for doctors because of the large number of lung 4D-CT data slices. Meanwhile, tumor segmentation is still a notoriously challenging problem in computer-aided diagnosis. In this paper, we propose a new method based on an improved graph cut algorithm with context information constraint to find a convenient and robust approach of lung 4D-CT tumor segmentation. We combine all phases of the lung 4D-CT into a global graph, and construct a global energy function accordingly. The sub-graph is first constructed for each phase. A context cost term is enforced to achieve segmentation results in every phase by adding a context constraint between neighboring phases. A global energy function is finally constructed by combining all cost terms. The optimization is achieved by solving a max-flow/min-cut problem, which leads to simultaneous and robust segmentation of the tumor in all the lung 4D-CT phases. The effectiveness of our approach is validated through experiments on 10 different lung 4D-CT cases. The comparison with the graph cut without context constraint, the level set method and the graph cut with star shape prior demonstrates that the proposed method obtains more accurate and robust segmentation results.

Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets

PubMed Central

Cha, Kenny H.; Hadjiiski, Lubomir; Samala, Ravi K.; Chan, Heang-Ping; Caoili, Elaine M.; Cohan, Richard H.

2016-01-01

Purpose: The authors are developing a computerized system for bladder segmentation in CT urography (CTU) as a critical component for computer-aided detection of bladder cancer. Methods: A deep-learning convolutional neural network (DL-CNN) was trained to distinguish between the inside and the outside of the bladder using 160 000 regions of interest (ROI) from CTU images. The trained DL-CNN was used to estimate the likelihood of an ROI being inside the bladder for ROIs centered at each voxel in a CTU case, resulting in a likelihood map. Thresholding and hole-filling were applied to the map to generate the initial contour for the bladder, which was then refined by 3D and 2D level sets. The segmentation performance was evaluated using 173 cases: 81 cases in the training set (42 lesions, 21 wall thickenings, and 18 normal bladders) and 92 cases in the test set (43 lesions, 36 wall thickenings, and 13 normal bladders). The computerized segmentation accuracy using the DL likelihood map was compared to that using a likelihood map generated by Haar features and a random forest classifier, and that using our previous conjoint level set analysis and segmentation system (CLASS) without using a likelihood map. All methods were evaluated relative to the 3D hand-segmented reference contours. Results: With DL-CNN-based likelihood map and level sets, the average volume intersection ratio, average percent volume error, average absolute volume error, average minimum distance, and the Jaccard index for the test set were 81.9% ± 12.1%, 10.2% ± 16.2%, 14.0% ± 13.0%, 3.6 ± 2.0 mm, and 76.2% ± 11.8%, respectively. With the Haar-feature-based likelihood map and level sets, the corresponding values were 74.3% ± 12.7%, 13.0% ± 22.3%, 20.5% ± 15.7%, 5.7 ± 2.6 mm, and 66.7% ± 12.6%, respectively. With our previous CLASS with local contour refinement (LCR) method, the corresponding values were 78.0% ± 14.7%, 16.5% ± 16.8%, 18.2% ± 15.0%, 3.8 ± 2.3 mm, and 73.9% ± 13.5%, respectively. Conclusions: The authors demonstrated that the DL-CNN can overcome the strong boundary between two regions that have large difference in gray levels and provides a seamless mask to guide level set segmentation, which has been a problem for many gradient-based segmentation methods. Compared to our previous CLASS with LCR method, which required two user inputs to initialize the segmentation, DL-CNN with level sets achieved better segmentation performance while using a single user input. Compared to the Haar-feature-based likelihood map, the DL-CNN-based likelihood map could guide the level sets to achieve better segmentation. The results demonstrate the feasibility of our new approach of using DL-CNN in combination with level sets for segmentation of the bladder. PMID:27036584

Enhancing atlas based segmentation with multiclass linear classifiers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sdika, Michaël, E-mail: michael.sdika@creatis.insa-lyon.fr

Purpose: To present a method to enrich atlases for atlas based segmentation. Such enriched atlases can then be used as a single atlas or within a multiatlas framework. Methods: In this paper, machine learning techniques have been used to enhance the atlas based segmentation approach. The enhanced atlas defined in this work is a pair composed of a gray level image alongside an image of multiclass classifiers with one classifier per voxel. Each classifier embeds local information from the whole training dataset that allows for the correction of some systematic errors in the segmentation and accounts for the possible localmore » registration errors. The authors also propose to use these images of classifiers within a multiatlas framework: results produced by a set of such local classifier atlases can be combined using a label fusion method. Results: Experiments have been made on the in vivo images of the IBSR dataset and a comparison has been made with several state-of-the-art methods such as FreeSurfer and the multiatlas nonlocal patch based method of Coupé or Rousseau. These experiments show that their method is competitive with state-of-the-art methods while having a low computational cost. Further enhancement has also been obtained with a multiatlas version of their method. It is also shown that, in this case, nonlocal fusion is unnecessary. The multiatlas fusion can therefore be done efficiently. Conclusions: The single atlas version has similar quality as state-of-the-arts multiatlas methods but with the computational cost of a naive single atlas segmentation. The multiatlas version offers a improvement in quality and can be done efficiently without a nonlocal strategy.« less

Stabilizing potential of anterior, posterior, and circumferential fixation for multilevel cervical arthrodesis: an in vitro human cadaveric study of the operative and adjacent segment kinematics.

PubMed

Dmitriev, Anton E; Kuklo, Timothy R; Lehman, Ronald A; Rosner, Michael K

2007-03-15

This is an in vitro biomechanical study. The current investigation was performed to evaluate the stabilizing potential of anterior, posterior, and circumferential cervical fixation on operative and adjacent segment motion following 2 and 3-level reconstructions. Previous studies reported increases in adjacent level range of motion (ROM) and intradiscal pressure following single-level cervical arthrodesis; however, no studies have compared adjacent level effects following multilevel anterior versus posterior reconstructions. Ten human cadaveric cervical spines were biomechanically tested using an unconstrained spine simulator under axial rotation, flexion-extension, and lateral bending loading. After intact analysis, all specimens were sequentially instrumented from C3 to C5 with: (1) lateral mass fixation, (2) anterior cervical plate with interbody cages, and (3) combined anterior and posterior fixation. Following biomechanical analysis of 2-level constructs, fixation was extended to C6 and testing repeated. Full ROM was monitored at the operative and adjacent levels, and data normalized to the intact (100%). All reconstructive methods reduced operative level ROM relative to intact specimens under all loading methods (P < 0.05). However, circumferential fixation provided the greatest segmental stability among 2 and 3-level constructs (P < 0.05). Moreover, anterior cervical plate fixation was least efficient at stabilizing operative segments following C3-C6 arthrodesis (P < 0.05). Supradjacent ROM was increased for all treatment groups compared to normal data during flexion-extension testing (P < 0.05). Similar trends were observed under axial rotation and lateral bending loading. At the distal level, flexion-extension and axial rotation testing revealed comparable intergroup differences (P < 0.05), while lateral bending loading indicated greater ROM following 2-level circumferential fixation (P < 0.05). Results from our study revealed greater adjacent level motion following all 3 fixation types. No consistent significant intergroup differences in neighboring segment kinematics were detected among reconstructions. Circumferential fixation provided the greatest level of segmental stability without additional significant increase in adjacent level ROM.

Retinal blood vessel segmentation in high resolution fundus photographs using automated feature parameter estimation

NASA Astrophysics Data System (ADS)

Orlando, José Ignacio; Fracchia, Marcos; del Río, Valeria; del Fresno, Mariana

2017-11-01

Several ophthalmological and systemic diseases are manifested through pathological changes in the properties and the distribution of the retinal blood vessels. The characterization of such alterations requires the segmentation of the vasculature, which is a tedious and time-consuming task that is infeasible to be performed manually. Numerous attempts have been made to propose automated methods for segmenting the retinal vasculature from fundus photographs, although their application in real clinical scenarios is usually limited by their ability to deal with images taken at different resolutions. This is likely due to the large number of parameters that have to be properly calibrated according to each image scale. In this paper we propose to apply a novel strategy for automated feature parameter estimation, combined with a vessel segmentation method based on fully connected conditional random fields. The estimation model is learned by linear regression from structural properties of the images and known optimal configurations, that were previously obtained for low resolution data sets. Our experiments in high resolution images show that this approach is able to estimate appropriate configurations that are suitable for performing the segmentation task without requiring to re-engineer parameters. Furthermore, our combined approach reported state of the art performance on the benchmark data set HRF, as measured in terms of the F1-score and the Matthews correlation coefficient.

Brain Tumor Segmentation Using Deep Belief Networks and Pathological Knowledge.

PubMed

Zhan, Tianming; Chen, Yi; Hong, Xunning; Lu, Zhenyu; Chen, Yunjie

2017-01-01

In this paper, we propose an automatic brain tumor segmentation method based on Deep Belief Networks (DBNs) and pathological knowledge. The proposed method is targeted against gliomas (both low and high grade) obtained in multi-sequence magnetic resonance images (MRIs). Firstly, a novel deep architecture is proposed to combine the multi-sequences intensities feature extraction with classification to get the classification probabilities of each voxel. Then, graph cut based optimization is executed on the classification probabilities to strengthen the spatial relationships of voxels. At last, pathological knowledge of gliomas is applied to remove some false positives. Our method was validated in the Brain Tumor Segmentation Challenge 2012 and 2013 databases (BRATS 2012, 2013). The performance of segmentation results demonstrates our proposal providing a competitive solution with stateof- the-art methods. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

User-assisted video segmentation system for visual communication

NASA Astrophysics Data System (ADS)

Wu, Zhengping; Chen, Chun

2002-01-01

Video segmentation plays an important role for efficient storage and transmission in visual communication. In this paper, we introduce a novel video segmentation system using point tracking and contour formation techniques. Inspired by the results from the study of the human visual system, we intend to solve the video segmentation problem into three separate phases: user-assisted feature points selection, feature points' automatic tracking, and contour formation. This splitting relieves the computer of ill-posed automatic segmentation problems, and allows a higher level of flexibility of the method. First, the precise feature points can be found using a combination of user assistance and an eigenvalue-based adjustment. Second, the feature points in the remaining frames are obtained using motion estimation and point refinement. At last, contour formation is used to extract the object, and plus a point insertion process to provide the feature points for next frame's tracking.

Method of Grassland Information Extraction Based on Multi-Level Segmentation and Cart Model

NASA Astrophysics Data System (ADS)

Qiao, Y.; Chen, T.; He, J.; Wen, Q.; Liu, F.; Wang, Z.

2018-04-01

It is difficult to extract grassland accurately by traditional classification methods, such as supervised method based on pixels or objects. This paper proposed a new method combing the multi-level segmentation with CART (classification and regression tree) model. The multi-level segmentation which combined the multi-resolution segmentation and the spectral difference segmentation could avoid the over and insufficient segmentation seen in the single segmentation mode. The CART model was established based on the spectral characteristics and texture feature which were excavated from training sample data. Xilinhaote City in Inner Mongolia Autonomous Region was chosen as the typical study area and the proposed method was verified by using visual interpretation results as approximate truth value. Meanwhile, the comparison with the nearest neighbor supervised classification method was obtained. The experimental results showed that the total precision of classification and the Kappa coefficient of the proposed method was 95 % and 0.9, respectively. However, the total precision of classification and the Kappa coefficient of the nearest neighbor supervised classification method was 80 % and 0.56, respectively. The result suggested that the accuracy of classification proposed in this paper was higher than the nearest neighbor supervised classification method. The experiment certificated that the proposed method was an effective extraction method of grassland information, which could enhance the boundary of grassland classification and avoid the restriction of grassland distribution scale. This method was also applicable to the extraction of grassland information in other regions with complicated spatial features, which could avoid the interference of woodland, arable land and water body effectively.

A computerized recognition system for the home-based physiotherapy exercises using an RGBD camera.

PubMed

Ar, Ilktan; Akgul, Yusuf Sinan

2014-11-01

Computerized recognition of the home based physiotherapy exercises has many benefits and it has attracted considerable interest among the computer vision community. However, most methods in the literature view this task as a special case of motion recognition. In contrast, we propose to employ the three main components of a physiotherapy exercise (the motion patterns, the stance knowledge, and the exercise object) as different recognition tasks and embed them separately into the recognition system. The low level information about each component is gathered using machine learning methods. Then, we use a generative Bayesian network to recognize the exercise types by combining the information from these sources at an abstract level, which takes the advantage of domain knowledge for a more robust system. Finally, a novel postprocessing step is employed to estimate the exercise repetitions counts. The performance evaluation of the system is conducted with a new dataset which contains RGB (red, green, and blue) and depth videos of home-based exercise sessions for commonly applied shoulder and knee exercises. The proposed system works without any body-part segmentation, bodypart tracking, joint detection, and temporal segmentation methods. In the end, favorable exercise recognition rates and encouraging results on the estimation of repetition counts are obtained.

A general-purpose optimization program for engineering design

NASA Technical Reports Server (NTRS)

Vanderplaats, G. N.; Sugimoto, H.

1986-01-01

A new general-purpose optimization program for engineering design is described. ADS (Automated Design Synthesis) is a FORTRAN program for nonlinear constrained (or unconstrained) function minimization. The optimization process is segmented into three levels: Strategy, Optimizer, and One-dimensional search. At each level, several options are available so that a total of nearly 100 possible combinations can be created. An example of available combinations is the Augmented Lagrange Multiplier method, using the BFGS variable metric unconstrained minimization together with polynomial interpolation for the one-dimensional search.

a Framework of Change Detection Based on Combined Morphologica Features and Multi-Index Classification

NASA Astrophysics Data System (ADS)

Li, S.; Zhang, S.; Yang, D.

2017-09-01

Remote sensing images are particularly well suited for analysis of land cover change. In this paper, we present a new framework for detection of changing land cover using satellite imagery. Morphological features and a multi-index are used to extract typical objects from the imagery, including vegetation, water, bare land, buildings, and roads. Our method, based on connected domains, is different from traditional methods; it uses image segmentation to extract morphological features, while the enhanced vegetation index (EVI), the differential water index (NDWI) are used to extract vegetation and water, and a fragmentation index is used to the correct extraction results of water. HSV transformation and threshold segmentation extract and remove the effects of shadows on extraction results. Change detection is performed on these results. One of the advantages of the proposed framework is that semantic information is extracted automatically using low-level morphological features and indexes. Another advantage is that the proposed method detects specific types of change without any training samples. A test on ZY-3 images demonstrates that our framework has a promising capability to detect change.

A single-band envelope cue as a supplement to speechreading of segmentals: a comparison of auditory versus tactual presentation.

PubMed

Bratakos, M S; Reed, C M; Delhorne, L A; Denesvich, G

2001-06-01

The objective of this study was to compare the effects of a single-band envelope cue as a supplement to speechreading of segmentals and sentences when presented through either the auditory or tactual modality. The supplementary signal, which consisted of a 200-Hz carrier amplitude-modulated by the envelope of an octave band of speech centered at 500 Hz, was presented through a high-performance single-channel vibrator for tactual stimulation or through headphones for auditory stimulation. Normal-hearing subjects were trained and tested on the identification of a set of 16 medial vowels in /b/-V-/d/ context and a set of 24 initial consonants in C-/a/-C context under five conditions: speechreading alone (S), auditory supplement alone (A), tactual supplement alone (T), speechreading combined with the auditory supplement (S+A), and speechreading combined with the tactual supplement (S+T). Performance on various speech features was examined to determine the contribution of different features toward improvements under the aided conditions for each modality. Performance on the combined conditions (S+A and S+T) was compared with predictions generated from a quantitative model of multi-modal performance. To explore the relationship between benefits for segmentals and for connected speech within the same subjects, sentence reception was also examined for the three conditions of S, S+A, and S+T. For segmentals, performance generally followed the pattern of T < A < S < S+T < S+A. Significant improvements to speechreading were observed with both the tactual and auditory supplements for consonants (10 and 23 percentage-point improvements, respectively), but only with the auditory supplement for vowels (a 10 percentage-point improvement). The results of the feature analyses indicated that improvements to speechreading arose primarily from improved performance on the features low and tense for vowels and on the features voicing, nasality, and plosion for consonants. These improvements were greater for auditory relative to tactual presentation. When predicted percent-correct scores for the multi-modal conditions were compared with observed scores, the predicted values always exceeded observed values and the predictions were somewhat more accurate for the S+A than for the S+T conditions. For sentences, significant improvements to speechreading were observed with both the auditory and tactual supplements for high-context materials but again only with the auditory supplement for low-context materials. The tactual supplement provided a relative gain to speechreading of roughly 25% for all materials except low-context sentences (where gain was only 10%), whereas the auditory supplement provided relative gains of roughly 50% (for vowels, consonants, and low-context sentences) to 75% (for high-context sentences). The envelope cue provides a significant benefit to the speechreading of consonant segments when presented through either the auditory or tactual modality and of vowel segments through audition only. These benefits were found to be related to the reception of the same types of features under both modalities (voicing, manner, and plosion for consonants and low and tense for vowels); however, benefits were larger for auditory compared with tactual presentation. The benefits observed for segmentals appear to carry over into benefits for sentence reception under both modalities.

Implicit kernel sparse shape representation: a sparse-neighbors-based objection segmentation framework.

PubMed

Yao, Jincao; Yu, Huimin; Hu, Roland

2017-01-01

This paper introduces a new implicit-kernel-sparse-shape-representation-based object segmentation framework. Given an input object whose shape is similar to some of the elements in the training set, the proposed model can automatically find a cluster of implicit kernel sparse neighbors to approximately represent the input shape and guide the segmentation. A distance-constrained probabilistic definition together with a dualization energy term is developed to connect high-level shape representation and low-level image information. We theoretically prove that our model not only derives from two projected convex sets but is also equivalent to a sparse-reconstruction-error-based representation in the Hilbert space. Finally, a "wake-sleep"-based segmentation framework is applied to drive the evolutionary curve to recover the original shape of the object. We test our model on two public datasets. Numerical experiments on both synthetic images and real applications show the superior capabilities of the proposed framework.

Sensitivity analysis for future space missions with segmented telescopes for high-contrast imaging

NASA Astrophysics Data System (ADS)

Leboulleux, Lucie; Pueyo, Laurent; Sauvage, Jean-François; Mazoyer, Johan; Soummer, Remi; Fusco, Thierry; Sivaramakrishnan, Anand

2018-01-01

The detection and analysis of biomarkers on earth-like planets using direct-imaging will require both high-contrast imaging and spectroscopy at very close angular separation (10^10 star to planet flux ratio at a few 0.1”). This goal can only be achieved with large telescopes in space to overcome atmospheric turbulence, often combined with a coronagraphic instrument with wavefront control. Large segmented space telescopes such as studied for the LUVOIR mission will generate segment-level instabilities and cophasing errors in addition to local mirror surface errors and other aberrations of the overall optical system. These effects contribute directly to the degradation of the final image quality and contrast. We present an analytical model that produces coronagraphic images of a segmented pupil telescope in the presence of segment phasing aberrations expressed as Zernike polynomials. This model relies on a pair-based projection of the segmented pupil and provides results that match an end-to-end simulation with an rms error on the final contrast of ~3%. This analytical model can be applied both to static and dynamic modes, and either in monochromatic or broadband light. It retires the need for end-to-end Monte-Carlo simulations that are otherwise needed to build a rigorous error budget, by enabling quasi-instantaneous analytical evaluations. The ability to invert directly the analytical model provides direct constraints and tolerances on all segments-level phasing and aberrations.

Loosely coupled level sets for retinal layers and drusen segmentation in subjects with dry age-related macular degeneration

NASA Astrophysics Data System (ADS)

Novosel, Jelena; Wang, Ziyuan; de Jong, Henk; Vermeer, Koenraad A.; van Vliet, Lucas J.

2016-03-01

Optical coherence tomography (OCT) is used to produce high-resolution three-dimensional images of the retina, which permit the investigation of retinal irregularities. In dry age-related macular degeneration (AMD), a chronic eye disease that causes central vision loss, disruptions such as drusen and changes in retinal layer thicknesses occur which could be used as biomarkers for disease monitoring and diagnosis. Due to the topology disrupting pathology, existing segmentation methods often fail. Here, we present a solution for the segmentation of retinal layers in dry AMD subjects by extending our previously presented loosely coupled level sets framework which operates on attenuation coefficients. In eyes affected by AMD, Bruch's membrane becomes visible only below the drusen and our segmentation framework is adapted to delineate such a partially discernible interface. Furthermore, the initialization stage, which tentatively segments five interfaces, is modified to accommodate the appearance of drusen. This stage is based on Dijkstra's algorithm and combines prior knowledge on the shape of the interface, gradient and attenuation coefficient in the newly proposed cost function. This prior knowledge is incorporated by varying the weights for horizontal, diagonal and vertical edges. Finally, quantitative evaluation of the accuracy shows a good agreement between manual and automated segmentation.

Cortical Enhanced Tissue Segmentation of Neonatal Brain MR Images Acquired by a Dedicated Phased Array Coil

PubMed Central

Shi, Feng; Yap, Pew-Thian; Fan, Yong; Cheng, Jie-Zhi; Wald, Lawrence L.; Gerig, Guido; Lin, Weili; Shen, Dinggang

2010-01-01

The acquisition of high quality MR images of neonatal brains is largely hampered by their characteristically small head size and low tissue contrast. As a result, subsequent image processing and analysis, especially for brain tissue segmentation, are often hindered. To overcome this problem, a dedicated phased array neonatal head coil is utilized to improve MR image quality by effectively combing images obtained from 8 coil elements without lengthening data acquisition time. In addition, a subject-specific atlas based tissue segmentation algorithm is specifically developed for the delineation of fine structures in the acquired neonatal brain MR images. The proposed tissue segmentation method first enhances the sheet-like cortical gray matter (GM) structures in neonatal images with a Hessian filter for generation of cortical GM prior. Then, the prior is combined with our neonatal population atlas to form a cortical enhanced hybrid atlas, which we refer to as the subject-specific atlas. Various experiments are conducted to compare the proposed method with manual segmentation results, as well as with additional two population atlas based segmentation methods. Results show that the proposed method is capable of segmenting the neonatal brain with the highest accuracy, compared to other two methods. PMID:20862268

A Marker-Based Approach for the Automated Selection of a Single Segmentation from a Hierarchical Set of Image Segmentations

NASA Technical Reports Server (NTRS)

Tarabalka, Y.; Tilton, J. C.; Benediktsson, J. A.; Chanussot, J.

2012-01-01

The Hierarchical SEGmentation (HSEG) algorithm, which combines region object finding with region object clustering, has given good performances for multi- and hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. Two classification-based approaches for automatic marker selection are adapted and compared for this purpose. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. Three different implementations of the M-HSEG method are proposed and their performances in terms of classification accuracies are compared. The experimental results, presented for three hyperspectral airborne images, demonstrate that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for remote sensing image analysis.

Automatic summarization of soccer highlights using audio-visual descriptors.

PubMed

Raventós, A; Quijada, R; Torres, Luis; Tarrés, Francesc

2015-01-01

Automatic summarization generation of sports video content has been object of great interest for many years. Although semantic descriptions techniques have been proposed, many of the approaches still rely on low-level video descriptors that render quite limited results due to the complexity of the problem and to the low capability of the descriptors to represent semantic content. In this paper, a new approach for automatic highlights summarization generation of soccer videos using audio-visual descriptors is presented. The approach is based on the segmentation of the video sequence into shots that will be further analyzed to determine its relevance and interest. Of special interest in the approach is the use of the audio information that provides additional robustness to the overall performance of the summarization system. For every video shot a set of low and mid level audio-visual descriptors are computed and lately adequately combined in order to obtain different relevance measures based on empirical knowledge rules. The final summary is generated by selecting those shots with highest interest according to the specifications of the user and the results of relevance measures. A variety of results are presented with real soccer video sequences that prove the validity of the approach.

SU-E-J-168: Automated Pancreas Segmentation Based On Dynamic MRI

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gou, S; Rapacchi, S; Hu, P

2014-06-01

Purpose: MRI guided radiotherapy is particularly attractive for abdominal targets with low CT contrast. To fully utilize this modality for pancreas tracking, automated segmentation tools are needed. A hybrid gradient, region growth and shape constraint (hGReS) method to segment 2D upper abdominal dynamic MRI is developed for this purpose. Methods: 2D coronal dynamic MR images of 2 healthy volunteers were acquired with a frame rate of 5 f/second. The regions of interest (ROIs) included the liver, pancreas and stomach. The first frame was used as the source where the centers of the ROIs were annotated. These center locations were propagatedmore » to the next dynamic MRI frame. 4-neighborhood region transfer growth was performed from these initial seeds for rough segmentation. To improve the results, gradient, edge and shape constraints were applied to the ROIs before final refinement using morphological operations. Results from hGReS and 3 other automated segmentation methods using edge detection, region growth and level set were compared to manual contouring. Results: For the first patient, hGReS resulted in the organ segmentation accuracy as measure by the Dices index (0.77) for the pancreas. The accuracy was slightly superior to the level set method (0.72), and both are significantly more accurate than the edge detection (0.53) and region growth methods (0.42). For the second healthy volunteer, hGReS reliably segmented the pancreatic region, achieving a Dices index of 0.82, 0.92 and 0.93 for the pancreas, stomach and liver, respectively, comparing to manual segmentation. Motion trajectories derived from the hGReS, level set and manual segmentation methods showed high correlation to respiratory motion calculated using a lung blood vessel as the reference while the other two methods showed substantial motion tracking errors. hGReS was 10 times faster than level set. Conclusion: We have shown the feasibility of automated segmentation of the pancreas anatomy based on dynamic MRI.« less

Segmentation of stereo terrain images

NASA Astrophysics Data System (ADS)

George, Debra A.; Privitera, Claudio M.; Blackmon, Theodore T.; Zbinden, Eric; Stark, Lawrence W.

2000-06-01

We have studied four approaches to segmentation of images: three automatic ones using image processing algorithms and a fourth approach, human manual segmentation. We were motivated toward helping with an important NASA Mars rover mission task -- replacing laborious manual path planning with automatic navigation of the rover on the Mars terrain. The goal of the automatic segmentations was to identify an obstacle map on the Mars terrain to enable automatic path planning for the rover. The automatic segmentation was first explored with two different segmentation methods: one based on pixel luminance, and the other based on pixel altitude generated through stereo image processing. The third automatic segmentation was achieved by combining these two types of image segmentation. Human manual segmentation of Martian terrain images was used for evaluating the effectiveness of the combined automatic segmentation as well as for determining how different humans segment the same images. Comparisons between two different segmentations, manual or automatic, were measured using a similarity metric, SAB. Based on this metric, the combined automatic segmentation did fairly well in agreeing with the manual segmentation. This was a demonstration of a positive step towards automatically creating the accurate obstacle maps necessary for automatic path planning and rover navigation.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schmitt, Adam K.; Mahanthappa, Mahesh K.

Using a combination of small-angle X-ray scattering (SAXS) and transmission electron microscopy (TEM), we document the composition-dependent morphologies of 39 new poly(lactide-block-1,4-butadiene-block-lactide) (LBL) block polymers, comprising a broad dispersity B segment (Mn = 4.5–17.7 kg/mol;more » $$\\def\\eth{{\\specialfont\\char238}}$$ = Mw/Mn = 1.72–1.88) and narrow dispersity L end blocks (Mn = 0.6–15.3 kg/mol; $$\\def\\eth{{\\specialfont\\char238}}$$ = 1.10–1.21) with volume fractions 0.26 ≤ fB ≤ 0.95. A subset of these samples undergo melt self-assembly into cylindrical, lamellar, and apparently bicontinuous morphologies. By assessing the states of order and disorder in these triblock polymer melts using temperature-dependent SAXS, we find that broad B segment dispersity increases the minimum segregation strength χN ≳ 27 required for LBL triblock self-assembly relative to the self-consistent mean-field theory prediction χN ≥ 17.9 for narrow dispersity analogues. While B segment dispersity has previously been shown to thermodynamically stabilize the self-assembled morphologies of low χ/high N ABA triblocks, the present study indicates that broad B block dispersity in related high χ/low N systems destabilizes the microphase-separated melt. These observations are rationalized in terms of recent theories that suggest that broad segmental dispersity substantially enhances fluctuation effects at low N, thus disfavoring melt segregation.« less

Segmental dependent transport of low permeability compounds along the small intestine due to P-glycoprotein: the role of efflux transport in the oral absorption of BCS class III drugs.

PubMed

Dahan, Arik; Amidon, Gordon L

2009-01-01

The purpose of this study was to investigate the role of P-gp efflux in the in vivo intestinal absorption process of BCS class III P-gp substrates, i.e. high-solubility low-permeability drugs. The in vivo permeability of two H (2)-antagonists, cimetidine and famotidine, was determined by the single-pass intestinal perfusion model in different regions of the rat small intestine, in the presence or absence of the P-gp inhibitor verapamil. The apical to basolateral (AP-BL) and the BL-AP transport of the compounds in the presence or absence of various efflux transporters inhibitors (verapamil, erythromycin, quinidine, MK-571 and fumitremorgin C) was investigated across Caco-2 cell monolayers. P-gp expression levels in the different intestinal segments were confirmed by immunoblotting. Cimetidine and famotidine exhibited segmental dependent permeability through the gut wall, with decreased P(eff) in the distal ileum in comparison to the proximal regions of the intestine. Coperfusion of verapamil with the drugs significantly increased the permeability in the ileum, while no significant change in the jejunal permeability was observed. Both drugs exhibited significantly greater BL-AP than AP-BL Caco-2 permeability, indicative of net mucosal secretion. Concentration dependent decrease of this secretion was obtained by the P-gp inhibitors verapamil, erythromycin and quinidine, while no effect was evident by the MRP2 inhibitor MK-571 and the BCRP inhibitor FTC, indicating that P-gp is the transporter mediates the intestinal efflux of cimetidine and famotidine. P-gp levels throughout the intestine were inversely related to the in vivo permeability of the drugs from the different segments. The data demonstrate that for these high-solubility low-permeability P-gp substrates, P-gp limits in vivo intestinal absorption in the distal segments of the small intestine; however P-gp plays a minimal role in the proximal intestinal segments due to significant lower P-gp expression levels in this region.

Mixed vitiligo of Blaschko lines: a newly discovered presentation of vitiligo responsive to combination treatment.

PubMed

Kovacevic, Maja; Stanimirovic, Andrija; Vucic, Majda; Goren, Andy; Situm, Mirna; Lukinovic Skudar, Vesna; Lotti, Torello

2016-07-01

Vitiligo, depigmenting disorder of the skin and mucous membranes, affects up to 1% of the population worldwide. It is classified into four major types: segmental, non-segmental, mixed, and unclassified type. Non-segmental vitiligo refers to non-dermatomal distribution of lesions, while dermatomal distribution of lesions is present in patients with segmental vitiligo. Segmental vitiligo can also follow Blaschko lines - pathways of epidermal cell migration and proliferation during the development of the fetus. Here, we present patient with segmental and non-segmental vitiligo following Blaschko lines with excellent therapeutic response to combined therapy. Prior to our report, a case of segmental and non-segmental vitiligo followed by Blaschko lines was never described, therefore we suggest the term "mixed vitiligo of Blaschko lines" to describe this entity. This is also a rare case in which 90% repigmentation was achieved in patient with segmental and nonsegmental vitiligo following Blaschko lines in only 2 months of combined therapy. © 2016 Wiley Periodicals, Inc.

Application of the Low-dose One-stop-shop Cardiac CT Protocol with Third-generation Dual-source CT.

PubMed

Lin, Lu; Wang, Yining; Yi, Yan; Cao, Jian; Kong, Lingyan; Qian, Hao; Zhang, Hongzhi; Wu, Wei; Wang, Yun; Jin, Zhengyu

2017-02-20

Objective To evaluate the feasibility of a low-dose one-stop-shop cardiac CT imaging protocol with third-generation dual-source CT (DSCT). Methods Totally 23 coronary artery disease (CAD) patients were prospectively enrolled between March to September in 2016. All patients underwent an ATP stress dynamic myocardial perfusion imaging (MPI) (data acquired prospectively ECG-triggered during end systole by table shuttle mode in 32 seconds) at 70 kV combined with prospectively ECG-triggered high-pitch coronary artery angiography (CCTA) on a third-generation DSCT system. Myocardial blood flow (MBF) was quantified and compared between perfusion normal and abnormal myocardial segments based on AHA-17-segment model. CCTA images were evaluated qualitatively based on SCCT-18-segment model and the effective dose(ED) was calculated. In patients with subsequent catheter coronary angiography (CCA) as reference,the diagnosis performance of MPI (for per-vessel ≥50% and ≥70% stenosis) and CCTA (for≥50% stenosis) were assessed. Results Of 23 patients who had completed the examination of ATP stress MPI plus CCTA,12 patients received follow-up CCA. At ATP stress MPI,77 segments (19.7%) in 13 patients (56.5%) had perfusion abnormalities. The MBF values of hypo-perfused myocardial segments decreased significantly compared with normal segments [(93±22)ml/(100 ml·min) vs. (147±27)ml/(100 ml·min);t=15.978,P=0.000]. At CCTA,93.9% (308/328) of the coronary segments had diagnostic image quality. With CCA as the reference standard,the per-vessel and per-segment sensitivity,specificity,and accuracy of CCTA for stenosis≥50% were 94.1%,93.5%,and 93.7% and 90.9%,97.8%,and 96.8%,and the per-vessel sensitivity,specificity and accuracy of ATP stress MPI for stenosis≥50% and ≥70% were 68.7%,100%,and 89.5% and 91.7%,100%,and 97.9%. The total ED of MPI and CCTA was (3.9±1.3) mSv [MPI:(3.5±1.2) mSv,CCTA:(0.3±0.1) mSv]. Conclusion The third-generation DSCT stress dynamic MPI at 70 kV combined with prospectively ECG-triggered high-pitch CCTA is a feasible and reliable tool for clinical diagnosis,with remarkably reduced radiation dose.

An approach to analyze the breast tissues in infrared images using nonlinear adaptive level sets and Riesz transform features.

PubMed

Prabha, S; Suganthi, S S; Sujatha, C M

2015-01-01

Breast thermography is a potential imaging method for the early detection of breast cancer. The pathological conditions can be determined by measuring temperature variations in the abnormal breast regions. Accurate delineation of breast tissues is reported as a challenging task due to inherent limitations of infrared images such as low contrast, low signal to noise ratio and absence of clear edges. Segmentation technique is attempted to delineate the breast tissues by detecting proper lower breast boundaries and inframammary folds. Characteristic features are extracted to analyze the asymmetrical thermal variations in normal and abnormal breast tissues. An automated analysis of thermal variations of breast tissues is attempted using nonlinear adaptive level sets and Riesz transform. Breast thermal images are initially subjected to Stein's unbiased risk estimate based orthonormal wavelet denoising. These denoised images are enhanced using contrast-limited adaptive histogram equalization method. The breast tissues are then segmented using non-linear adaptive level set method. The phase map of enhanced image is integrated into the level set framework for final boundary estimation. The segmented results are validated against the corresponding ground truth images using overlap and regional similarity metrics. The segmented images are further processed with Riesz transform and structural texture features are derived from the transformed coefficients to analyze pathological conditions of breast tissues. Results show that the estimated average signal to noise ratio of denoised images and average sharpness of enhanced images are improved by 38% and 6% respectively. The interscale consideration adopted in the denoising algorithm is able to improve signal to noise ratio by preserving edges. The proposed segmentation framework could delineate the breast tissues with high degree of correlation (97%) between the segmented and ground truth areas. Also, the average segmentation accuracy and sensitivity are found to be 98%. Similarly, the maximum regional overlap between segmented and ground truth images obtained using volume similarity measure is observed to be 99%. Directionality as a feature, showed a considerable difference between normal and abnormal tissues which is found to be 11%. The proposed framework for breast thermal image analysis that is aided with necessary preprocessing is found to be useful in assisting the early diagnosis of breast abnormalities.

Bayesian automated cortical segmentation for neonatal MRI

NASA Astrophysics Data System (ADS)

Chou, Zane; Paquette, Natacha; Ganesh, Bhavana; Wang, Yalin; Ceschin, Rafael; Nelson, Marvin D.; Macyszyn, Luke; Gaonkar, Bilwaj; Panigrahy, Ashok; Lepore, Natasha

2017-11-01

Several attempts have been made in the past few years to develop and implement an automated segmentation of neonatal brain structural MRI. However, accurate automated MRI segmentation remains challenging in this population because of the low signal-to-noise ratio, large partial volume effects and inter-individual anatomical variability of the neonatal brain. In this paper, we propose a learning method for segmenting the whole brain cortical grey matter on neonatal T2-weighted images. We trained our algorithm using a neonatal dataset composed of 3 fullterm and 4 preterm infants scanned at term equivalent age. Our segmentation pipeline combines the FAST algorithm from the FSL library software and a Bayesian segmentation approach to create a threshold matrix that minimizes the error of mislabeling brain tissue types. Our method shows promising results with our pilot training set. In both preterm and full-term neonates, automated Bayesian segmentation generates a smoother and more consistent parcellation compared to FAST, while successfully removing the subcortical structure and cleaning the edges of the cortical grey matter. This method show promising refinement of the FAST segmentation by considerably reducing manual input and editing required from the user, and further improving reliability and processing time of neonatal MR images. Further improvement will include a larger dataset of training images acquired from different manufacturers.

Combinational pixel-by-pixel and object-level classifying, segmenting, and agglomerating in performing quantitative image analysis that distinguishes between healthy non-cancerous and cancerous cell nuclei and delineates nuclear, cytoplasm, and stromal material objects from stained biological tissue materials

DOEpatents

Boucheron, Laura E

2013-07-16

Quantitative object and spatial arrangement-level analysis of tissue are detailed using expert (pathologist) input to guide the classification process. A two-step method is disclosed for imaging tissue, by classifying one or more biological materials, e.g. nuclei, cytoplasm, and stroma, in the tissue into one or more identified classes on a pixel-by-pixel basis, and segmenting the identified classes to agglomerate one or more sets of identified pixels into segmented regions. Typically, the one or more biological materials comprises nuclear material, cytoplasm material, and stromal material. The method further allows a user to markup the image subsequent to the classification to re-classify said materials. The markup is performed via a graphic user interface to edit designated regions in the image.

Accuracy assessment of a surface electromyogram decomposition system in human first dorsal interosseus muscle

NASA Astrophysics Data System (ADS)

Hu, Xiaogang; Rymer, William Z.; Suresh, Nina L.

2014-04-01

Objective. The aim of this study is to assess the accuracy of a surface electromyogram (sEMG) motor unit (MU) decomposition algorithm during low levels of muscle contraction. Approach. A two-source method was used to verify the accuracy of the sEMG decomposition system, by utilizing simultaneous intramuscular and surface EMG recordings from the human first dorsal interosseous muscle recorded during isometric trapezoidal force contractions. Spike trains from each recording type were decomposed independently utilizing two different algorithms, EMGlab and dEMG decomposition algorithms. The degree of agreement of the decomposed spike timings was assessed for three different segments of the EMG signals, corresponding to specified regions in the force task. A regression analysis was performed to examine whether certain properties of the sEMG and force signal can predict the decomposition accuracy. Main results. The average accuracy of successful decomposition among the 119 MUs that were common to both intramuscular and surface records was approximately 95%, and the accuracy was comparable between the different segments of the sEMG signals (i.e., force ramp-up versus steady state force versus combined). The regression function between the accuracy and properties of sEMG and force signals revealed that the signal-to-noise ratio of the action potential and stability in the action potential records were significant predictors of the surface decomposition accuracy. Significance. The outcomes of our study confirm the accuracy of the sEMG decomposition algorithm during low muscle contraction levels and provide confidence in the overall validity of the surface dEMG decomposition algorithm.

SU-F-J-112: Clinical Feasibility Test of An RF Pulse-Based MRI Method for the Quantitative Fat-Water Segmentation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yee, S; Wloch, J; Pirkola, M

Purpose: Quantitative fat-water segmentation is important not only because of the clinical utility of fat-suppressed MRI images in better detecting lesions of clinical significance (in the midst of bright fat signal) but also because of the possible physical need, in which CT-like images based on the materials’ photon attenuation properties may have to be generated from MR images; particularly, as in the case of MR-only radiation oncology environment to obtain radiation dose calculation or as in the case of hybrid PET/MR modality to obtain attenuation correction map for the quantitative PET reconstruction. The majority of such fat-water quantitative segmentations havemore » been performed by utilizing the Dixon’s method and its variations, which have to enforce the proper settings (often predefined) of echo time (TE) in the pulse sequences. Therefore, such methods have been unable to be directly combined with those ultrashort TE (UTE) sequences that, taking the advantage of very low TE values (∼ 10’s microsecond), might be beneficial to directly detect bones. Recently, an RF pulse-based method (http://dx.doi.org/10.1016/j.mri.2015.11.006), termed as PROD pulse method, was introduced as a method of quantitative fat-water segmentation that does not have to depend on predefined TE settings. Here, the clinical feasibility of this method is verified in brain tumor patients by combining the PROD pulse with several sequences. Methods: In a clinical 3T MRI, the PROD pulse was combined with turbo spin echo (e.g. TR=1500, TE=16 or 60, ETL=15) or turbo field echo (e.g. TR=5.6, TE=2.8, ETL=12) sequences without specifying TE values. Results: The fat-water segmentation was possible without having to set specific TE values. Conclusion: The PROD pulse method is clinically feasible. Although not yet combined with UTE sequences in our laboratory, the method is potentially compatible with UTE sequences, and thus, might be useful to directly segment fat, water, bone and air.« less

Joint level-set and spatio-temporal motion detection for cell segmentation.

PubMed

Boukari, Fatima; Makrogiannis, Sokratis

2016-08-10

Cell segmentation is a critical step for quantification and monitoring of cell cycle progression, cell migration, and growth control to investigate cellular immune response, embryonic development, tumorigenesis, and drug effects on live cells in time-lapse microscopy images. In this study, we propose a joint spatio-temporal diffusion and region-based level-set optimization approach for moving cell segmentation. Moving regions are initially detected in each set of three consecutive sequence images by numerically solving a system of coupled spatio-temporal partial differential equations. In order to standardize intensities of each frame, we apply a histogram transformation approach to match the pixel intensities of each processed frame with an intensity distribution model learned from all frames of the sequence during the training stage. After the spatio-temporal diffusion stage is completed, we compute the edge map by nonparametric density estimation using Parzen kernels. This process is followed by watershed-based segmentation and moving cell detection. We use this result as an initial level-set function to evolve the cell boundaries, refine the delineation, and optimize the final segmentation result. We applied this method to several datasets of fluorescence microscopy images with varying levels of difficulty with respect to cell density, resolution, contrast, and signal-to-noise ratio. We compared the results with those produced by Chan and Vese segmentation, a temporally linked level-set technique, and nonlinear diffusion-based segmentation. We validated all segmentation techniques against reference masks provided by the international Cell Tracking Challenge consortium. The proposed approach delineated cells with an average Dice similarity coefficient of 89 % over a variety of simulated and real fluorescent image sequences. It yielded average improvements of 11 % in segmentation accuracy compared to both strictly spatial and temporally linked Chan-Vese techniques, and 4 % compared to the nonlinear spatio-temporal diffusion method. Despite the wide variation in cell shape, density, mitotic events, and image quality among the datasets, our proposed method produced promising segmentation results. These results indicate the efficiency and robustness of this method especially for mitotic events and low SNR imaging, enabling the application of subsequent quantification tasks.

X-ray tomography using the full complex index of refraction.

PubMed

Nielsen, M S; Lauridsen, T; Thomsen, M; Jensen, T H; Bech, M; Christensen, L B; Olsen, E V; Hviid, M; Feidenhans'l, R; Pfeiffer, F

2012-10-07

We report on x-ray tomography using the full complex index of refraction recorded with a grating-based x-ray phase-contrast setup. Combining simultaneous absorption and phase-contrast information, the distribution of the full complex index of refraction is determined and depicted in a bivariate graph. A simple multivariable threshold segmentation can be applied offering higher accuracy than with a single-variable threshold segmentation as well as new possibilities for the partial volume analysis and edge detection. It is particularly beneficial for low-contrast systems. In this paper, this concept is demonstrated by experimental results.

A robust and fast active contour model for image segmentation with intensity inhomogeneity

NASA Astrophysics Data System (ADS)

Ding, Keyan; Weng, Guirong

2018-04-01

In this paper, a robust and fast active contour model is proposed for image segmentation in the presence of intensity inhomogeneity. By introducing the local image intensities fitting functions before the evolution of curve, the proposed model can effectively segment images with intensity inhomogeneity. And the computation cost is low because the fitting functions do not need to be updated in each iteration. Experiments have shown that the proposed model has a higher segmentation efficiency compared to some well-known active contour models based on local region fitting energy. In addition, the proposed model is robust to initialization, which allows the initial level set function to be a small constant function.

BlobContours: adapting Blobworld for supervised color- and texture-based image segmentation

NASA Astrophysics Data System (ADS)

Vogel, Thomas; Nguyen, Dinh Quyen; Dittmann, Jana

2006-01-01

Extracting features is the first and one of the most crucial steps in recent image retrieval process. While the color features and the texture features of digital images can be extracted rather easily, the shape features and the layout features depend on reliable image segmentation. Unsupervised image segmentation, often used in image analysis, works on merely syntactical basis. That is, what an unsupervised segmentation algorithm can segment is only regions, but not objects. To obtain high-level objects, which is desirable in image retrieval, human assistance is needed. Supervised image segmentations schemes can improve the reliability of segmentation and segmentation refinement. In this paper we propose a novel interactive image segmentation technique that combines the reliability of a human expert with the precision of automated image segmentation. The iterative procedure can be considered a variation on the Blobworld algorithm introduced by Carson et al. from EECS Department, University of California, Berkeley. Starting with an initial segmentation as provided by the Blobworld framework, our algorithm, namely BlobContours, gradually updates it by recalculating every blob, based on the original features and the updated number of Gaussians. Since the original algorithm has hardly been designed for interactive processing we had to consider additional requirements for realizing a supervised segmentation scheme on the basis of Blobworld. Increasing transparency of the algorithm by applying usercontrolled iterative segmentation, providing different types of visualization for displaying the segmented image and decreasing computational time of segmentation are three major requirements which are discussed in detail.

Structure and Neotectonics of the Southern Chile Forearc 35°S - 40°S

NASA Astrophysics Data System (ADS)

Geersen, Jacob; Völker, David; Weinrebe, Wilhelm; Krastel-Gudegast, Sebastian; Behrmann, Jan H.

2010-05-01

The Southern Chile Forearc exhibits an extreme level of neotectonic deformation. On-land studies have documented a pronounced segmentation in the region 36°S - 41°S. However, information on the seaward continuation of the individual segments towards the Chile Trench is rare, as direct observations end at the coastline and are replaced by a less dense set of marine geophysical data. In this study we use swath bathymetric data combined with high and low-frequency reflection seismic data as well as results from heat-flow measurements to: (A) map and identify active deformation structures and investigate their spatial distribution, and (B) analyse the factors controlling segmentation along the Southern Chile Forearc. Considering the region 35°S to 40°S we found evidence for a division into four major segments; Concepcion North, Concepcion South, Nahuelbuta, and Tolten (from North to South). Within all four segments, the lower continental slope is dissected by distinct margin-parallel thrust ridges overlying active landward-dipping thrust faults, indicating the presence of an active accretionary prism. The middle and upper slope, however, shows major differences between the four segments. The Concepcion North Segment is dominated by a large margin-parallel thrust ridge. The Concepcion South Segment shows large up to 600 m high north-south aligned normal fault scarps highlighting east-west extension. The change from thrust to normal faulting domains is accompanied by a drastic decrease in surface heat-flow by a factor of up to four. Further south in the Nahuelbuta Segment, east-west trending active thrust ridges indicate north-south compression of this part of the forearc. Shortening in this segment is not only limited to the middle and upper slope, but includes the entire marine forearc and occurs perpendicular to the direction of plate convergence. In the southernmost Tolten Segment the middle and upper continental slope shows no signs of compressive or extensional deformation. For the factors controlling segmentation our data suggest that when considering the whole forearc variations in the overriding plate such as the position of continental fault zones are responsible for the large scale tectonic segmentation. The east-west oriented shortening structures in the Nahuelbuta Segment (perpendicular to the direction of plate motion) probably originate from the collision of the Chiloe Microplate with a marine buttress situated below the Concepcion South Segment. The Chiloe Microplate represents a 1000 km-sized forearc sliver, which is kinematically decoupled from stable South America along the Liquine-Ofqui and Lanalhue Fault Zones. The important transition from wholesale forearc compression to extension observed between the two Concepcion segments, however, is more likely related to plate boundary processes, i.e. different degrees of coupling and/or friction in the plate boundary itself.

Copoly(imide siloxane) Abhesive Materials with Varied Siloxane Oligomer Length

NASA Technical Reports Server (NTRS)

Wohl, Christoper J.; Atkins, Brad M.; Lin, Yi; Belcher, Marcus A.; Connell, John W.

2010-01-01

In this work, low surface energy copoly(imide siloxane)s were synthesized with various siloxane segment lengths. Characterization of these materials revealed that domain formation of the low surface energy component within the matrix was more prevalent for longer siloxane segments as indicated by increased opacity, decreased mechanical properties, and variation of the Tg. Incorporation of siloxanes lowered the polymer s surface energy as indicated by water contact angle values. Topographical modification of these materials by laser ablation patterning further reduced the surface energy, even generating superhydrophobic surfaces. Combined, the contact angle data and particle adhesion testing indicated that copoly(imide siloxane) materials may provide greater mitigation to particulate adhesion than polyimide materials alone. These enhanced surface properties for abhesive applications did result in a reduction of the tensile moduli of the copolymers. It is possible that lower siloxane loading levels would result in retention of the mechanical properties of the polyimide while still affording abhesive surface properties. This hypothesis is currently being investigated. Laser ablation patterning offers further reduction in particle retention as the available surface area for particle adhesion is reduced. Pattern variation and size dependencies are currently being evaluated. For the purposes of lunar dust adhesion mitigation, it is likely that this approach, termed passive due to the lack of input from an external energy source, would not be sufficient to mitigate surface contamination or clean contaminated surfaces for some lunar applications. It is feasible to combine these materials with active mitigation strategies - methods that utilize input from external energy sources - would broaden the applicability of such materials for abhesive purposes. Collaborative efforts along these lines have been initiated with researchers at NASA Kennedy Space Center where experiments are being conducted involving a series of embedded electrodes within polymeric matrices.

Safety analysis of urban arterials at the meso level.

PubMed

Li, Jia; Wang, Xuesong

2017-11-01

Urban arterials form the main structure of street networks. They typically have multiple lanes, high traffic volume, and high crash frequency. Classical crash prediction models investigate the relationship between arterial characteristics and traffic safety by treating road segments and intersections as isolated units. This micro-level analysis does not work when examining urban arterial crashes because signal spacing is typically short for urban arterials, and there are interactions between intersections and road segments that classical models do not accommodate. Signal spacing also has safety effects on both intersections and road segments that classical models cannot fully account for because they allocate crashes separately to intersections and road segments. In addition, classical models do not consider the impact on arterial safety of the immediately surrounding street network pattern. This study proposes a new modeling methodology that will offer an integrated treatment of intersections and road segments by combining signalized intersections and their adjacent road segments into a single unit based on road geometric design characteristics and operational conditions. These are called meso-level units because they offer an analytical approach between micro and macro. The safety effects of signal spacing and street network pattern were estimated for this study based on 118 meso-level units obtained from 21 urban arterials in Shanghai, and were examined using CAR (conditional auto regressive) models that corrected for spatial correlation among the units within individual arterials. Results showed shorter arterial signal spacing was associated with higher total and PDO (property damage only) crashes, while arterials with a greater number of parallel roads were associated with lower total, PDO, and injury crashes. The findings from this study can be used in the traffic safety planning, design, and management of urban arterials. Copyright © 2017 Elsevier Ltd. All rights reserved.

Deep neural ensemble for retinal vessel segmentation in fundus images towards achieving label-free angiography.

PubMed

Lahiri, A; Roy, Abhijit Guha; Sheet, Debdoot; Biswas, Prabir Kumar

2016-08-01

Automated segmentation of retinal blood vessels in label-free fundus images entails a pivotal role in computed aided diagnosis of ophthalmic pathologies, viz., diabetic retinopathy, hypertensive disorders and cardiovascular diseases. The challenge remains active in medical image analysis research due to varied distribution of blood vessels, which manifest variations in their dimensions of physical appearance against a noisy background. In this paper we formulate the segmentation challenge as a classification task. Specifically, we employ unsupervised hierarchical feature learning using ensemble of two level of sparsely trained denoised stacked autoencoder. First level training with bootstrap samples ensures decoupling and second level ensemble formed by different network architectures ensures architectural revision. We show that ensemble training of auto-encoders fosters diversity in learning dictionary of visual kernels for vessel segmentation. SoftMax classifier is used for fine tuning each member autoencoder and multiple strategies are explored for 2-level fusion of ensemble members. On DRIVE dataset, we achieve maximum average accuracy of 95.33% with an impressively low standard deviation of 0.003 and Kappa agreement coefficient of 0.708. Comparison with other major algorithms substantiates the high efficacy of our model.

Gray matter segmentation of the spinal cord with active contours in MR images.

PubMed

Datta, Esha; Papinutto, Nico; Schlaeger, Regina; Zhu, Alyssa; Carballido-Gamio, Julio; Henry, Roland G

2017-02-15

Fully or partially automated spinal cord gray matter segmentation techniques for spinal cord gray matter segmentation will allow for pivotal spinal cord gray matter measurements in the study of various neurological disorders. The objective of this work was multi-fold: (1) to develop a gray matter segmentation technique that uses registration methods with an existing delineation of the cord edge along with Morphological Geodesic Active Contour (MGAC) models; (2) to assess the accuracy and reproducibility of the newly developed technique on 2D PSIR T1 weighted images; (3) to test how the algorithm performs on different resolutions and other contrasts; (4) to demonstrate how the algorithm can be extended to 3D scans; and (5) to show the clinical potential for multiple sclerosis patients. The MGAC algorithm was developed using a publicly available implementation of a morphological geodesic active contour model and the spinal cord segmentation tool of the software Jim (Xinapse Systems) for initial estimate of the cord boundary. The MGAC algorithm was demonstrated on 2D PSIR images of the C2/C3 level with two different resolutions, 2D T2* weighted images of the C2/C3 level, and a 3D PSIR image. These images were acquired from 45 healthy controls and 58 multiple sclerosis patients selected for the absence of evident lesions at the C2/C3 level. Accuracy was assessed though visual assessment, Hausdorff distances, and Dice similarity coefficients. Reproducibility was assessed through interclass correlation coefficients. Validity was assessed through comparison of segmented gray matter areas in images with different resolution for both manual and MGAC segmentations. Between MGAC and manual segmentations in healthy controls, the mean Dice similarity coefficient was 0.88 (0.82-0.93) and the mean Hausdorff distance was 0.61 (0.46-0.76) mm. The interclass correlation coefficient from test and retest scans of healthy controls was 0.88. The percent change between the manual segmentations from high and low-resolution images was 25%, while the percent change between the MGAC segmentations from high and low resolution images was 13%. Between MGAC and manual segmentations in MS patients, the average Dice similarity coefficient was 0.86 (0.8-0.92) and the average Hausdorff distance was 0.83 (0.29-1.37) mm. We demonstrate that an automatic segmentation technique, based on a morphometric geodesic active contours algorithm, can provide accurate and precise spinal cord gray matter segmentations on 2D PSIR images. We have also shown how this automated technique can potentially be extended to other imaging protocols. Copyright © 2016 Elsevier Inc. All rights reserved.

Combining registration and active shape models for the automatic segmentation of the lymph node regions in head and neck CT images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen Antong; Deeley, Matthew A.; Niermann, Kenneth J.

2010-12-15

Purpose: Intensity-modulated radiation therapy (IMRT) is the state of the art technique for head and neck cancer treatment. It requires precise delineation of the target to be treated and structures to be spared, which is currently done manually. The process is a time-consuming task of which the delineation of lymph node regions is often the longest step. Atlas-based delineation has been proposed as an alternative, but, in the authors' experience, this approach is not accurate enough for routine clinical use. Here, the authors improve atlas-based segmentation results obtained for level II-IV lymph node regions using an active shape model (ASM)more » approach. Methods: An average image volume was first created from a set of head and neck patient images with minimally enlarged nodes. The average image volume was then registered using affine, global, and local nonrigid transformations to the other volumes to establish a correspondence between surface points in the atlas and surface points in each of the other volumes. Once the correspondence was established, the ASMs were created for each node level. The models were then used to first constrain the results obtained with an atlas-based approach and then to iteratively refine the solution. Results: The method was evaluated through a leave-one-out experiment. The ASM- and atlas-based segmentations were compared to manual delineations via the Dice similarity coefficient (DSC) for volume overlap and the Euclidean distance between manual and automatic 3D surfaces. The mean DSC value obtained with the ASM-based approach is 10.7% higher than with the atlas-based approach; the mean and median surface errors were decreased by 13.6% and 12.0%, respectively. Conclusions: The ASM approach is effective in reducing segmentation errors in areas of low CT contrast where purely atlas-based methods are challenged. Statistical analysis shows that the improvements brought by this approach are significant.« less

Mis-segmentation in voxel-based morphometry due to a signal intensity change in the putamen.

PubMed

Goto, Masami; Abe, Osamu; Miyati, Tosiaki; Aoki, Shigeki; Gomi, Tsutomu; Takeda, Tohoru

2017-12-01

The aims of this study were to demonstrate an association between changes in the signal intensity of the putamen on three-dimensional T1-weighted magnetic resonance images (3D-T1WI) and mis-segmentation, using the voxel-based morphometry (VBM) 8 toolbox. The sagittal 3D-T1WIs of 22 healthy volunteers were obtained for VBM analysis using the 1.5-T MR scanner. We prepared five levels of 3D-T1WI signal intensity (baseline, same level, background level, low level, and high level) in regions of interest containing the putamen. Groups of smoothed, spatially normalized tissue images were compared to the baseline group using a paired t test. The baseline was compared to the other four levels. In all comparisons, significant volume changes were observed around and outside the area that included the signal intensity change. The present study demonstrated an association between a change in the signal intensity of the putamen on 3D-T1WI and changed volume in segmented tissue images.

A novel variant associated with HDL-C levels by modifying DAGLB expression levels: An annotation-based genome-wide association study.

PubMed

Zhou, Dan; Zhang, Dandan; Sun, Xiaohui; Li, Zhiqiang; Ni, Yaqin; Shan, Zhongyan; Li, Hong; Liu, Chengguo; Zhang, Shuai; Liu, Yi; Zheng, Ruizhi; Pan, Feixia; Zhu, Yimin; Shi, Yongyong; Lai, Maode

2018-06-01

Although numbers of genome-wide association studies (GWAS) have been performed for serum lipid levels, limited heritability has been explained. Studies showed that combining data from GWAS and expression quantitative trait loci (eQTLs) signals can both enhance the discovery of trait-associated SNPs and gain a better understanding of the mechanism. We performed an annotation-based, multistage genome-wide screening for serum-lipid-level-associated loci in totally 6863 Han Chinese. A serum high-density lipoprotein cholesterol (HDL-C) associated variant rs1880118 (hg19 chr7:g. 6435220G>C) was replicated (P combined  = 1.4E-10). rs1880118 was associated with DAGLB (diacylglycerol lipase, beta) expression levels in subcutaneous adipose tissue (P = 5.9E-42) and explained 47.7% of the expression variance. After the replication, an active segment covering variants tagged by rs1880118 near 5' of DAGLB was annotated using histone modification and transcription factor binding signals. The luciferase report assay revealed that the segment containing the minor alleles showed increased transcriptional activity compared with segment contains the major alleles, which was consistent with the eQTL analyses. The expression-trait association tests indicated the association between the DAGLB and serum HDL-C levels using gene-based approaches called "TWAS" (P = 3.0E-8), "SMR" (P = 1.1E-4), and "Sherlock" (P = 1.6E-6). To summarize, we identified a novel HDL-C-associated variant which explained nearly half of the expression variance of DAGLB. Integrated analyses established a genotype-gene-phenotype three-way association and expanded our knowledge of DAGLB in lipid metabolism.

Supervised variational model with statistical inference and its application in medical image segmentation.

PubMed

Li, Changyang; Wang, Xiuying; Eberl, Stefan; Fulham, Michael; Yin, Yong; Dagan Feng, David

2015-01-01

Automated and general medical image segmentation can be challenging because the foreground and the background may have complicated and overlapping density distributions in medical imaging. Conventional region-based level set algorithms often assume piecewise constant or piecewise smooth for segments, which are implausible for general medical image segmentation. Furthermore, low contrast and noise make identification of the boundaries between foreground and background difficult for edge-based level set algorithms. Thus, to address these problems, we suggest a supervised variational level set segmentation model to harness the statistical region energy functional with a weighted probability approximation. Our approach models the region density distributions by using the mixture-of-mixtures Gaussian model to better approximate real intensity distributions and distinguish statistical intensity differences between foreground and background. The region-based statistical model in our algorithm can intuitively provide better performance on noisy images. We constructed a weighted probability map on graphs to incorporate spatial indications from user input with a contextual constraint based on the minimization of contextual graphs energy functional. We measured the performance of our approach on ten noisy synthetic images and 58 medical datasets with heterogeneous intensities and ill-defined boundaries and compared our technique to the Chan-Vese region-based level set model, the geodesic active contour model with distance regularization, and the random walker model. Our method consistently achieved the highest Dice similarity coefficient when compared to the other methods.

Low-dose adenosine stress echocardiography: detection of myocardial viability.

PubMed

Djordjevic-Dikic, Ana; Ostojic, Miodrag; Beleslin, Branko; Nedeljkovic, Ivana; Stepanovic, Jelena; Stojkovic, Sinisa; Petrasinovic, Zorica; Nedeljkovic, Milan; Saponjski, Jovica; Giga, Vojislav

2003-06-03

The aim of this study was to evaluate the diagnostic potential of low-dose adenosine stress echocardiography in detection of myocardial viability. Vasodilation through low dose dipyridamole infusion may recruit contractile reserve by increasing coronary flow or by increasing levels of endogenous adenosine. Forty-three patients with resting dyssynergy, due to previous myocardial infarction, underwent low-dose adenosine (80, 100, 110 mcg/kg/min in 3 minutes intervals) echocardiography test. Gold standard for myocardial viability was improvement in systolic thickening of dyssinergic segments of >or= 1 grade at follow-up. Coronary angiography was done in 41 pts. Twenty-seven patients were revascularized and 16 were medically treated. Echocardiographic follow up data (12 +/- 2 months) were available in 24 revascularized patients. Wall motion score index improved from rest 1.55 +/- 0.30 to 1.33 +/- 0.26 at low-dose adenosine (p < 0.001). Of the 257 segments with baseline dyssynergy, adenosine echocardiography identified 122 segments as positive for viability, and 135 as necrotic since no improvement of systolic thickening was observed. Follow-up wall motion score index was 1.31 +/- 0.30 (p < 0.001 vs. rest). The sensitivity of adenosine echo test for identification of viable segments was 87%, while specificity was 95%, and diagnostic accuracy 90%. Positive and negative predictive values were 97% and 80%, respectively. Low-dose adenosine stress echocardiography test has high diagnostic potential for detection of myocardial viability in the group of patients with left ventricle dysfunction due to previous myocardial infarction. Low dose adenosine stress echocardiography may be adequate alternative to low-dose dobutamine test for evaluation of myocardial viability.

Segmentation of white rat sperm image

NASA Astrophysics Data System (ADS)

Bai, Weiguo; Liu, Jianguo; Chen, Guoyuan

2011-11-01

The segmentation of sperm image exerts a profound influence in the analysis of sperm morphology, which plays a significant role in the research of animals' infertility and reproduction. To overcome the microscope image's properties of low contrast and highly polluted noise, and to get better segmentation results of sperm image, this paper presents a multi-scale gradient operator combined with a multi-structuring element for the micro-spermatozoa image of white rat, as the multi-scale gradient operator can smooth the noise of an image, while the multi-structuring element can retain more shape details of the sperms. Then, we use the Otsu method to segment the modified gradient image whose gray scale processed is strong in sperms and weak in the background, converting it into a binary sperm image. As the obtained binary image owns impurities that are not similar with sperms in the shape, we choose a form factor to filter those objects whose form factor value is larger than the select critical value, and retain those objects whose not. And then, we can get the final binary image of the segmented sperms. The experiment shows this method's great advantage in the segmentation of the micro-spermatozoa image.

Design and Analysis of an X-Ray Mirror Assembly Using the Meta-Shell Approach

NASA Technical Reports Server (NTRS)

McClelland, Ryan S.; Bonafede, Joseph; Saha, Timo T.; Solly, Peter M.; Zhang, William W.

2016-01-01

Lightweight and high resolution optics are needed for future space-based x-ray telescopes to achieve advances in high-energy astrophysics. Past missions such as Chandra and XMM-Newton have achieved excellent angular resolution using a full shell mirror approach. Other missions such as Suzaku and NuSTAR have achieved lightweight mirrors using a segmented approach. This paper describes a new approach, called meta-shells, which combines the fabrication advantages of segmented optics with the alignment advantages of full shell optics. Meta-shells are built by layering overlapping mirror segments onto a central structural shell. The resulting optic has the stiffness and rotational symmetry of a full shell, but with an order of magnitude greater collecting area. Several meta-shells so constructed can be integrated into a large x-ray mirror assembly by proven methods used for Chandra and XMM-Newton. The mirror segments are mounted to the meta-shell using a novel four point semi-kinematic mount. The four point mount deterministically locates the segment in its most performance sensitive degrees of freedom. Extensive analysis has been performed to demonstrate the feasibility of the four point mount and meta-shell approach. A mathematical model of a meta-shell constructed with mirror segments bonded at four points and subject to launch loads has been developed to determine the optimal design parameters, namely bond size, mirror segment span, and number of layers per meta-shell. The parameters of an example 1.3 m diameter mirror assembly are given including the predicted effective area. To verify the mathematical model and support opto-mechanical analysis, a detailed finite element model of a meta-shell was created. Finite element analysis predicts low gravity distortion and low sensitivity to thermal gradients.

Shape-controlled continuous synthesis of metal nanostructures

NASA Astrophysics Data System (ADS)

Sebastian, Victor; Smith, Christopher D.; Jensen, Klavs F.

2016-03-01

A segmented flow-based microreactor is used for the continuous production of faceted nanocrystals. Flow segmentation is proposed as a versatile tool to manipulate the reduction kinetics and control the growth of faceted nanostructures; tuning the size and shape. Switching the gas from oxygen to carbon monoxide permits the adjustment in nanostructure growth from 1D (nanorods) to 2D (nanosheets). CO is a key factor in the formation of Pd nanosheets and Pt nanocubes; operating as a second phase, a reductant, and a capping agent. This combination confines the growth to specific structures. In addition, the segmented flow microfluidic reactor inherently has the ability to operate in a reproducible manner at elevated temperatures and pressures whilst confining potentially toxic reactants, such as CO, in nanoliter slugs. This continuous system successfully synthesised Pd nanorods with an aspect ratio of 6; thin palladium nanosheets with a thickness of 1.5 nm; and Pt nanocubes with a 5.6 nm edge length, all in a synthesis time as low as 150 s.A segmented flow-based microreactor is used for the continuous production of faceted nanocrystals. Flow segmentation is proposed as a versatile tool to manipulate the reduction kinetics and control the growth of faceted nanostructures; tuning the size and shape. Switching the gas from oxygen to carbon monoxide permits the adjustment in nanostructure growth from 1D (nanorods) to 2D (nanosheets). CO is a key factor in the formation of Pd nanosheets and Pt nanocubes; operating as a second phase, a reductant, and a capping agent. This combination confines the growth to specific structures. In addition, the segmented flow microfluidic reactor inherently has the ability to operate in a reproducible manner at elevated temperatures and pressures whilst confining potentially toxic reactants, such as CO, in nanoliter slugs. This continuous system successfully synthesised Pd nanorods with an aspect ratio of 6; thin palladium nanosheets with a thickness of 1.5 nm; and Pt nanocubes with a 5.6 nm edge length, all in a synthesis time as low as 150 s. Electronic supplementary information (ESI) available: ESI Fig. S1-S8. See DOI: 10.1039/c5nr08531d

The Florida manatee (Trichechus manatus latirostris) immunoglobulin heavy chain suggests the importance of clan III variable segments in repertoire diversity

USGS Publications Warehouse

Breaux, Breanna; Deiss, Thaddeus C.; Chen, Patricia L.; Cruz-Schneider, Maria Paula; Sena, Leonardo; Hunter, Margaret E.; Bonde, Robert K.; Criscitiello, Michael F.

2017-01-01

Manatees are a vulnerable, charismatic sentinel species from the evolutionarily divergent Afrotheria. Manatee health and resistance to infectious disease is of great concern to conservation groups, but little is known about their immune system. To develop manatee-specific tools for monitoring health, we first must have a general knowledge of how the immunoglobulin heavy (IgH) chain locus is organized and transcriptionally expressed. Using the genomic scaffolds of the Florida manatee (Trichechus manatus latirostris), we characterized the potential IgH segmental diversity and constant region isotypic diversity and performed the first Afrotherian repertoire analysis. The Florida manatee has low V(D)J combinatorial diversity (3744 potential combinations) and few constant region isotypes. They also lack clan III V segments, which may have caused reduced VH segment numbers. However, we found productive somatic hypermutation concentrated in the complementarity determining regions. In conclusion, manatees have limited IGHV clan and combinatorial diversity. This suggests that clan III V segments are essential for maintaining IgH locus diversity.

The Florida manatee (Trichechus manatus latirostris) immunoglobulin heavy chain suggests the importance of clan III variable segments in repertoire diversity.

PubMed

Breaux, Breanna; Deiss, Thaddeus C; Chen, Patricia L; Cruz-Schneider, Maria Paula; Sena, Leonardo; Hunter, Margaret E; Bonde, Robert K; Criscitiello, Michael F

2017-07-01

Manatees are a vulnerable, charismatic sentinel species from the evolutionarily divergent Afrotheria. Manatee health and resistance to infectious disease is of great concern to conservation groups, but little is known about their immune system. To develop manatee-specific tools for monitoring health, we first must have a general knowledge of how the immunoglobulin heavy (IgH) chain locus is organized and transcriptionally expressed. Using the genomic scaffolds of the Florida manatee (Trichechus manatus latirostris), we characterized the potential IgH segmental diversity and constant region isotypic diversity and performed the first Afrotherian repertoire analysis. The Florida manatee has low V(D)J combinatorial diversity (3744 potential combinations) and few constant region isotypes. They also lack clan III V segments, which may have caused reduced VH segment numbers. However, we found productive somatic hypermutation concentrated in the complementarity determining regions. In conclusion, manatees have limited IGHV clan and combinatorial diversity. This suggests that clan III V segments are essential for maintaining IgH locus diversity. Copyright © 2017 Elsevier Ltd. All rights reserved.

A fast 3D region growing approach for CT angiography applications

NASA Astrophysics Data System (ADS)

Ye, Zhen; Lin, Zhongmin; Lu, Cheng-chang

2004-05-01

Region growing is one of the most popular methods for low-level image segmentation. Many researches on region growing have focused on the definition of the homogeneity criterion or growing and merging criterion. However, one disadvantage of conventional region growing is redundancy. It requires a large memory usage, and the computation-efficiency is very low especially for 3D images. To overcome this problem, a non-recursive single-pass 3D region growing algorithm named SymRG is implemented and successfully applied to 3D CT angiography (CTA) applications for vessel segmentation and bone removal. The method consists of three steps: segmenting one-dimensional regions of each row; doing region merging to adjacent rows to obtain the region segmentation of each slice; and doing region merging to adjacent slices to obtain the final region segmentation of 3D images. To improve the segmentation speed for very large volume 3D CTA images, this algorithm is applied repeatedly to newly updated local cubes. The next new cube can be estimated by checking isolated segmented regions on all 6 faces of the current local cube. This local non-recursive 3D region-growing algorithm is memory-efficient and computation-efficient. Clinical testings of this algorithm on Brain CTA show this technique could effectively remove whole skull, most of the bones on the skull base, and reveal the cerebral vascular structures clearly.

Sequential addition of short DNA oligos in DNA-polymerase-based synthesis reactions

DOEpatents

Gardner, Shea N; Mariella, Jr., Raymond P; Christian, Allen T; Young, Jennifer A; Clague, David S

2013-06-25

A method of preselecting a multiplicity of DNA sequence segments that will comprise the DNA molecule of user-defined sequence, separating the DNA sequence segments temporally, and combining the multiplicity of DNA sequence segments with at least one polymerase enzyme wherein the multiplicity of DNA sequence segments join to produce the DNA molecule of user-defined sequence. Sequence segments may be of length n, where n is an odd integer. In one embodiment the length of desired hybridizing overlap is specified by the user and the sequences and the protocol for combining them are guided by computational (bioinformatics) predictions. In one embodiment sequence segments are combined from multiple reading frames to span the same region of a sequence, so that multiple desired hybridizations may occur with different overlap lengths.

Airborne Infrared and Visible Image Fusion Combined with Region Segmentation

PubMed Central

Zuo, Yujia; Liu, Jinghong; Bai, Guanbing; Wang, Xuan; Sun, Mingchao

2017-01-01

This paper proposes an infrared (IR) and visible image fusion method introducing region segmentation into the dual-tree complex wavelet transform (DTCWT) region. This method should effectively improve both the target indication and scene spectrum features of fusion images, and the target identification and tracking reliability of fusion system, on an airborne photoelectric platform. The method involves segmenting the region in an IR image by significance, and identifying the target region and the background region; then, fusing the low-frequency components in the DTCWT region according to the region segmentation result. For high-frequency components, the region weights need to be assigned by the information richness of region details to conduct fusion based on both weights and adaptive phases, and then introducing a shrinkage function to suppress noise; Finally, the fused low-frequency and high-frequency components are reconstructed to obtain the fusion image. The experimental results show that the proposed method can fully extract complementary information from the source images to obtain a fusion image with good target indication and rich information on scene details. They also give a fusion result superior to existing popular fusion methods, based on eithers subjective or objective evaluation. With good stability and high fusion accuracy, this method can meet the fusion requirements of IR-visible image fusion systems. PMID:28505137

Airborne Infrared and Visible Image Fusion Combined with Region Segmentation.

PubMed

Zuo, Yujia; Liu, Jinghong; Bai, Guanbing; Wang, Xuan; Sun, Mingchao

2017-05-15

This paper proposes an infrared (IR) and visible image fusion method introducing region segmentation into the dual-tree complex wavelet transform (DTCWT) region. This method should effectively improve both the target indication and scene spectrum features of fusion images, and the target identification and tracking reliability of fusion system, on an airborne photoelectric platform. The method involves segmenting the region in an IR image by significance, and identifying the target region and the background region; then, fusing the low-frequency components in the DTCWT region according to the region segmentation result. For high-frequency components, the region weights need to be assigned by the information richness of region details to conduct fusion based on both weights and adaptive phases, and then introducing a shrinkage function to suppress noise; Finally, the fused low-frequency and high-frequency components are reconstructed to obtain the fusion image. The experimental results show that the proposed method can fully extract complementary information from the source images to obtain a fusion image with good target indication and rich information on scene details. They also give a fusion result superior to existing popular fusion methods, based on eithers subjective or objective evaluation. With good stability and high fusion accuracy, this method can meet the fusion requirements of IR-visible image fusion systems.

Experimental comparison of landmark-based methods for 3D elastic registration of pre- and postoperative liver CT data

NASA Astrophysics Data System (ADS)

Lange, Thomas; Wörz, Stefan; Rohr, Karl; Schlag, Peter M.

2009-02-01

The qualitative and quantitative comparison of pre- and postoperative image data is an important possibility to validate surgical procedures, in particular, if computer assisted planning and/or navigation is performed. Due to deformations after surgery, partially caused by the removal of tissue, a non-rigid registration scheme is a prerequisite for a precise comparison. Interactive landmark-based schemes are a suitable approach, if high accuracy and reliability is difficult to achieve by automatic registration approaches. Incorporation of a priori knowledge about the anatomical structures to be registered may help to reduce interaction time and improve accuracy. Concerning pre- and postoperative CT data of oncological liver resections the intrahepatic vessels are suitable anatomical structures. In addition to using branching landmarks for registration, we here introduce quasi landmarks at vessel segments with high localization precision perpendicular to the vessels and low precision along the vessels. A comparison of interpolating thin-plate splines (TPS), interpolating Gaussian elastic body splines (GEBS) and approximating GEBS on landmarks at vessel branchings as well as approximating GEBS on the introduced vessel segment landmarks is performed. It turns out that the segment landmarks provide registration accuracies as good as branching landmarks and can improve accuracy if combined with branching landmarks. For a low number of landmarks segment landmarks are even superior.

Testicular Lumicrine Factors Regulate ERK, STAT, and NFKB Pathways in the Initial Segment of the Rat Epididymis to Prevent Apoptosis1

PubMed Central

Xu, Bingfang; Abdel-Fattah, Rana; Yang, Ling; Crenshaw, Sallie A.; Black, Michael B.; Hinton, Barry T.

2011-01-01

The initial segment of the epididymis is vital for male fertility; therefore, it is important to understand the mechanisms that regulate this important region. Deprival of testicular luminal fluid factors/lumicrine factors from the epididymis results in a wave of apoptosis in the initial segment. In this study, a combination of protein array and microarray analyses was used to examine the early changes in downstream signal transduction pathways following loss of lumicrine factors. We discovered the following cascade of events leading to the loss of protection and eventual apoptosis: in the first 6 h after loss of lumicrine factors, down-regulation of the ERK pathway components was observed at the mRNA expression and protein activity levels. Microarray analysis revealed that mRNA levels of several key components of the ERK pathway, Dusp6, Dusp5, and Etv5, decreased sharply, while the analysis from the protein array revealed a decline in the activities of MAP2K1/2 and MAPK1. Immunostaining of phospho-MAPK3/1 indicated that down-regulation of the ERK pathway was specific to the epithelial cells of the initial segment. Subsequently, after 12 h of loss of lumicrine factors, levels of mRNA expression of STAT and NFKB pathway components increased, mRNA levels of several genes encoding cell cycle inhibitors increased, and levels of protein expression of several proapoptotic phosphatases increased. Finally, after 18 h of loss of protection from lumicrine factors, apoptosis was observed. In conclusion, testicular lumicrine factors protect the cells of the initial segment by activating the ERK pathway, repressing STAT and NFKB pathways, and thereby preventing apoptosis. PMID:21311037

A novel multiphoton microscopy images segmentation method based on superpixel and watershed.

PubMed

Wu, Weilin; Lin, Jinyong; Wang, Shu; Li, Yan; Liu, Mingyu; Liu, Gaoqiang; Cai, Jianyong; Chen, Guannan; Chen, Rong

2017-04-01

Multiphoton microscopy (MPM) imaging technique based on two-photon excited fluorescence (TPEF) and second harmonic generation (SHG) shows fantastic performance for biological imaging. The automatic segmentation of cellular architectural properties for biomedical diagnosis based on MPM images is still a challenging issue. A novel multiphoton microscopy images segmentation method based on superpixels and watershed (MSW) is presented here to provide good segmentation results for MPM images. The proposed method uses SLIC superpixels instead of pixels to analyze MPM images for the first time. The superpixels segmentation based on a new distance metric combined with spatial, CIE Lab color space and phase congruency features, divides the images into patches which keep the details of the cell boundaries. Then the superpixels are used to reconstruct new images by defining an average value of superpixels as image pixels intensity level. Finally, the marker-controlled watershed is utilized to segment the cell boundaries from the reconstructed images. Experimental results show that cellular boundaries can be extracted from MPM images by MSW with higher accuracy and robustness. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

An evaluation of the signature extension approach to large area crop inventories utilizing space image data. [Kansas and North Dakota

NASA Technical Reports Server (NTRS)

Nalepka, R. F. (Principal Investigator); Cicone, R. C.; Stinson, J. L.; Balon, R. J.

1977-01-01

The author has identified the following significant results. Two examples of haze correction algorithms were tested: CROP-A and XSTAR. The CROP-A was tested in a unitemporal mode on data collected in 1973-74 over ten sample segments in Kansas. Because of the uniformly low level of haze present in these segments, no conclusion could be reached about CROP-A's ability to compensate for haze. It was noted, however, that in some cases CROP-A made serious errors which actually degraded classification performance. The haze correction algorithm XSTAR was tested in a multitemporal mode on 1975-76 LACIE sample segment data over 23 blind sites in Kansas and 18 sample segments in North Dakota, providing wide range of haze levels and other conditions for algorithm evaluation. It was found that this algorithm substantially improved signature extension classification accuracy when a sum-of-likelihoods classifier was used with an alien rejection threshold.

Analyses of Synteny Between Arabidopsis thaliana and Species in the Asteraceae Reveal a Complex Network of Small Syntenic Segments and Major Chromosomal Rearrangements

PubMed Central

Timms, Lee; Jimenez, Rosmery; Chase, Mike; Lavelle, Dean; McHale, Leah; Kozik, Alexander; Lai, Zhao; Heesacker, Adam; Knapp, Steven; Rieseberg, Loren; Michelmore, Richard; Kesseli, Rick

2006-01-01

Comparative genomic studies among highly divergent species have been problematic because reduced gene similarities make orthologous gene pairs difficult to identify and because colinearity is expected to be low with greater time since divergence from the last common ancestor. Nevertheless, synteny between divergent taxa in several lineages has been detected over short chromosomal segments. We have examined the level of synteny between the model species Arabidopsis thaliana and species in the Compositae, one of the largest and most diverse plant families. While macrosyntenic patterns covering large segments of the chromosomes are not evident, significant levels of local synteny are detected at a fine scale covering segments of 1-Mb regions of A. thaliana and regions of <5 cM in lettuce and sunflower. These syntenic patches are often not colinear, however, and form a network of regions that have likely evolved by duplications followed by differential gene loss. PMID:16783026

An Active Contour Model Based on Adaptive Threshold for Extraction of Cerebral Vascular Structures.

PubMed

Wang, Jiaxin; Zhao, Shifeng; Liu, Zifeng; Tian, Yun; Duan, Fuqing; Pan, Yutong

2016-01-01

Cerebral vessel segmentation is essential and helpful for the clinical diagnosis and the related research. However, automatic segmentation of brain vessels remains challenging because of the variable vessel shape and high complex of vessel geometry. This study proposes a new active contour model (ACM) implemented by the level-set method for segmenting vessels from TOF-MRA data. The energy function of the new model, combining both region intensity and boundary information, is composed of two region terms, one boundary term and one penalty term. The global threshold representing the lower gray boundary of the target object by maximum intensity projection (MIP) is defined in the first-region term, and it is used to guide the segmentation of the thick vessels. In the second term, a dynamic intensity threshold is employed to extract the tiny vessels. The boundary term is used to drive the contours to evolve towards the boundaries with high gradients. The penalty term is used to avoid reinitialization of the level-set function. Experimental results on 10 clinical brain data sets demonstrate that our method is not only able to achieve better Dice Similarity Coefficient than the global threshold based method and localized hybrid level-set method but also able to extract whole cerebral vessel trees, including the thin vessels.

Aorta and pulmonary artery segmentation using optimal surface graph cuts in non-contrast CT

NASA Astrophysics Data System (ADS)

Sedghi Gamechi, Zahra; Arias-Lorza, Andres M.; Pedersen, Jesper Holst; de Bruijne, Marleen

2018-03-01

Accurate measurements of the size and shape of the aorta and pulmonary arteries are important as risk factors for cardiovascular diseases, and for Chronicle Obstacle Pulmonary Disease (COPD).1 The aim of this paper is to propose an automated method for segmenting the aorta and pulmonary arteries in low-dose non-ECGgated non-contrast CT scans. Low contrast and the high noise level make the automatic segmentation in such images a challenging task. In the proposed method, first, a minimum cost path tracking algorithm traces the centerline between user-defined seed points. The cost function is based on a multi-directional medialness filter and a lumen intensity similarity metric. The vessel radius is also estimated from the medialness filter. The extracted centerlines are then smoothed and dilated non-uniformly according to the extracted local vessel radius and subsequently used as initialization for a graph-cut segmentation. The algorithm is evaluated on 225 low-dose non-ECG-gated non-contrast CT scans from a lung cancer screening trial. Quantitatively analyzing 25 scans with full manual annotations, we obtain a dice overlap of 0.94+/-0.01 for the aorta and 0.92+/-0.01 for pulmonary arteries. Qualitative validation by visual inspection on 200 scans shows successful segmentation in 93% of all cases for the aorta and 94% for pulmonary arteries.

Segmental spinal anaesthesia for cholecystectomy in a patient with severe lung disease.

PubMed

van Zundert, A A J; Stultiens, G; Jakimowicz, J J; van den Borne, B E E M; van der Ham, W G J M; Wildsmith, J A W

2006-04-01

Occasionally patients awaiting heart or lung transplant because of terminal disease require other types of surgery, but present significant challenges to the anaesthetist because of impaired organ function. Regional anaesthesia may have much to offer such patients and we here report one who underwent successfully a laparoscopic cholecystectomy under segmental subarachnoid (spinal) anaesthesia performed at the low thoracic level. The anatomical and physiological consequences of such a technique are discussed.

Segmental thoracic spinal anesthesia in patient with Byssinosis undergoing nephrectomy.

PubMed

Patel, Kiran; Salgaonkar, Sweta

2012-01-01

Byssinosis is an occupational disease occurring commonly in cotton mill workers; it usually presents with features of chronic obstructive pulmonary disease (COPD). The management of patients with COPD presents a significant challenges to the anesthetist. Regional anesthesia is preferred in most of these patients to avoid perioperative and postoperative complications related to general anesthesia. We report a known case of Byssinosis who underwent nephrectomy under segmental spinal anesthesia at the low thoracic level.

Segmentation quality evaluation using region-based precision and recall measures for remote sensing images

NASA Astrophysics Data System (ADS)

Zhang, Xueliang; Feng, Xuezhi; Xiao, Pengfeng; He, Guangjun; Zhu, Liujun

2015-04-01

Segmentation of remote sensing images is a critical step in geographic object-based image analysis. Evaluating the performance of segmentation algorithms is essential to identify effective segmentation methods and optimize their parameters. In this study, we propose region-based precision and recall measures and use them to compare two image partitions for the purpose of evaluating segmentation quality. The two measures are calculated based on region overlapping and presented as a point or a curve in a precision-recall space, which can indicate segmentation quality in both geometric and arithmetic respects. Furthermore, the precision and recall measures are combined by using four different methods. We examine and compare the effectiveness of the combined indicators through geometric illustration, in an effort to reveal segmentation quality clearly and capture the trade-off between the two measures. In the experiments, we adopted the multiresolution segmentation (MRS) method for evaluation. The proposed measures are compared with four existing discrepancy measures to further confirm their capabilities. Finally, we suggest using a combination of the region-based precision-recall curve and the F-measure for supervised segmentation evaluation.

The analysis of Stability reliability of Qian Tang River seawall

NASA Astrophysics Data System (ADS)

Wu, Xue-Xiong

2017-11-01

Qiantang River seawall due to high water soaking pond by foreshore scour, encountered during the low tide prone slope overall instability. Considering the seawall beach scour in front of random change, using the simplified Bishop method, combined with the variability of soil mechanics parameters, calculation and analysis of Qiantang River Xiasha seawall segments of the overall stability.

Comparison of an adaptive local thresholding method on CBCT and µCT endodontic images

NASA Astrophysics Data System (ADS)

Michetti, Jérôme; Basarab, Adrian; Diemer, Franck; Kouame, Denis

2018-01-01

Root canal segmentation on cone beam computed tomography (CBCT) images is difficult because of the noise level, resolution limitations, beam hardening and dental morphological variations. An image processing framework, based on an adaptive local threshold method, was evaluated on CBCT images acquired on extracted teeth. A comparison with high quality segmented endodontic images on micro computed tomography (µCT) images acquired from the same teeth was carried out using a dedicated registration process. Each segmented tooth was evaluated according to volume and root canal sections through the area and the Feret’s diameter. The proposed method is shown to overcome the limitations of CBCT and to provide an automated and adaptive complete endodontic segmentation. Despite a slight underestimation (-4, 08%), the local threshold segmentation method based on edge-detection was shown to be fast and accurate. Strong correlations between CBCT and µCT segmentations were found both for the root canal area and diameter (respectively 0.98 and 0.88). Our findings suggest that combining CBCT imaging with this image processing framework may benefit experimental endodontology, teaching and could represent a first development step towards the clinical use of endodontic CBCT segmentation during pulp cavity treatment.

Automatic Segmentation of High-Throughput RNAi Fluorescent Cellular Images

PubMed Central

Yan, Pingkum; Zhou, Xiaobo; Shah, Mubarak; Wong, Stephen T. C.

2010-01-01

High-throughput genome-wide RNA interference (RNAi) screening is emerging as an essential tool to assist biologists in understanding complex cellular processes. The large number of images produced in each study make manual analysis intractable; hence, automatic cellular image analysis becomes an urgent need, where segmentation is the first and one of the most important steps. In this paper, a fully automatic method for segmentation of cells from genome-wide RNAi screening images is proposed. Nuclei are first extracted from the DNA channel by using a modified watershed algorithm. Cells are then extracted by modeling the interaction between them as well as combining both gradient and region information in the Actin and Rac channels. A new energy functional is formulated based on a novel interaction model for segmenting tightly clustered cells with significant intensity variance and specific phenotypes. The energy functional is minimized by using a multiphase level set method, which leads to a highly effective cell segmentation method. Promising experimental results demonstrate that automatic segmentation of high-throughput genome-wide multichannel screening can be achieved by using the proposed method, which may also be extended to other multichannel image segmentation problems. PMID:18270043

Novel multimodality segmentation using level sets and Jensen-Rényi divergence

DOE Office of Scientific and Technical Information (OSTI.GOV)

Markel, Daniel, E-mail: daniel.markel@mail.mcgill.ca; Zaidi, Habib; Geneva Neuroscience Center, Geneva University, CH-1205 Geneva

2013-12-15

Purpose: Positron emission tomography (PET) is playing an increasing role in radiotherapy treatment planning. However, despite progress, robust algorithms for PET and multimodal image segmentation are still lacking, especially if the algorithm were extended to image-guided and adaptive radiotherapy (IGART). This work presents a novel multimodality segmentation algorithm using the Jensen-Rényi divergence (JRD) to evolve the geometric level set contour. The algorithm offers improved noise tolerance which is particularly applicable to segmentation of regions found in PET and cone-beam computed tomography. Methods: A steepest gradient ascent optimization method is used in conjunction with the JRD and a level set activemore » contour to iteratively evolve a contour to partition an image based on statistical divergence of the intensity histograms. The algorithm is evaluated using PET scans of pharyngolaryngeal squamous cell carcinoma with the corresponding histological reference. The multimodality extension of the algorithm is evaluated using 22 PET/CT scans of patients with lung carcinoma and a physical phantom scanned under varying image quality conditions. Results: The average concordance index (CI) of the JRD segmentation of the PET images was 0.56 with an average classification error of 65%. The segmentation of the lung carcinoma images had a maximum diameter relative error of 63%, 19.5%, and 14.8% when using CT, PET, and combined PET/CT images, respectively. The estimated maximal diameters of the gross tumor volume (GTV) showed a high correlation with the macroscopically determined maximal diameters, with aR{sup 2} value of 0.85 and 0.88 using the PET and PET/CT images, respectively. Results from the physical phantom show that the JRD is more robust to image noise compared to mutual information and region growing. Conclusions: The JRD has shown improved noise tolerance compared to mutual information for the purpose of PET image segmentation. Presented is a flexible framework for multimodal image segmentation that can incorporate a large number of inputs efficiently for IGART.« less

Novel multimodality segmentation using level sets and Jensen-Rényi divergence.

PubMed

Markel, Daniel; Zaidi, Habib; El Naqa, Issam

2013-12-01

Positron emission tomography (PET) is playing an increasing role in radiotherapy treatment planning. However, despite progress, robust algorithms for PET and multimodal image segmentation are still lacking, especially if the algorithm were extended to image-guided and adaptive radiotherapy (IGART). This work presents a novel multimodality segmentation algorithm using the Jensen-Rényi divergence (JRD) to evolve the geometric level set contour. The algorithm offers improved noise tolerance which is particularly applicable to segmentation of regions found in PET and cone-beam computed tomography. A steepest gradient ascent optimization method is used in conjunction with the JRD and a level set active contour to iteratively evolve a contour to partition an image based on statistical divergence of the intensity histograms. The algorithm is evaluated using PET scans of pharyngolaryngeal squamous cell carcinoma with the corresponding histological reference. The multimodality extension of the algorithm is evaluated using 22 PET/CT scans of patients with lung carcinoma and a physical phantom scanned under varying image quality conditions. The average concordance index (CI) of the JRD segmentation of the PET images was 0.56 with an average classification error of 65%. The segmentation of the lung carcinoma images had a maximum diameter relative error of 63%, 19.5%, and 14.8% when using CT, PET, and combined PET/CT images, respectively. The estimated maximal diameters of the gross tumor volume (GTV) showed a high correlation with the macroscopically determined maximal diameters, with a R(2) value of 0.85 and 0.88 using the PET and PET/CT images, respectively. Results from the physical phantom show that the JRD is more robust to image noise compared to mutual information and region growing. The JRD has shown improved noise tolerance compared to mutual information for the purpose of PET image segmentation. Presented is a flexible framework for multimodal image segmentation that can incorporate a large number of inputs efficiently for IGART.

Hybrid Corpectomy and Disc Arthroplasty for Cervical Spondylotic Myelopathy Caused by Ossification of Posterior Longitudinal Ligament and Disc Herniation.

PubMed

Chang, Huang-Chou; Tu, Tsung-Hsi; Chang, Hsuan-Kan; Wu, Jau-Ching; Fay, Li-Yu; Chang, Peng-Yuan; Wu, Ching-Lan; Huang, Wen-Cheng; Cheng, Henrich

2016-11-01

The combination of anterior cervical discectomy and fusion (ACDF) and anterior cervical corpectomy and fusion (ACCF) has been demonstrated to be effective for multilevel cervical spondylotic myelopathy (CSM); however, the combination of ACCF and cervical disc arthroplasty (CDA) for 3-level CSM has never been addressed. Consecutive patients (>18 years of age) with CSM caused by segmental ossification of posterior longitudinal ligament (OPLL) and degenerative disc disease (DDD) were reviewed. Inclusion criteria were patients who underwent hybrid ACCF and CDA surgery for symptomatic 3-level CSM with OPLL and DDD. Medical and radiologic records were reviewed retrospectively. A total of 15 patients were analyzed with a mean follow-up of 18.1 ± 7.42 months. Every patient had hybrid surgery composed of 1-level ACCF (for segmental-type OPLL causing spinal stenosis) and 1-level CDA at the adjacent level (for DDD causing stenosis). All clinical outcomes, including visual analogue scale of neck and arm pain, Neck Disability Index, Japanese Orthopedic Association scores, and Nurick scores of myelopathy, demonstrated significant improvement at 12 months after surgery. All patients (100%) achieved arthrodesis for the ACCF (instrumented) and preserved mobility for CDA (preoperation 6.2 ± 3.81° vs. postoperation 7.0 ± 4.18°; P = 0.579). For patients with multilevel CSM caused by segmental OPLL and DDD, the hybrid surgery of ACCF and CDA demonstrated satisfactory clinical and radiologic outcomes. Moreover, although located next to each other, the instrumented ACCF construct and CDA still achieved solid arthrodesis and preserved mobility, respectively. Therefore, hybrid surgery may be a reasonable option for the management of CSM with OPLL. Copyright © 2016 Elsevier Inc. All rights reserved.

Random walks with shape prior for cochlea segmentation in ex vivo μCT.

PubMed

Ruiz Pujadas, Esmeralda; Kjer, Hans Martin; Piella, Gemma; Ceresa, Mario; González Ballester, Miguel Angel

2016-09-01

Cochlear implantation is a safe and effective surgical procedure to restore hearing in deaf patients. However, the level of restoration achieved may vary due to differences in anatomy, implant type and surgical access. In order to reduce the variability of the surgical outcomes, we previously proposed the use of a high-resolution model built from [Formula: see text] images and then adapted to patient-specific clinical CT scans. As the accuracy of the model is dependent on the precision of the original segmentation, it is extremely important to have accurate [Formula: see text] segmentation algorithms. We propose a new framework for cochlea segmentation in ex vivo [Formula: see text] images using random walks where a distance-based shape prior is combined with a region term estimated by a Gaussian mixture model. The prior is also weighted by a confidence map to adjust its influence according to the strength of the image contour. Random walks is performed iteratively, and the prior mask is aligned in every iteration. We tested the proposed approach in ten [Formula: see text] data sets and compared it with other random walks-based segmentation techniques such as guided random walks (Eslami et al. in Med Image Anal 17(2):236-253, 2013) and constrained random walks (Li et al. in Advances in image and video technology. Springer, Berlin, pp 215-226, 2012). Our approach demonstrated higher accuracy results due to the probability density model constituted by the region term and shape prior information weighed by a confidence map. The weighted combination of the distance-based shape prior with a region term into random walks provides accurate segmentations of the cochlea. The experiments suggest that the proposed approach is robust for cochlea segmentation.

Chicago transit authority train noise exposure.

PubMed

Phan, Linh T; Jones, Rachael M

2017-06-01

To characterize noise exposure of riders on Chicago Transit Authority (CTA) trains, we measured noise levels twice on each segment of 7 of the 8 CTA train lines, which are named after colors, yielding 48 time-series measurements. We found the Blue Line has the highest noise levels compared to other train lines, with mean 76.9 dBA; and that the maximum noise level, 88.9 dBA occurred in the tunnel between the Chicago and Grand stations. Train segments involving travel through a tunnel had significantly higher noise levels than segments with travel on elevated and ground level tracks. While 8-hr doses inside the passenger cars were not estimated to exceed occupational exposure limits, train operators ride in a separate cab with operational windows and may therefore have higher noise exposures than riders. Despite the low risk of hearing loss for riders on CTA trains, in part because transit noise accounts for a small part of total daily noise exposure, 1-min average noise levels exceeded 85 dBA at times. This confirms anecdotal observations of discomfort due to noise levels, and indicates a need for noise management, particularly in tunnels.

A new medical image segmentation model based on fractional order differentiation and level set

NASA Astrophysics Data System (ADS)

Chen, Bo; Huang, Shan; Xie, Feifei; Li, Lihong; Chen, Wensheng; Liang, Zhengrong

2018-03-01

Segmenting medical images is still a challenging task for both traditional local and global methods because the image intensity inhomogeneous. In this paper, two contributions are made: (i) on the one hand, a new hybrid model is proposed for medical image segmentation, which is built based on fractional order differentiation, level set description and curve evolution; and (ii) on the other hand, three popular definitions of Fourier-domain, Grünwald-Letnikov (G-L) and Riemann-Liouville (R-L) fractional order differentiation are investigated and compared through experimental results. Because of the merits of enhancing high frequency features of images and preserving low frequency features of images in a nonlinear manner by the fractional order differentiation definitions, one fractional order differentiation definition is used in our hybrid model to perform segmentation of inhomogeneous images. The proposed hybrid model also integrates fractional order differentiation, fractional order gradient magnitude and difference image information. The widely-used dice similarity coefficient metric is employed to evaluate quantitatively the segmentation results. Firstly, experimental results demonstrated that a slight difference exists among the three expressions of Fourier-domain, G-L, RL fractional order differentiation. This outcome supports our selection of one of the three definitions in our hybrid model. Secondly, further experiments were performed for comparison between our hybrid segmentation model and other existing segmentation models. A noticeable gain was seen by our hybrid model in segmenting intensity inhomogeneous images.

Whole vertebral bone segmentation method with a statistical intensity-shape model based approach

NASA Astrophysics Data System (ADS)

Hanaoka, Shouhei; Fritscher, Karl; Schuler, Benedikt; Masutani, Yoshitaka; Hayashi, Naoto; Ohtomo, Kuni; Schubert, Rainer

2011-03-01

An automatic segmentation algorithm for the vertebrae in human body CT images is presented. Especially we focused on constructing and utilizing 4 different statistical intensity-shape combined models for the cervical, upper / lower thoracic and lumbar vertebrae, respectively. For this purpose, two previously reported methods were combined: a deformable model-based initial segmentation method and a statistical shape-intensity model-based precise segmentation method. The former is used as a pre-processing to detect the position and orientation of each vertebra, which determines the initial condition for the latter precise segmentation method. The precise segmentation method needs prior knowledge on both the intensities and the shapes of the objects. After PCA analysis of such shape-intensity expressions obtained from training image sets, vertebrae were parametrically modeled as a linear combination of the principal component vectors. The segmentation of each target vertebra was performed as fitting of this parametric model to the target image by maximum a posteriori estimation, combined with the geodesic active contour method. In the experimental result by using 10 cases, the initial segmentation was successful in 6 cases and only partially failed in 4 cases (2 in the cervical area and 2 in the lumbo-sacral). In the precise segmentation, the mean error distances were 2.078, 1.416, 0.777, 0.939 mm for cervical, upper and lower thoracic, lumbar spines, respectively. In conclusion, our automatic segmentation algorithm for the vertebrae in human body CT images showed a fair performance for cervical, thoracic and lumbar vertebrae.

Segmentation of epidermal tissue with histopathological damage in images of haematoxylin and eosin stained human skin

PubMed Central

2014-01-01

Background Digital image analysis has the potential to address issues surrounding traditional histological techniques including a lack of objectivity and high variability, through the application of quantitative analysis. A key initial step in image analysis is the identification of regions of interest. A widely applied methodology is that of segmentation. This paper proposes the application of image analysis techniques to segment skin tissue with varying degrees of histopathological damage. The segmentation of human tissue is challenging as a consequence of the complexity of the tissue structures and inconsistencies in tissue preparation, hence there is a need for a new robust method with the capability to handle the additional challenges materialising from histopathological damage. Methods A new algorithm has been developed which combines enhanced colour information, created following a transformation to the L*a*b* colourspace, with general image intensity information. A colour normalisation step is included to enhance the algorithm’s robustness to variations in the lighting and staining of the input images. The resulting optimised image is subjected to thresholding and the segmentation is fine-tuned using a combination of morphological processing and object classification rules. The segmentation algorithm was tested on 40 digital images of haematoxylin & eosin (H&E) stained skin biopsies. Accuracy, sensitivity and specificity of the algorithmic procedure were assessed through the comparison of the proposed methodology against manual methods. Results Experimental results show the proposed fully automated methodology segments the epidermis with a mean specificity of 97.7%, a mean sensitivity of 89.4% and a mean accuracy of 96.5%. When a simple user interaction step is included, the specificity increases to 98.0%, the sensitivity to 91.0% and the accuracy to 96.8%. The algorithm segments effectively for different severities of tissue damage. Conclusions Epidermal segmentation is a crucial first step in a range of applications including melanoma detection and the assessment of histopathological damage in skin. The proposed methodology is able to segment the epidermis with different levels of histological damage. The basic method framework could be applied to segmentation of other epithelial tissues. PMID:24521154

Myocardial scar segmentation from magnetic resonance images using convolutional neural network

NASA Astrophysics Data System (ADS)

Zabihollahy, Fatemeh; White, James A.; Ukwatta, Eranga

2018-02-01

Accurate segmentation of the myocardial fibrosis or scar may provide important advancements for the prediction and management of malignant ventricular arrhythmias in patients with cardiovascular disease. In this paper, we propose a semi-automated method for segmentation of myocardial scar from late gadolinium enhancement magnetic resonance image (LGE-MRI) using a convolutional neural network (CNN). In contrast to image intensitybased methods, CNN-based algorithms have the potential to improve the accuracy of scar segmentation through the creation of high-level features from a combination of convolutional, detection and pooling layers. Our developed algorithm was trained using 2,336,703 image patches extracted from 420 slices of five 3D LGE-MR datasets, then validated on 2,204,178 patches from a testing dataset of seven 3D LGE-MR images including 624 slices, all obtained from patients with chronic myocardial infarction. For evaluation of the algorithm, we compared the algorithmgenerated segmentations to manual delineations by experts. Our CNN-based method reported an average Dice similarity coefficient (DSC), precision, and recall of 94.50 +/- 3.62%, 96.08 +/- 3.10%, and 93.96 +/- 3.75% as the accuracy of segmentation, respectively. As compared to several intensity threshold-based methods for scar segmentation, the results of our developed method have a greater agreement with manual expert segmentation.

CT volumetry of the skeletal tissues

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brindle, James M.; Alexandre Trindade, A.; Pichardo, Jose C.

2006-10-15

Computed tomography (CT) is an important and widely used modality in the diagnosis and treatment of various cancers. In the field of molecular radiotherapy, the use of spongiosa volume (combined tissues of the bone marrow and bone trabeculae) has been suggested as a means to improve the patient-specificity of bone marrow dose estimates. The noninvasive estimation of an organ volume comes with some degree of error or variation from the true organ volume. The present study explores the ability to obtain estimates of spongiosa volume or its surrogate via manual image segmentation. The variation among different segmentation raters was exploredmore » and found not to be statistically significant (p value >0.05). Accuracy was assessed by having several raters manually segment a polyvinyl chloride (PVC) pipe with known volumes. Segmentation of the outer region of the PVC pipe resulted in mean percent errors as great as 15% while segmentation of the pipe's inner region resulted in mean percent errors within {approx}5%. Differences between volumes estimated with the high-resolution CT data set (typical of ex vivo skeletal scans) and the low-resolution CT data set (typical of in vivo skeletal scans) were also explored using both patient CT images and a PVC pipe phantom. While a statistically significant difference (p value <0.002) between the high-resolution and low-resolution data sets was observed with excised femoral heads obtained following total hip arthroplasty, the mean difference between high-resolution and low-resolution data sets was found to be only 1.24 and 2.18 cm{sup 3} for spongiosa and cortical bone, respectively. With respect to differences observed with the PVC pipe, the variation between the high-resolution and low-resolution mean percent errors was a high as {approx}20% for the outer region volume estimates and only as high as {approx}6% for the inner region volume estimates. The findings from this study suggest that manual segmentation is a reasonably accurate and reliable means for the in vivo estimation of spongiosa volume. This work also provides a foundation for future studies where spongiosa volumes are estimated by various raters in more comprehensive CT data sets.« less

Lagged segmented Poincaré plot analysis for risk stratification in patients with dilated cardiomyopathy.

PubMed

Voss, Andreas; Fischer, Claudia; Schroeder, Rico; Figulla, Hans R; Goernig, Matthias

2012-07-01

The objectives of this study were to introduce a new type of heart-rate variability analysis improving risk stratification in patients with idiopathic dilated cardiomyopathy (DCM) and to provide additional information about impaired heart beat generation in these patients. Beat-to-beat intervals (BBI) of 30-min ECGs recorded from 91 DCM patients and 21 healthy subjects were analyzed applying the lagged segmented Poincaré plot analysis (LSPPA) method. LSPPA includes the Poincaré plot reconstruction with lags of 1-100, rotating the cloud of points, its normalized segmentation adapted to their standard deviations, and finally, a frequency-dependent clustering. The lags were combined into eight different clusters representing specific frequency bands within 0.012-1.153 Hz. Statistical differences between low- and high-risk DCM could be found within the clusters II-VIII (e.g., cluster IV: 0.033-0.038 Hz; p = 0.0002; sensitivity = 85.7 %; specificity = 71.4 %). The multivariate statistics led to a sensitivity of 92.9 %, specificity of 85.7 % and an area under the curve of 92.1 % discriminating these patient groups. We introduced the LSPPA method to investigate time correlations in BBI time series. We found that LSPPA contributes considerably to risk stratification in DCM and yields the highest discriminant power in the low and very low-frequency bands.

Low-dose adenosine stress echocardiography: Detection of myocardial viability

PubMed Central

Djordjevic-Dikic, Ana; Ostojic, Miodrag; Beleslin, Branko; Nedeljkovic, Ivana; Stepanovic, Jelena; Stojkovic, Sinisa; Petrasinovic, Zorica; Nedeljkovic, Milan; Saponjski, Jovica; Giga, Vojislav

2003-01-01

Objective The aim of this study was to evaluate the diagnostic potential of low-dose adenosine stress echocardiography in detection of myocardial viability. Background Vasodilation through low dose dipyridamole infusion may recruit contractile reserve by increasing coronary flow or by increasing levels of endogenous adenosine. Methods Forty-three patients with resting dyssynergy, due to previous myocardial infarction, underwent low-dose adenosine (80, 100, 110 mcg/kg/min in 3 minutes intervals) echocardiography test. Gold standard for myocardial viability was improvement in systolic thickening of dyssinergic segments of ≥ 1 grade at follow-up. Coronary angiography was done in 41 pts. Twenty-seven patients were revascularized and 16 were medically treated. Echocardiographic follow up data (12 ± 2 months) were available in 24 revascularized patients. Results Wall motion score index improved from rest 1.55 ± 0.30 to 1.33 ± 0.26 at low-dose adenosine (p < 0.001). Of the 257 segments with baseline dyssynergy, adenosine echocardiography identified 122 segments as positive for viability, and 135 as necrotic since no improvement of systolic thickening was observed. Follow-up wall motion score index was 1.31 ± 0.30 (p < 0.001 vs. rest). The sensitivity of adenosine echo test for identification of viable segments was 87%, while specificity was 95%, and diagnostic accuracy 90%. Positive and negative predictive values were 97% and 80%, respectively. Conclusion Low-dose adenosine stress echocardiography test has high diagnostic potential for detection of myocardial viability in the group of patients with left ventricle dysfunction due to previous myocardial infarction. Low dose adenosine stress echocardiography may be adequate alternative to low-dose dobutamine test for evaluation of myocardial viability. PMID:12812523

JANUS - A setup for low-energy Coulomb excitation at ReA3

NASA Astrophysics Data System (ADS)

Lunderberg, E.; Belarge, J.; Bender, P. C.; Bucher, B.; Cline, D.; Elman, B.; Gade, A.; Liddick, S. N.; Longfellow, B.; Prokop, C.; Weisshaar, D.; Wu, C. Y.

2018-03-01

A new experimental setup for low-energy Coulomb excitation experiments was constructed in a collaboration between the National Superconducting Cyclotron Laboratory (NSCL), Lawrence Livermore National Laboratory (LLNL), and the University of Rochester and was commissioned at the general purpose beam line of NSCL's ReA3 reaccelerator facility. The so-called JANUS setup combines γ-ray detection with the Segmented Ge Array (SeGA) and scattered particle detection using a pair of segmented double-sided Si detectors (Bambino 2). The low-energy Coulomb excitation program that JANUS enables will complement intermediate-energy Coulomb excitation studies that have long been performed at NSCL by providing access to observables that quantify collectivity beyond the first excited state, including the sign and magnitude of excited-state quadrupole moments. In this work, the setup and its performance will be described based on the commissioning run that used stable 78Kr impinging onto a 1.09 mg/cm2208Pb target at a beam energy of 3.9 MeV/u.

Multisegment nanowire sensors for the detection of DNA molecules.

PubMed

Wang, Xu; Ozkan, Cengiz S

2008-02-01

We describe a novel application for detecting specific single strand DNA sequences using multisegment nanowires via a straightforward surface functionalization method. Nanowires comprising CdTe-Au-CdTe segments are fabricated using electrochemical deposition, and electrical characterization indicates a p-type behavior for the multisegment nanostructures, in a back-to-back Schottky diode configuration. Such nanostructures modified with thiol-terminated probe DNA fragments could function as high fidelity sensors for biomolecules at very low concentration. The gold segment is utilized for functionalization and binding of single strand DNA (ssDNA) fragments while the CdTe segments at both ends serve to modulate the equilibrium Fermi level of the heterojunction device upon hybridization of the complementary DNA fragments (cDNA) to the ssDNA over the Au segment. Employing such multisegment nanowires could lead to the fabrication more sophisticated and high multispecificity biosensors via selective functionalization of individual segments for biowarfare sensing and medical diagnostics applications.

Segmental thoracic spinal anesthesia in patient with Byssinosis undergoing nephrectomy

PubMed Central

Patel, Kiran; Salgaonkar, Sweta

2012-01-01

Byssinosis is an occupational disease occurring commonly in cotton mill workers; it usually presents with features of chronic obstructive pulmonary disease (COPD). The management of patients with COPD presents a significant challenges to the anesthetist. Regional anesthesia is preferred in most of these patients to avoid perioperative and postoperative complications related to general anesthesia. We report a known case of Byssinosis who underwent nephrectomy under segmental spinal anesthesia at the low thoracic level. PMID:25885628

An Approach for Reducing the Error Rate in Automated Lung Segmentation

PubMed Central

Gill, Gurman; Beichel, Reinhard R.

2016-01-01

Robust lung segmentation is challenging, especially when tens of thousands of lung CT scans need to be processed, as required by large multi-center studies. The goal of this work was to develop and assess a method for the fusion of segmentation results from two different methods to generate lung segmentations that have a lower failure rate than individual input segmentations. As basis for the fusion approach, lung segmentations generated with a region growing and model-based approach were utilized. The fusion result was generated by comparing input segmentations and selectively combining them using a trained classification system. The method was evaluated on a diverse set of 204 CT scans of normal and diseased lungs. The fusion approach resulted in a Dice coefficient of 0.9855 ± 0.0106 and showed a statistically significant improvement compared to both input segmentation methods. In addition, the failure rate at different segmentation accuracy levels was assessed. For example, when requiring that lung segmentations must have a Dice coefficient of better than 0.97, the fusion approach had a failure rate of 6.13%. In contrast, the failure rate for region growing and model-based methods was 18.14% and 15.69%, respectively. Therefore, the proposed method improves the quality of the lung segmentations, which is important for subsequent quantitative analysis of lungs. Also, to enable a comparison with other methods, results on the LOLA11 challenge test set are reported. PMID:27447897

Segmentation methodology for automated classification and differentiation of soft tissues in multiband images of high-resolution ultrasonic transmission tomography.

PubMed

Jeong, Jeong-Won; Shin, Dae C; Do, Synho; Marmarelis, Vasilis Z

2006-08-01

This paper presents a novel segmentation methodology for automated classification and differentiation of soft tissues using multiband data obtained with the newly developed system of high-resolution ultrasonic transmission tomography (HUTT) for imaging biological organs. This methodology extends and combines two existing approaches: the L-level set active contour (AC) segmentation approach and the agglomerative hierarchical kappa-means approach for unsupervised clustering (UC). To prevent the trapping of the current iterative minimization AC algorithm in a local minimum, we introduce a multiresolution approach that applies the level set functions at successively increasing resolutions of the image data. The resulting AC clusters are subsequently rearranged by the UC algorithm that seeks the optimal set of clusters yielding the minimum within-cluster distances in the feature space. The presented results from Monte Carlo simulations and experimental animal-tissue data demonstrate that the proposed methodology outperforms other existing methods without depending on heuristic parameters and provides a reliable means for soft tissue differentiation in HUTT images.

Integrated β-catenin, BMP, PTEN, and Notch signalling patterns the nephron.

PubMed

Lindström, Nils O; Lawrence, Melanie L; Burn, Sally F; Johansson, Jeanette A; Bakker, Elvira R M; Ridgway, Rachel A; Chang, C-Hong; Karolak, Michele J; Oxburgh, Leif; Headon, Denis J; Sansom, Owen J; Smits, Ron; Davies, Jamie A; Hohenstein, Peter

2015-02-03

The different segments of the nephron and glomerulus in the kidney balance the processes of water homeostasis, solute recovery, blood filtration, and metabolite excretion. When segment function is disrupted, a range of pathological features are presented. Little is known about nephron patterning during embryogenesis. In this study, we demonstrate that the early nephron is patterned by a gradient in β-catenin activity along the axis of the nephron tubule. By modifying β-catenin activity, we force cells within nephrons to differentiate according to the imposed β-catenin activity level, thereby causing spatial shifts in nephron segments. The β-catenin signalling gradient interacts with the BMP pathway which, through PTEN/PI3K/AKT signalling, antagonises β-catenin activity and promotes segment identities associated with low β-catenin activity. β-catenin activity and PI3K signalling also integrate with Notch signalling to control segmentation: modulating β-catenin activity or PI3K rescues segment identities normally lost by inhibition of Notch. Our data therefore identifies a molecular network for nephron patterning.

Multichannel loudness compensation method based on segmented sound pressure level for digital hearing aids

NASA Astrophysics Data System (ADS)

Liang, Ruiyu; Xi, Ji; Bao, Yongqiang

2017-07-01

To improve the performance of gain compensation based on three-segment sound pressure level (SPL) in hearing aids, an improved multichannel loudness compensation method based on eight-segment SPL was proposed. Firstly, the uniform cosine modulated filter bank was designed. Then, the adjacent channels which have low or gradual slopes were adaptively merged to obtain the corresponding non-uniform cosine modulated filter according to the audiogram of hearing impaired persons. Secondly, the input speech was decomposed into sub-band signals and the SPL of every sub-band signal was computed. Meanwhile, the audible SPL range from 0 dB SPL to 120 dB SPL was equally divided into eight segments. Based on these segments, a different prescription formula was designed to compute more detailed gain to compensate according to the audiogram and the computed SPL. Finally, the enhanced signal was synthesized. Objective experiments showed the decomposed signals after cosine modulated filter bank have little distortion. Objective experiments showed that the hearing aids speech perception index (HASPI) and hearing aids speech quality index (HASQI) increased 0.083 and 0.082 on average, respectively. Subjective experiments showed the proposed algorithm can effectively improve the speech recognition of six hearing impaired persons.

Active Segmentation.

PubMed

Mishra, Ajay; Aloimonos, Yiannis

2009-01-01

The human visual system observes and understands a scene/image by making a series of fixations. Every fixation point lies inside a particular region of arbitrary shape and size in the scene which can either be an object or just a part of it. We define as a basic segmentation problem the task of segmenting that region containing the fixation point. Segmenting the region containing the fixation is equivalent to finding the enclosing contour- a connected set of boundary edge fragments in the edge map of the scene - around the fixation. This enclosing contour should be a depth boundary.We present here a novel algorithm that finds this bounding contour and achieves the segmentation of one object, given the fixation. The proposed segmentation framework combines monocular cues (color/intensity/texture) with stereo and/or motion, in a cue independent manner. The semantic robots of the immediate future will be able to use this algorithm to automatically find objects in any environment. The capability of automatically segmenting objects in their visual field can bring the visual processing to the next level. Our approach is different from current approaches. While existing work attempts to segment the whole scene at once into many areas, we segment only one image region, specifically the one containing the fixation point. Experiments with real imagery collected by our active robot and from the known databases 1 demonstrate the promise of the approach.

The use of atlas registration and graph cuts for prostate segmentation in magnetic resonance images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Korsager, Anne Sofie, E-mail: asko@hst.aau.dk; Østergaard, Lasse Riis; Fortunati, Valerio

2015-04-15

Purpose: An automatic method for 3D prostate segmentation in magnetic resonance (MR) images is presented for planning image-guided radiotherapy treatment of prostate cancer. Methods: A spatial prior based on intersubject atlas registration is combined with organ-specific intensity information in a graph cut segmentation framework. The segmentation is tested on 67 axial T{sub 2}-weighted MR images in a leave-one-out cross validation experiment and compared with both manual reference segmentations and with multiatlas-based segmentations using majority voting atlas fusion. The impact of atlas selection is investigated in both the traditional atlas-based segmentation and the new graph cut method that combines atlas andmore » intensity information in order to improve the segmentation accuracy. Best results were achieved using the method that combines intensity information, shape information, and atlas selection in the graph cut framework. Results: A mean Dice similarity coefficient (DSC) of 0.88 and a mean surface distance (MSD) of 1.45 mm with respect to the manual delineation were achieved. Conclusions: This approaches the interobserver DSC of 0.90 and interobserver MSD 0f 1.15 mm and is comparable to other studies performing prostate segmentation in MR.« less

Statistical optimisation techniques in fatigue signal editing problem

NASA Astrophysics Data System (ADS)

Nopiah, Z. M.; Osman, M. H.; Baharin, N.; Abdullah, S.

2015-02-01

Success in fatigue signal editing is determined by the level of length reduction without compromising statistical constraints. A great reduction rate can be achieved by removing small amplitude cycles from the recorded signal. The long recorded signal sometimes renders the cycle-to-cycle editing process daunting. This has encouraged researchers to focus on the segment-based approach. This paper discusses joint application of the Running Damage Extraction (RDE) technique and single constrained Genetic Algorithm (GA) in fatigue signal editing optimisation.. In the first section, the RDE technique is used to restructure and summarise the fatigue strain. This technique combines the overlapping window and fatigue strain-life models. It is designed to identify and isolate the fatigue events that exist in the variable amplitude strain data into different segments whereby the retention of statistical parameters and the vibration energy are considered. In the second section, the fatigue data editing problem is formulated as a constrained single optimisation problem that can be solved using GA method. The GA produces the shortest edited fatigue signal by selecting appropriate segments from a pool of labelling segments. Challenges arise due to constraints on the segment selection by deviation level over three signal properties, namely cumulative fatigue damage, root mean square and kurtosis values. Experimental results over several case studies show that the idea of solving fatigue signal editing within a framework of optimisation is effective and automatic, and that the GA is robust for constrained segment selection.

Statistical optimisation techniques in fatigue signal editing problem

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nopiah, Z. M.; Osman, M. H.; Baharin, N.

Success in fatigue signal editing is determined by the level of length reduction without compromising statistical constraints. A great reduction rate can be achieved by removing small amplitude cycles from the recorded signal. The long recorded signal sometimes renders the cycle-to-cycle editing process daunting. This has encouraged researchers to focus on the segment-based approach. This paper discusses joint application of the Running Damage Extraction (RDE) technique and single constrained Genetic Algorithm (GA) in fatigue signal editing optimisation.. In the first section, the RDE technique is used to restructure and summarise the fatigue strain. This technique combines the overlapping window andmore » fatigue strain-life models. It is designed to identify and isolate the fatigue events that exist in the variable amplitude strain data into different segments whereby the retention of statistical parameters and the vibration energy are considered. In the second section, the fatigue data editing problem is formulated as a constrained single optimisation problem that can be solved using GA method. The GA produces the shortest edited fatigue signal by selecting appropriate segments from a pool of labelling segments. Challenges arise due to constraints on the segment selection by deviation level over three signal properties, namely cumulative fatigue damage, root mean square and kurtosis values. Experimental results over several case studies show that the idea of solving fatigue signal editing within a framework of optimisation is effective and automatic, and that the GA is robust for constrained segment selection.« less

Segmentation and Characterization of Chewing Bouts by Monitoring Temporalis Muscle Using Smart Glasses With Piezoelectric Sensor.

PubMed

Farooq, Muhammad; Sazonov, Edward

2017-11-01

Several methods have been proposed for automatic and objective monitoring of food intake, but their performance suffers in the presence of speech and motion artifacts. This paper presents a novel sensor system and algorithms for detection and characterization of chewing bouts from a piezoelectric strain sensor placed on the temporalis muscle. The proposed data acquisition device was incorporated into the temple of eyeglasses. The system was tested by ten participants in two part experiments, one under controlled laboratory conditions and the other in unrestricted free-living. The proposed food intake recognition method first performed an energy-based segmentation to isolate candidate chewing segments (instead of using epochs of fixed duration commonly reported in research literature), with the subsequent classification of the segments by linear support vector machine models. On participant level (combining data from both laboratory and free-living experiments), with ten-fold leave-one-out cross-validation, chewing were recognized with average F-score of 96.28% and the resultant area under the curve was 0.97, which are higher than any of the previously reported results. A multivariate regression model was used to estimate chew counts from segments classified as chewing with an average mean absolute error of 3.83% on participant level. These results suggest that the proposed system is able to identify chewing segments in the presence of speech and motion artifacts, as well as automatically and accurately quantify chewing behavior, both under controlled laboratory conditions and unrestricted free-living.

Image segmentation by hierarchial agglomeration of polygons using ecological statistics

DOEpatents

Prasad, Lakshman; Swaminarayan, Sriram

2013-04-23

A method for rapid hierarchical image segmentation based on perceptually driven contour completion and scene statistics is disclosed. The method begins with an initial fine-scale segmentation of an image, such as obtained by perceptual completion of partial contours into polygonal regions using region-contour correspondences established by Delaunay triangulation of edge pixels as implemented in VISTA. The resulting polygons are analyzed with respect to their size and color/intensity distributions and the structural properties of their boundaries. Statistical estimates of granularity of size, similarity of color, texture, and saliency of intervening boundaries are computed and formulated into logical (Boolean) predicates. The combined satisfiability of these Boolean predicates by a pair of adjacent polygons at a given segmentation level qualifies them for merging into a larger polygon representing a coarser, larger-scale feature of the pixel image and collectively obtains the next level of polygonal segments in a hierarchy of fine-to-coarse segmentations. The iterative application of this process precipitates textured regions as polygons with highly convolved boundaries and helps distinguish them from objects which typically have more regular boundaries. The method yields a multiscale decomposition of an image into constituent features that enjoy a hierarchical relationship with features at finer and coarser scales. This provides a traversable graph structure from which feature content and context in terms of other features can be derived, aiding in automated image understanding tasks. The method disclosed is highly efficient and can be used to decompose and analyze large images.

In vitro propagation and cryopreservation of Aerides odorata Lour. (Orchidaceae).

PubMed

Hongthongkham, J; Bunnag, S

2014-05-01

An efficient method for in vitro propagation and cryopreservation of Aerides odorata was established. Leaf segments were cultured on New Dogashima (ND) mediums supplemented with various concentrations of Benzyladenine (BA) (0-5 mg L(-1)) combined with Naphthaleneacetic Acid (NAA) (0-2 mg L(-1)). The optimal treatment for inducing Protocorm-like Bodies (PLBs) from leaf segments was obtained from the combination of 1 or 3 mg L(-1) BA and 0.5 or 1 mg L(-1) NAA; whereas, the addition of BA or NAA alone induced shoot and/or root initiation rather than PLB or callus formation. Shoots rapidly developed on ND mediums containing 5 mg L(-1) BA. Cryopreservation of leaf segment-derived PLBs was successful using the encapsulation-dehydration method. The maximum survival percentage of Cryopreserved (Cryp) PLBs was achieved by encapsulating PLBs with 2% Na-alginate combined with 2 M glycerol and 0.4 M sucrose. The encapsulated PLBs were then precultured in 0.75 M sucrose for 24 h and dehydrated for 6 h before plunging into liquid nitrogen. Genetic stability of Cryp PLBs after regrowth was assessed by flow cytometry. The findings showed no different patterns of ploidy levels and morphology between Cryp and non-cryopreserved (Ncryp) control plantlets.

An efficient global energy optimization approach for robust 3D plane segmentation of point clouds

NASA Astrophysics Data System (ADS)

Dong, Zhen; Yang, Bisheng; Hu, Pingbo; Scherer, Sebastian

2018-03-01

Automatic 3D plane segmentation is necessary for many applications including point cloud registration, building information model (BIM) reconstruction, simultaneous localization and mapping (SLAM), and point cloud compression. However, most of the existing 3D plane segmentation methods still suffer from low precision and recall, and inaccurate and incomplete boundaries, especially for low-quality point clouds collected by RGB-D sensors. To overcome these challenges, this paper formulates the plane segmentation problem as a global energy optimization because it is robust to high levels of noise and clutter. First, the proposed method divides the raw point cloud into multiscale supervoxels, and considers planar supervoxels and individual points corresponding to nonplanar supervoxels as basic units. Then, an efficient hybrid region growing algorithm is utilized to generate initial plane set by incrementally merging adjacent basic units with similar features. Next, the initial plane set is further enriched and refined in a mutually reinforcing manner under the framework of global energy optimization. Finally, the performances of the proposed method are evaluated with respect to six metrics (i.e., plane precision, plane recall, under-segmentation rate, over-segmentation rate, boundary precision, and boundary recall) on two benchmark datasets. Comprehensive experiments demonstrate that the proposed method obtained good performances both in high-quality TLS point clouds (i.e., http://SEMANTIC3D.NET)

Association of ST segment depression >5 min after exercise testing with severity of coronary artery disease.

PubMed

Shaikh, Ayaz Hussain; Hanif, Bashir; Siddiqui, Adeel M; Shahab, Hunaina; Qazi, Hammad Ali; Mujtaba, Iqbal

2010-04-01

To determine the association of prolonged ST segment depression after an exercise test with severity of coronary artery disease. A cross sectional study of 100 consecutive patients referred to the cardiology laboratory for stress myocardial perfusion imaging (MPI) conducted between April-August 2008. All selected patients were monitored until their ST segment depression was recovered to baseline. ST segment recovery time was categorized into less and more than 5 minutes. Subsequent gated SPECT-MPI was performed and stratified according to severity of perfusion defect. Association was determined between post exercise ST segment depression recovery time (<5 minutes and >5 minutes) and severity of perfusion defect on MPI. The mean age of the patients was 57.12 +/- 9.0 years. The results showed statistically insignificant association (p > 0.05) between ST segment recovery time of <5 minutes and >5 minutes with low, intermediate or high risk MPI. Our findings suggest that the commonly used cut-off levels used in literature for prolonged, post exercise ST segment depression (>5 minutes into recovery phase) does not correlate with severity of ischaemia based on MPI results.

Assessment of the Effect of Swedish Massage and Acupressure in Rehabilitation of Patients with Low Back Pain. Preliminary Report.

PubMed

Boguszewski, Dariusz; Krupiński, Mateusz; Białoszewski, Dariusz

2017-12-30

Low-back pain is a common problem in developed societies. The quest for methods to reduce this com-plaint may contribute to improving the quality of life for many people. The aim of the study was to compare the effect of Swedish massage combined with acupressure vs. Swedish massage alone in patients with low back pain. The study involved 20 women and 20 men with lumbosacral pain. The group was clinically ho-mo-geneous. The participants were randomized into two groups: Group 1, which received Swedish massage with acu-pressure techniques, and Group 2, treated with Swedish massage only. The research tools comprised the Laitinen Pain Score, the International Physical Activity Questionnaire, the Roland-Morris Ques-tion-naire, the Thomayer test, and the measurement of lumbar spine extension. Differences between the mea-surements were evaluated with the Wilcoxon test, with the minimum significance level set at p≤0.05. Both groups demonstrated a significant (p<0.05) decrease in pain intensity, improvement in quality of life and increase in physical activity. Increased segmental mobility of the spine was also observed in all patients, with significant changes (p<0.05) noted only in Group 1. In Group 2, the differences tended towards significance. In selected cases, Swedish massage combined with acupressure techniques may be more effective as a mo-notherapy in patients with non-specific low back pain than massage alone.

Interactive vs. automatic ultrasound image segmentation methods for staging hepatic lipidosis.

PubMed

Weijers, Gert; Starke, Alexander; Haudum, Alois; Thijssen, Johan M; Rehage, Jürgen; De Korte, Chris L

2010-07-01

The aim of this study was to test the hypothesis that automatic segmentation of vessels in ultrasound (US) images can produce similar or better results in grading fatty livers than interactive segmentation. A study was performed in postpartum dairy cows (N=151), as an animal model of human fatty liver disease, to test this hypothesis. Five transcutaneous and five intraoperative US liver images were acquired in each animal and a liverbiopsy was taken. In liver tissue samples, triacylglycerol (TAG) was measured by biochemical analysis and hepatic diseases other than hepatic lipidosis were excluded by histopathologic examination. Ultrasonic tissue characterization (UTC) parameters--Mean echo level, standard deviation (SD) of echo level, signal-to-noise ratio (SNR), residual attenuation coefficient (ResAtt) and axial and lateral speckle size--were derived using a computer-aided US (CAUS) protocol and software package. First, the liver tissue was interactively segmented by two observers. With increasing fat content, fewer hepatic vessels were visible in the ultrasound images and, therefore, a smaller proportion of the liver needed to be excluded from these images. Automatic-segmentation algorithms were implemented and it was investigated whether better results could be achieved than with the subjective and time-consuming interactive-segmentation procedure. The automatic-segmentation algorithms were based on both fixed and adaptive thresholding techniques in combination with a 'speckle'-shaped moving-window exclusion technique. All data were analyzed with and without postprocessing as contained in CAUS and with different automated-segmentation techniques. This enabled us to study the effect of the applied postprocessing steps on single and multiple linear regressions ofthe various UTC parameters with TAG. Improved correlations for all US parameters were found by using automatic-segmentation techniques. Stepwise multiple linear-regression formulas where derived and used to predict TAG level in the liver. Receiver-operating-characteristics (ROC) analysis was applied to assess the performance and area under the curve (AUC) of predicting TAG and to compare the sensitivity and specificity of the methods. Best speckle-size estimates and overall performance (R2 = 0.71, AUC = 0.94) were achieved by using an SNR-based adaptive automatic-segmentation method (used TAG threshold: 50 mg/g liver wet weight). Automatic segmentation is thus feasible and profitable.

A New Vegetation Segmentation Approach for Cropped Fields Based on Threshold Detection from Hue Histograms

PubMed Central

Hassanein, Mohamed; El-Sheimy, Naser

2018-01-01

Over the last decade, the use of unmanned aerial vehicle (UAV) technology has evolved significantly in different applications as it provides a special platform capable of combining the benefits of terrestrial and aerial remote sensing. Therefore, such technology has been established as an important source of data collection for different precision agriculture (PA) applications such as crop health monitoring and weed management. Generally, these PA applications depend on performing a vegetation segmentation process as an initial step, which aims to detect the vegetation objects in collected agriculture fields’ images. The main result of the vegetation segmentation process is a binary image, where vegetations are presented in white color and the remaining objects are presented in black. Such process could easily be performed using different vegetation indexes derived from multispectral imagery. Recently, to expand the use of UAV imagery systems for PA applications, it was important to reduce the cost of such systems through using low-cost RGB cameras Thus, developing vegetation segmentation techniques for RGB images is a challenging problem. The proposed paper introduces a new vegetation segmentation methodology for low-cost UAV RGB images, which depends on using Hue color channel. The proposed methodology follows the assumption that the colors in any agriculture field image can be distributed into vegetation and non-vegetations colors. Therefore, four main steps are developed to detect five different threshold values using the hue histogram of the RGB image, these thresholds are capable to discriminate the dominant color, either vegetation or non-vegetation, within the agriculture field image. The achieved results for implementing the proposed methodology showed its ability to generate accurate and stable vegetation segmentation performance with mean accuracy equal to 87.29% and standard deviation as 12.5%. PMID:29670055

Low-dose levobupivacaine plus fentanyl combination for spinal anesthesia in anorectal surgery.

PubMed

Honca, Mehtap; Dereli, Necla; Kose, Emine Arzu; Honca, Tevfik; Kutuk, Selcen; Unal, Selma Savas; Horasanli, Eyup

2015-01-01

the aim of this study was to investigate the effects of spinal anesthesia using two different doses of fentanyl combined with low-dose levobupivacaine in anorectal surgery. in this prospective, double-blind study, 52 American Society of Anaesthesiologists I-II patients scheduled for elective anorectal surgery were randomized into two groups. The patients in group I received intrathecal 2.5mg hyperbaric levobupivacaine plus 12.5 μg fentanyl and in group II received intrathecal 2.5mg hyperbaric levobupivacaine plus 25 μg fentanyl. All the patients remained in the seated position for 5 min after completion of the spinal anesthesia. Sensory block was evaluated with pin-prick test and motor block was evaluated with a modified Bromage scale. motor block was not observed in both of the groups. The sensory block was limited to the S2 level in group I, and S1 level in group II. None of the patients required additional analgesics during the operation. Time to two-segment regression was shorter in group I compared with group II (p<0.01). One patient in group I and 5 patients in group II had pruritus. Hemodynamic parameters were stable during the operation in both of the groups. spinal saddle block using hyperbaric levobupivacaine with both 12.5 μg and 25 μg fentanyl provided good quality of anesthesia without motor block for anorectal surgery in the prone position. Copyright © 2014 Sociedade Brasileira de Anestesiologia. Published by Elsevier Editora Ltda. All rights reserved.

[Low-dose levobupivacaine plus fentanyl combination for spinal anesthesia in anorectal surgery].

PubMed

Honca, Mehtap; Dereli, Necla; Kose, Emine Arzu; Honca, Tevfik; Kutuk, Selcen; Unal, Selma Savas; Horasanli, Eyup

2015-01-01

The aim of this study was to investigate the effects of spinal anesthesia using two different doses of fentanyl combined with low-dose levobupivacaine in anorectal surgery. In this prospective, double-blind study, 52 American Society of Anaesthesiologists I-II patients scheduled for elective anorectal surgery were randomized into two groups. The patients in group I received intrathecal 2.5mg hyperbaric levobupivacaine plus 12.5μg fentanyl and in group II received intrathecal 2.5mg hyperbaric levobupivacaine plus 25μg fentanyl. All the patients remained in the seated position for 5min after completion of the spinal anesthesia. Sensory block was evaluated with pin-prick test and motor block was evaluated with a modified Bromage scale. Motor block was not observed in both of the groups. The sensory block was limited to the S2 level in group I, and S1 level in group II. None of the patients required additional analgesics during the operation. Time to two-segment regression was shorter in group I compared with group II (p<0.01). One patient in group I and 5 patients in group II had pruritus. Hemodynamic parameters were stable during the operation in both of the groups. Spinal saddle block using hyperbaric levobupivacaine with both 12.5μg and 25μg fentanyl provided good quality of anesthesia without motor block for anorectal surgery in the prone position. Copyright © 2014 Sociedade Brasileira de Anestesiologia. Publicado por Elsevier Editora Ltda. All rights reserved.

Influence of low temperature preincubation on somatic embryogenesis and ethylene emanation from orchardgrass leaves

NASA Technical Reports Server (NTRS)

Tomaszewski, Z. Jr; Kuklin, A. I.; Sams, C. E.; Conger, B. V.

1994-01-01

The objectives of this study were to determine the effects of low temperature (4 degrees C) preincubation on somatic embryogenesis from orchardgrass (Dactylis glomerata L.) leaf cultures and to relate these effects to ethylene emanation during the preincubation and incubation periods. Experiments were also conducted with an ethylene biosynthesis inhibitor aminooxyacetic acid (AOA). Segments from the innermost two leaves were cultured on SH medium with 30 micromoles dicamba at 4 degrees C for 1 to 7 d before transfer to 21 degrees C. Results from a paired design showed that the embryogenic response of leaf segments preincubated at 4 degrees C was equal or superior to nonpreincubated leaves at all time periods. Ethylene emanation was decreased during the low temperature incubation. Transfer of leaf segments from 4 degrees C to 21 degrees C was accompanied by a burst of ethylene which rose to control levels within 30 min. AOA at 20 and 40 micromoles decreased ethylene emanation but did not stimulate the embryogenic response. We conclude that the stimulation of somatic embryogenesis by low temperature is probably due to factors other than suppression of ethylene biosynthesis.

A study of crown development mechanisms using a shoot-based tree model and segmented terrestrial laser scanning data.

PubMed

Sievänen, Risto; Raumonen, Pasi; Perttunen, Jari; Nikinmaa, Eero; Kaitaniemi, Pekka

2018-05-24

Functional-structural plant models (FSPMs) allow simulation of tree crown development as the sum of modular (e.g. shoot-level) responses triggered by the local environmental conditions. The actual process of space filling by the crowns can be studied. Although the FSPM simulations are at organ scale, the data for their validation have usually been at more aggregated levels (whole-crown or whole-tree). Measurements made by terrestrial laser scanning (TLS) that have been segmented into elementary units (internodes) offer a phenotyping tool to validate the FSPM predictions at levels comparable with their detail. We demonstrate the testing of different formulations of crown development of Scots pine trees in the LIGNUM model using segmented TLS data. We made TLS measurements from four sample trees growing in a forest on a relatively poor soil from sapling size to mature stage. The TLS data were segmented into internodes. The segmentation also produced information on whether needles were present in the internode. We applied different formulations of crown development (flushing of buds and length of growth of new internodes) in LIGNUM. We optimized the parameter values of each formulation using genetic algorithms to observe the best fit of LIGNUM simulations to the measured trees. The fitness function in the estimation combined both tree-level characteristics (e.g. tree height and crown length) and measures of crown shape (e.g. spatial distribution of needle area). Comparison of different formulations against the data indicates that the Extended Borchert-Honda model for shoot elongation works best within LIGNUM. Control of growth by local density in the crown was important for all shoot elongation formulations. Modifying the number of lateral buds as a function of local density in the crown was the best way to accomplish density control. It was demonstrated how segmented TLS data can be used in the context of a shoot-based model to select model components.

Multiple Hypotheses Image Segmentation and Classification With Application to Dietary Assessment

PubMed Central

Zhu, Fengqing; Bosch, Marc; Khanna, Nitin; Boushey, Carol J.; Delp, Edward J.

2016-01-01

We propose a method for dietary assessment to automatically identify and locate food in a variety of images captured during controlled and natural eating events. Two concepts are combined to achieve this: a set of segmented objects can be partitioned into perceptually similar object classes based on global and local features; and perceptually similar object classes can be used to assess the accuracy of image segmentation. These ideas are implemented by generating multiple segmentations of an image to select stable segmentations based on the classifier’s confidence score assigned to each segmented image region. Automatic segmented regions are classified using a multichannel feature classification system. For each segmented region, multiple feature spaces are formed. Feature vectors in each of the feature spaces are individually classified. The final decision is obtained by combining class decisions from individual feature spaces using decision rules. We show improved accuracy of segmenting food images with classifier feedback. PMID:25561457

Multiple hypotheses image segmentation and classification with application to dietary assessment.

PubMed

Zhu, Fengqing; Bosch, Marc; Khanna, Nitin; Boushey, Carol J; Delp, Edward J

2015-01-01

We propose a method for dietary assessment to automatically identify and locate food in a variety of images captured during controlled and natural eating events. Two concepts are combined to achieve this: a set of segmented objects can be partitioned into perceptually similar object classes based on global and local features; and perceptually similar object classes can be used to assess the accuracy of image segmentation. These ideas are implemented by generating multiple segmentations of an image to select stable segmentations based on the classifier's confidence score assigned to each segmented image region. Automatic segmented regions are classified using a multichannel feature classification system. For each segmented region, multiple feature spaces are formed. Feature vectors in each of the feature spaces are individually classified. The final decision is obtained by combining class decisions from individual feature spaces using decision rules. We show improved accuracy of segmenting food images with classifier feedback.

Combining watershed and graph cuts methods to segment organs at risk in radiotherapy

NASA Astrophysics Data System (ADS)

Dolz, Jose; Kirisli, Hortense A.; Viard, Romain; Massoptier, Laurent

2014-03-01

Computer-aided segmentation of anatomical structures in medical images is a valuable tool for efficient radiation therapy planning (RTP). As delineation errors highly affect the radiation oncology treatment, it is crucial to delineate geometric structures accurately. In this paper, a semi-automatic segmentation approach for computed tomography (CT) images, based on watershed and graph-cuts methods, is presented. The watershed pre-segmentation groups small areas of similar intensities in homogeneous labels, which are subsequently used as input for the graph-cuts algorithm. This methodology does not require of prior knowledge of the structure to be segmented; even so, it performs well with complex shapes and low intensity. The presented method also allows the user to add foreground and background strokes in any of the three standard orthogonal views - axial, sagittal or coronal - making the interaction with the algorithm easy and fast. Hence, the segmentation information is propagated within the whole volume, providing a spatially coherent result. The proposed algorithm has been evaluated using 9 CT volumes, by comparing its segmentation performance over several organs - lungs, liver, spleen, heart and aorta - to those of manual delineation from experts. A Dicés coefficient higher than 0.89 was achieved in every case. That demonstrates that the proposed approach works well for all the anatomical structures analyzed. Due to the quality of the results, the introduction of the proposed approach in the RTP process will be a helpful tool for organs at risk (OARs) segmentation.

Swallow segmentation with artificial neural networks and multi-sensor fusion.

PubMed

Lee, Joon; Steele, Catriona M; Chau, Tom

2009-11-01

Swallow segmentation is a critical precursory step to the analysis of swallowing signal characteristics. In an effort to automatically segment swallows, we investigated artificial neural networks (ANN) with information from cervical dual-axis accelerometry, submental MMG, and nasal airflow. Our objectives were (1) to investigate the relationship between segmentation performance and the number of signal sources and (2) to identify the signals or signal combinations most useful for swallow segmentation. Signals were acquired from 17 healthy adults in both discrete and continuous swallowing tasks using five stimuli. Training and test feature vectors were constructed with variances from single or multiple signals, estimated within 200 ms moving windows with 50% overlap. Corresponding binary target labels (swallow or non-swallow) were derived by manual segmentation. A separate 3-layer ANN was trained for each participant-signal combination, and all possible signal combinations were investigated. As more signal sources were included, segmentation performance improved in terms of sensitivity, specificity, accuracy, and adjusted accuracy. The combination of all four signal sources achieved the highest mean accuracy and adjusted accuracy of 88.5% and 89.6%, respectively. A-P accelerometry proved to be the most discriminatory source, while the inclusion of MMG or nasal airflow resulted in the least performance improvement. These findings suggest that an ANN, multi-sensor fusion approach to segmentation is worthy of further investigation in swallowing studies.

Segmentation precedes face categorization under suboptimal conditions.

PubMed

Van Den Boomen, Carlijn; Fahrenfort, Johannes J; Snijders, Tineke M; Kemner, Chantal

2015-01-01

Both categorization and segmentation processes play a crucial role in face perception. However, the functional relation between these subprocesses is currently unclear. The present study investigates the temporal relation between segmentation-related and category-selective responses in the brain, using electroencephalography (EEG). Surface segmentation and category content were both manipulated using texture-defined objects, including faces. This allowed us to study brain activity related to segmentation and to categorization. In the main experiment, participants viewed texture-defined objects for a duration of 800 ms. EEG results revealed that segmentation-related responses precede category-selective responses. Three additional experiments revealed that the presence and timing of categorization depends on stimulus properties and presentation duration. Photographic objects were presented for a long and short (92 ms) duration and evoked fast category-selective responses in both cases. On the other hand, presentation of texture-defined objects for a short duration only evoked segmentation-related but no category-selective responses. Category-selective responses were much slower when evoked by texture-defined than by photographic objects. We suggest that in case of categorization of objects under suboptimal conditions, such as when low-level stimulus properties are not sufficient for fast object categorization, segmentation facilitates the slower categorization process.

Segmentation precedes face categorization under suboptimal conditions

PubMed Central

Van Den Boomen, Carlijn; Fahrenfort, Johannes J.; Snijders, Tineke M.; Kemner, Chantal

2015-01-01

Both categorization and segmentation processes play a crucial role in face perception. However, the functional relation between these subprocesses is currently unclear. The present study investigates the temporal relation between segmentation-related and category-selective responses in the brain, using electroencephalography (EEG). Surface segmentation and category content were both manipulated using texture-defined objects, including faces. This allowed us to study brain activity related to segmentation and to categorization. In the main experiment, participants viewed texture-defined objects for a duration of 800 ms. EEG results revealed that segmentation-related responses precede category-selective responses. Three additional experiments revealed that the presence and timing of categorization depends on stimulus properties and presentation duration. Photographic objects were presented for a long and short (92 ms) duration and evoked fast category-selective responses in both cases. On the other hand, presentation of texture-defined objects for a short duration only evoked segmentation-related but no category-selective responses. Category-selective responses were much slower when evoked by texture-defined than by photographic objects. We suggest that in case of categorization of objects under suboptimal conditions, such as when low-level stimulus properties are not sufficient for fast object categorization, segmentation facilitates the slower categorization process. PMID:26074838

Multi-atlas and label fusion approach for patient-specific MRI based skull estimation.

PubMed

Torrado-Carvajal, Angel; Herraiz, Joaquin L; Hernandez-Tamames, Juan A; San Jose-Estepar, Raul; Eryaman, Yigitcan; Rozenholc, Yves; Adalsteinsson, Elfar; Wald, Lawrence L; Malpica, Norberto

2016-04-01

MRI-based skull segmentation is a useful procedure for many imaging applications. This study describes a methodology for automatic segmentation of the complete skull from a single T1-weighted volume. The skull is estimated using a multi-atlas segmentation approach. Using a whole head computed tomography (CT) scan database, the skull in a new MRI volume is detected by nonrigid image registration of the volume to every CT, and combination of the individual segmentations by label-fusion. We have compared Majority Voting, Simultaneous Truth and Performance Level Estimation (STAPLE), Shape Based Averaging (SBA), and the Selective and Iterative Method for Performance Level Estimation (SIMPLE) algorithms. The pipeline has been evaluated quantitatively using images from the Retrospective Image Registration Evaluation database (reaching an overlap of 72.46 ± 6.99%), a clinical CT-MR dataset (maximum overlap of 78.31 ± 6.97%), and a whole head CT-MRI pair (maximum overlap 78.68%). A qualitative evaluation has also been performed on MRI acquisition of volunteers. It is possible to automatically segment the complete skull from MRI data using a multi-atlas and label fusion approach. This will allow the creation of complete MRI-based tissue models that can be used in electromagnetic dosimetry applications and attenuation correction in PET/MR. © 2015 Wiley Periodicals, Inc.

Evaluation of therapeutic effect of low dose naltrexone in experimentally-induced Crohn's disease in rats.

PubMed

Tawfik, Dina Ibrahim; Osman, Afaf Sayed; Tolba, Hedayat Mahmoud; Khattab, Aida; Abdel-Salam, Lubna O; Kamel, Mahmoud M

2016-10-01

Crohn's disease is a relapsing inflammatory condition afflicting the digestive tract. Drugs used for treatment of Crohn's disease may be associated with serious side effects. Endogenous opioid peptides modulate inflammatory cytokine production. Opioid antagonists have been shown to play a role in healing and repair of tissues. This work was designed to detect the possible beneficial effects of opioid antagonist naltrexone in indomethacin-induced Crohn's disease in rats. Enteritis was induced in male albino rats by two subcutaneous injection of indomethacin in a dose of 7.5mg/kg 24h apart started on day one. Salfasalazine, naltrexone and their combination were administered orally from day one of induction of enteritis to day 10. Disease activity index, serum levels of C-reactive protein and tumor necrosis factor-α, macroscopic and microscopic pathological scores and in vitro motility studies were evaluated. Induction of enteritis resulted in significant increase of disease activity index, significant elevation of serum levels of C-reactive protein and tumor necrosis factor-α, significant deterioration of pathological scores and significant increase in the mean contractility response of the isolated ileal segments compared with normal untreated rats. Treatment with sulfasalazine, low dose of natrexone or their combination resulted in significant improvement of all measured parameters compared with enteritis group. The current finding could provide new interesting opportunity for developing new therapeutic approaches for treatment of Crohn's disease. Use of naltrexone, especially in small dose, has little side effects making it of interest for treatment of Crohn's disease. Also, it provides the possibility of reduced doses of other drugs if it is used as combined therapy. Copyright © 2016 Elsevier Ltd. All rights reserved.

Singing voice detection for karaoke application

NASA Astrophysics Data System (ADS)

Shenoy, Arun; Wu, Yuansheng; Wang, Ye

2005-07-01

We present a framework to detect the regions of singing voice in musical audio signals. This work is oriented towards the development of a robust transcriber of lyrics for karaoke applications. The technique leverages on a combination of low-level audio features and higher level musical knowledge of rhythm and tonality. Musical knowledge of the key is used to create a song-specific filterbank to attenuate the presence of the pitched musical instruments. This is followed by subband processing of the audio to detect the musical octaves in which the vocals are present. Text processing is employed to approximate the duration of the sung passages using freely available lyrics. This is used to obtain a dynamic threshold for vocal/ non-vocal segmentation. This pairing of audio and text processing helps create a more accurate system. Experimental evaluation on a small database of popular songs shows the validity of the proposed approach. Holistic and per-component evaluation of the system is conducted and various improvements are discussed.

Prosthetic component segmentation with blur compensation: a fast method for 3D fluoroscopy.

PubMed

Tarroni, Giacomo; Tersi, Luca; Corsi, Cristiana; Stagni, Rita

2012-06-01

A new method for prosthetic component segmentation from fluoroscopic images is presented. The hybrid approach we propose combines diffusion filtering, region growing and level-set techniques without exploiting any a priori knowledge of the analyzed geometry. The method was evaluated on a synthetic dataset including 270 images of knee and hip prosthesis merged to real fluoroscopic data simulating different conditions of blurring and illumination gradient. The performance of the method was assessed by comparing estimated contours to references using different metrics. Results showed that the segmentation procedure is fast, accurate, independent on the operator as well as on the specific geometrical characteristics of the prosthetic component, and able to compensate for amount of blurring and illumination gradient. Importantly, the method allows a strong reduction of required user interaction time when compared to traditional segmentation techniques. Its effectiveness and robustness in different image conditions, together with simplicity and fast implementation, make this prosthetic component segmentation procedure promising and suitable for multiple clinical applications including assessment of in vivo joint kinematics in a variety of cases.

A hybrid segmentation approach for geographic atrophy in fundus auto-fluorescence images for diagnosis of age-related macular degeneration.

PubMed

Lee, Noah; Laine, Andrew F; Smith, R Theodore

2007-01-01

Fundus auto-fluorescence (FAF) images with hypo-fluorescence indicate geographic atrophy (GA) of the retinal pigment epithelium (RPE) in age-related macular degeneration (AMD). Manual quantification of GA is time consuming and prone to inter- and intra-observer variability. Automatic quantification is important for determining disease progression and facilitating clinical diagnosis of AMD. In this paper we describe a hybrid segmentation method for GA quantification by identifying hypo-fluorescent GA regions from other interfering retinal vessel structures. First, we employ background illumination correction exploiting a non-linear adaptive smoothing operator. Then, we use the level set framework to perform segmentation of hypo-fluorescent areas. Finally, we present an energy function combining morphological scale-space analysis with a geometric model-based approach to perform segmentation refinement of false positive hypo- fluorescent areas due to interfering retinal structures. The clinically apparent areas of hypo-fluorescence were drawn by an expert grader and compared on a pixel by pixel basis to our segmentation results. The mean sensitivity and specificity of the ROC analysis were 0.89 and 0.98%.

Prevalence and Distribution of Atherosclerosis in a Low- to Intermediate-Risk Population: Assessment with Whole-Body MR Angiography

PubMed Central

Lambert, Matthew A.; Weir-McCall, Jonathan R.; Gandy, Stephen J.; Levin, Daniel; Cavin, Ian; Littleford, Roberta; MacFarlane, Jennifer A.; Matthew, Shona Z.; Nicholas, Richard S.; Struthers, Allan D.; Sullivan, Frank; Henderson, Shelley A.; White, Richard D.; Belch, Jill J. F.

2018-01-01

Purpose To quantify the burden and distribution of asymptomatic atherosclerosis in a population with a low to intermediate risk of cardiovascular disease. Materials and Methods Between June 2008 and February 2013, 1528 participants with 10-year risk of cardiovascular disease less than 20% were prospectively enrolled. They underwent whole-body magnetic resonance (MR) angiography at 3.0 T by using a two-injection, four-station acquisition technique. Thirty-one arterial segments were scored according to maximum stenosis. Scores were summed and normalized for the number of assessable arterial segments to provide a standardized atheroma score (SAS). Multiple linear regression was performed to assess effects of risk factors on atheroma burden. Results A total of 1513 participants (577 [37.9%] men; median age, 53.5 years; range, 40–83 years) completed the study protocol. Among 46 903 potentially analyzable segments, 46 601 (99.4%) were interpretable. Among these, 2468 segments (5%) demonstrated stenoses, of which 1649 (3.5%) showed stenosis less than 50% and 484 (1.0%) showed stenosis greater than or equal to 50%. Vascular stenoses were distributed throughout the body with no localized distribution. Seven hundred forty-seven (49.4%) participants had at least one stenotic vessel, and 408 (27.0%) participants had multiple stenotic vessels. At multivariable linear regression, SAS correlated with age (B = 3.4; 95% confidence interval: 2.61, 4.20), heart rate (B = 1.23; 95% confidence interval: 0.51, 1.95), systolic blood pressure (B = 0.02; 95% confidence interval: 0.01, 0.03), smoking status (B = 0.79; 95% confidence interval: 0.44, 1.15), and socioeconomic status (B = −0.06; 95% confidence interval: −0.10, −0.02) (P < .01 for all). Conclusion Whole-body MR angiography identifies early vascular disease at a population level. Although disease prevalence is low on a per-vessel level, vascular disease is common on a per-participant level, even in this low- to intermediate-risk cohort. © RSNA, 2018 Online supplemental material is available for this article. PMID:29714681

Association of admission testosterone level with ST-segment resolution in male patients with ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention.

PubMed

Separham, Ahmad; Ghaffari, Samad; Sohrabi, Bahram; Aslanabadi, Naser; Hadavi Bavil, Mozhgan; Lotfollahi, Hasanali

2017-01-01

Low level of testosterone may be associated with cardiovascular diseases in men, as some evidence suggests a protective role for testosterone in cardiovascular system. Little is known about the possible role of serum testosterone in response to reperfusion therapy in ST-elevation myocardial infarction (STEMI) and its relationship with ST-segment recovery. The present study was conducted to evaluate the association of serum testosterone levels with ST-segment resolution following primary percutaneous coronary intervention (PPCI) in male patients with acute STEMI. Forty-eight men (mean age 54.55 ± 12.20) with STEMI undergoing PPCI were enrolled prospectively. Single-lead ST segment resolution in the lead with maximum baseline ST-elevation was measured and patients were divided into two groups according to the degree of ST-segment resolution: complete (> or =50%) or incomplete (<50%). The basic and demographic data of all patients, their left ventricular ejection fraction (LVEF) and laboratory findings including serum levels of free testosterone and cardiac enzymes were recorded along with angiographic finding and baseline TIMI (Thrombolysis in Myocardial Infarction) flow and also in-hospital complications and then these variables were compared between two groups. A complete ST-resolution (≥50%) was observed in 72.9% of the patients. The serum levels of free testosterone ( P  = 0.04), peak cardiac troponin ( P  = 0.03) were significantly higher and hs-CRP ( P  = 0.02) were lower in patients with complete ST-resolution compared to those with incomplete ST-resolution. In-hospital complications were observed in 31.2% of patients. The patients with a lower baseline TIMI flow ( P  = 0.03) and those who developed complications ( P  = 0.04) had lower levels of free testosterone. A significant positive correlation was observed between the left ventricular function and serum levels of free testosterone ( P  = 0.01 and r = +0.362). This study suggests that in men with STEMI undergoing PPCI, higher serum levels of testosterone are associated with a better reperfusion response, fewer complications and a better left ventricular function.

Leaf Segmentation and Tracking in Arabidopsis thaliana Combined to an Organ-Scale Plant Model for Genotypic Differentiation

PubMed Central

Viaud, Gautier; Loudet, Olivier; Cournède, Paul-Henry

2017-01-01

A promising method for characterizing the phenotype of a plant as an interaction between its genotype and its environment is to use refined organ-scale plant growth models that use the observation of architectural traits, such as leaf area, containing a lot of information on the whole history of the functioning of the plant. The Phenoscope, a high-throughput automated platform, allowed the acquisition of zenithal images of Arabidopsis thaliana over twenty one days for 4 different genotypes. A novel image processing algorithm involving both segmentation and tracking of the plant leaves allows to extract areas of the latter. First, all the images in the series are segmented independently using a watershed-based approach. A second step based on ellipsoid-shaped leaves is then applied on the segments found to refine the segmentation. Taking into account all the segments at every time, the whole history of each leaf is reconstructed by choosing recursively through time the most probable segment achieving the best score, computed using some characteristics of the segment such as its orientation, its distance to the plant mass center and its area. These results are compared to manually extracted segments, showing a very good accordance in leaf rank and that they therefore provide low-biased data in large quantity for leaf areas. Such data can therefore be exploited to design an organ-scale plant model adapted from the existing GreenLab model for A. thaliana and subsequently parameterize it. This calibration of the model parameters should pave the way for differentiation between the Arabidopsis genotypes. PMID:28123392

Word-level recognition of multifont Arabic text using a feature vector matching approach

NASA Astrophysics Data System (ADS)

Erlandson, Erik J.; Trenkle, John M.; Vogt, Robert C., III

1996-03-01

Many text recognition systems recognize text imagery at the character level and assemble words from the recognized characters. An alternative approach is to recognize text imagery at the word level, without analyzing individual characters. This approach avoids the problem of individual character segmentation, and can overcome local errors in character recognition. A word-level recognition system for machine-printed Arabic text has been implemented. Arabic is a script language, and is therefore difficult to segment at the character level. Character segmentation has been avoided by recognizing text imagery of complete words. The Arabic recognition system computes a vector of image-morphological features on a query word image. This vector is matched against a precomputed database of vectors from a lexicon of Arabic words. Vectors from the database with the highest match score are returned as hypotheses for the unknown image. Several feature vectors may be stored for each word in the database. Database feature vectors generated using multiple fonts and noise models allow the system to be tuned to its input stream. Used in conjunction with database pruning techniques, this Arabic recognition system has obtained promising word recognition rates on low-quality multifont text imagery.

Segmental Versican Expression in the Trabecular Meshwork and Involvement in Outflow Facility

PubMed Central

Keller, Kate E.; Bradley, John M.; Vranka, Janice A.

2011-01-01

Purpose. Versican is a large proteoglycan with numerous chondroitin sulfate (CS) glycosaminoglycan (GAG) side chains attached. To assess versican's potential contributions to aqueous humor outflow resistance, its segmental distribution in the trabecular meshwork (TM) and the effect on outflow facility of silencing the versican gene were evaluated. Methods. Fluorescent quantum dots (Qdots) were perfused to label outflow pathways of anterior segments. Immunofluorescence with confocal microscopy and quantitative RT-PCR were used to determine versican protein and mRNA distribution relative to Qdot-labeled regions. Lentiviral delivery of shRNA-silencing cassettes to TM cells in perfused anterior segment cultures was used to evaluate the involvement of versican and CS GAG chains in outflow facility. Results. Qdot uptake by TM cells showed considerable segmental variability in both human and porcine outflow pathways. Regional levels of Qdot labeling were inversely related to versican protein and mRNA levels; versican levels were relatively high in sparsely Qdot-labeled regions and low in densely labeled regions. Versican silencing decreased outflow facility in human and increased facility in porcine anterior segments. However, RNAi silencing of ChGn, an enzyme unique to CS GAG biosynthesis, increased outflow facility in both species. The fibrillar pattern of versican immunostaining in the TM juxtacanalicular region was disrupted after versican silencing in perfusion culture. Conclusions. Versican appears to be a central component of the outflow resistance, where it may organize GAGs and other ECM components to facilitate and control open flow channels in the TM. However, the exact molecular organization of this resistance appears to differ between human and porcine eyes. PMID:21596823

Attainment rate as a surrogate indicator of the intervertebral neutral zone length in lateral bending: an in vitro proof of concept study.

PubMed

Breen, Alexander C; Dupac, Mihai; Osborne, Neil

2015-01-01

Lumbar segmental instability is often considered to be a cause of chronic low back pain. However, defining its measurement has been largely limited to laboratory studies. These have characterised segmental stability as the intrinsic resistance of spine specimens to initial bending moments by quantifying the dynamic neutral zone. However these measurements have been impossible to obtain in vivo without invasive procedures, preventing the assessment of intervertebral stability in patients. Quantitative fluoroscopy (QF), measures the initial velocity of the attainment of intervertebral rotational motion in patients, which may to some extent be representative of the dynamic neutral zone. This study sought to explore the possible relationship between the dynamic neutral zone and intervertebral rotational attainment rate as measured with (QF) in an in vitro preparation. The purpose was to find out if further work into this concept is worth pursuing. This study used passive recumbent QF in a multi-segmental porcine model. This assessed the intrinsic intervertebral responses to a minimal coronal plane bending moment as measured with a digital force guage. Bending moments about each intervertebral joint were calculated and correlated with the rate at which global motion was attained at each intervertebral segment in the first 10° of global motion where the intervertebral joint was rotating. Unlike previous studies of single segment specimens, a neutral zone was found to exist during lateral bending. The initial attainment rates for left and right lateral flexion were comparable to previously published in vivo values for healthy controls. Substantial and highly significant levels of correlation between initial attainment rate and neutral zone were found for left (Rho = 0.75, P = 0.0002) and combined left-right bending (Rho = 0.72, P = 0.0001) and moderate ones for right alone (Rho = 0.55, P = 0.0012). This study found good correlation between the initial intervertebral attainment rate and the dynamic neutral zone, thereby opening the possibility to detect segmental instability from clinical studies. However the results must be treated with caution. Further studies with multiple specimens and adding sagittal plane motion are warranted.

Multiscale approach to contour fitting for MR images

NASA Astrophysics Data System (ADS)

Rueckert, Daniel; Burger, Peter

1996-04-01

We present a new multiscale contour fitting process which combines information about the image and the contour of the object at different levels of scale. The algorithm is based on energy minimizing deformable models but avoids some of the problems associated with these models. The segmentation algorithm starts by constructing a linear scale-space of an image through convolution of the original image with a Gaussian kernel at different levels of scale, where the scale corresponds to the standard deviation of the Gaussian kernel. At high levels of scale large scale features of the objects are preserved while small scale features, like object details as well as noise, are suppressed. In order to maximize the accuracy of the segmentation, the contour of the object of interest is then tracked in scale-space from coarse to fine scales. We propose a hybrid multi-temperature simulated annealing optimization to minimize the energy of the deformable model. At high levels of scale the SA optimization is started at high temperatures, enabling the SA optimization to find a global optimal solution. At lower levels of scale the SA optimization is started at lower temperatures (at the lowest level the temperature is close to 0). This enforces a more deterministic behavior of the SA optimization at lower scales and leads to an increasingly local optimization as high energy barriers cannot be crossed. The performance and robustness of the algorithm have been tested on spin-echo MR images of the cardiovascular system. The task was to segment the ascending and descending aorta in 15 datasets of different individuals in order to measure regional aortic compliance. The results show that the algorithm is able to provide more accurate segmentation results than the classic contour fitting process and is at the same time very robust to noise and initialization.

A Unified Framework for Brain Segmentation in MR Images

PubMed Central

Yazdani, S.; Yusof, R.; Karimian, A.; Riazi, A. H.; Bennamoun, M.

2015-01-01

Brain MRI segmentation is an important issue for discovering the brain structure and diagnosis of subtle anatomical changes in different brain diseases. However, due to several artifacts brain tissue segmentation remains a challenging task. The aim of this paper is to improve the automatic segmentation of brain into gray matter, white matter, and cerebrospinal fluid in magnetic resonance images (MRI). We proposed an automatic hybrid image segmentation method that integrates the modified statistical expectation-maximization (EM) method and the spatial information combined with support vector machine (SVM). The combined method has more accurate results than what can be achieved with its individual techniques that is demonstrated through experiments on both real data and simulated images. Experiments are carried out on both synthetic and real MRI. The results of proposed technique are evaluated against manual segmentation results and other methods based on real T1-weighted scans from Internet Brain Segmentation Repository (IBSR) and simulated images from BrainWeb. The Kappa index is calculated to assess the performance of the proposed framework relative to the ground truth and expert segmentations. The results demonstrate that the proposed combined method has satisfactory results on both simulated MRI and real brain datasets. PMID:26089978

Expression and Significance of Neuroligins in Myenteric Cells of Cajal in Hirschsprung's Disease

PubMed Central

Wang, Jian; Mou, Yaru; Zhang, Qiangye; Zhang, Fan; Yang, Hongchao; Zhang, Wentong; Li, Aiwu

2013-01-01

Purpose The aim of this study was to investigate the expression and significance of neuroligins in myenteric cells of Cajal (ICC-MY) in Hirschsprung’s disease (HSCR). Methods Longitudinal muscle with adherent myenteric plexus (LMMP) from surgical excision waste colon of HSCR children were prepared by peeling off the mucous layer, sub-mucosal layer and circular muscle. Neuroligins, c-Kit (c-Kit-immunoreactivity representing ICC) and their relationship were assessed by double labeling immunofluorescence staining. ICC-MY were dissociated and cultured from LMMP by enzymolysis method, and were purified and analyzed using a combination of magnetic-activated cell sorting (MACS) and flow cytometry (FCM). Western-blot analysis was applied to compare and evaluate the expression levels of neuroligins in ICC-MY which were dissociated from different segments of HSCR (ganglionic colonic segment, transitional colonic segment and aganglionic colonic segment). Results Neuroligins and c-Kit were expressed on the same cells (ICC-MY); ICC-MY were dissociated, cultured and purified. For HSCR, neuroligins were expressed significantly in ICC-MY from ganglionic colonic segments, moderately in those from transitional colonic segments and down-regulated significantly in those from aganglionic colonic segments. Conclusions Neuroligins were expressed in ICC-MY of human beings, and the expression varies from different segments of HSCR. This abnormal expression might play an important role in the pathogenesis of this disease through affecting the synaptic function of ICC-MY. PMID:23840625

Comparing demographic, health status and psychosocial strategies of audience segmentation to promote physical activity.

PubMed

Boslaugh, Sarah E; Kreuter, Matthew W; Nicholson, Robert A; Naleid, Kimberly

2005-08-01

The goal of audience segmentation is to identify population subgroups that are homogeneous with respect to certain variables associated with a given outcome or behavior. When such groups are identified and understood, targeted intervention strategies can be developed to address their unique characteristics and needs. This study compares the results of audience segmentation for physical activity that is based on either demographic, health status or psychosocial variables alone, or a combination of all three types of variables. Participants were 1090 African-American and White adults from two public health centers in St Louis, MO. Using a classification-tree algorithm to form homogeneous groups, analyses showed that more segments with greater variability in physical activity were created using psychosocial versus health status or demographic variables and that a combination of the three outperformed any individual set of variables. Simple segmentation strategies such as those relying on demographic variables alone provided little improvement over no segmentation at all. Audience segmentation appears to yield more homogeneous subgroups when psychosocial and health status factors are combined with demographic variables.

An adaptive multi-feature segmentation model for infrared image

NASA Astrophysics Data System (ADS)

Zhang, Tingting; Han, Jin; Zhang, Yi; Bai, Lianfa

2016-04-01

Active contour models (ACM) have been extensively applied to image segmentation, conventional region-based active contour models only utilize global or local single feature information to minimize the energy functional to drive the contour evolution. Considering the limitations of original ACMs, an adaptive multi-feature segmentation model is proposed to handle infrared images with blurred boundaries and low contrast. In the proposed model, several essential local statistic features are introduced to construct a multi-feature signed pressure function (MFSPF). In addition, we draw upon the adaptive weight coefficient to modify the level set formulation, which is formed by integrating MFSPF with local statistic features and signed pressure function with global information. Experimental results demonstrate that the proposed method can make up for the inadequacy of the original method and get desirable results in segmenting infrared images.

The Relationships between Verbal and Nonverbal Communication of Therapeutic Effectiveness.

ERIC Educational Resources Information Center

Proefrock, David W.; Bloom, Robert

The relationship between a therapist's verbal and nonverbal communication of therapeutic effectiveness was investigated. In a design intended to eliminate many of the methodological problems which exist in this area of research, subjects (N=102) were asked to rate videotaped segments showing combinations of three different levels of both verbal…

Cell and current collector felt arrangement for solid oxide electrochemical cell combinations

DOEpatents

Reichner, Philip

1988-01-01

A solid electrolyte electrochemical cell combination 1 is made, comprising an annular, axially elongated, inner electrode 2 containing at least one interior gas feed conduit 3; annular solid electrolyte segments 4 around and covering portions of the inner electrode; annular outer electrode segments 6 around and covering portions of the electrolyte segments; electronically conducting, non-porous, interconnection material 5 disposed between electrolyte segments and in contact with the inner electrode, and electronically conducting, porous, metal fiber current collector felts 7 disposed on top of the non-porous interconnect material and outer electrode segments, where both the non-porous interconnect material and the porous metal felts are disposed circumferentially about the cell, transversely to the axial length of the cell and the inner electrode is continuous for the entire axial length of the cell combination.

Optical Metrology for the Segmented Optics on the Constellation-X Spectroscopy X-Ray Telescope

NASA Technical Reports Server (NTRS)

Content, David; Colella, David; Fleetwood, Charles; Hadjimichael, Theo; Lehan, John; McMann, Joseph; Reid, Paul; Saha, Timo; Wright, Geraldine; Zhang, William

2004-01-01

We present the metrology requirements and metrology implementation necessary to prove out the reflector technology for the Constellation X(C-X) spectroscopy X-ray telescope (SXT). This segmented, 1.6m diameter highly nested Wolter-1 telescope presents many metrology and alignment challenges. In particular, these mirrors have a stringent imaging error budget as compared to their intrinsic stiffness; This is required for Constellation-X to have sufficient effective area with the weight requirement. This has implications for the metrology that can be used. A variety of contract and noncontact optical profiling and interferometric methods are combined to test the formed glass substrates before replication and the replicated reflector segments.The reflectors are tested both stand-alone and in-situ in an alignment tower.Some of these methods have not been used on prior X-ray telescopes and some are feasible only because of the segmented approach used on the SXT. Methods discussed include high precision coordinate measurement machines using very low force or optical probe axial interferometric profiling azimuthal circularity profiling and use of advanced null optics such as conical computer generated hologram (CGHs).

Meta-shell Approach for Constructing Lightweight and High Resolution X-Ray Optics

NASA Technical Reports Server (NTRS)

McClelland, Ryan S.

2016-01-01

Lightweight and high resolution optics are needed for future space-based x-ray telescopes to achieve advances in high-energy astrophysics. Past missions such as Chandra and XMM-Newton have achieved excellent angular resolution using a full shell mirror approach. Other missions such as Suzaku and NuSTAR have achieved lightweight mirrors using a segmented approach. This paper describes a new approach, called meta-shells, which combines the fabrication advantages of segmented optics with the alignment advantages of full shell optics. Meta-shells are built by layering overlapping mirror segments onto a central structural shell. The resulting optic has the stiffness and rotational symmetry of a full shell, but with an order of magnitude greater collecting area. Several meta-shells so constructed can be integrated into a large x-ray mirror assembly by proven methods used for Chandra and XMM-Newton. The mirror segments are mounted to the meta-shell using a novel four point semi-kinematic mount. The four point mount deterministically locates the segment in its most performance sensitive degrees of freedom. Extensive analysis has been performed to demonstrate the feasibility of the four point mount and meta-shell approach. A mathematical model of a meta-shell constructed with mirror segments bonded at four points and subject to launch loads has been developed to determine the optimal design parameters, namely bond size, mirror segment span, and number of layers per meta-shell. The parameters of an example 1.3 m diameter mirror assembly are given including the predicted effective area. To verify the mathematical model and support opto-mechanical analysis, a detailed finite element model of a meta-shell was created. Finite element analysis predicts low gravity distortion and low thermal distortion. Recent results are discussed including Structural Thermal Optical Performance (STOP) analysis as well as vibration and shock testing of prototype meta-shells.

Fast and robust brain tumor segmentation using level set method with multiple image information.

PubMed

Lok, Ka Hei; Shi, Lin; Zhu, Xianlun; Wang, Defeng

2017-01-01

Brain tumor segmentation is a challenging task for its variation in intensity. The phenomenon is caused by the inhomogeneous content of tumor tissue and the choice of imaging modality. In 2010 Zhang developed the Selective Binary Gaussian Filtering Regularizing Level Set (SBGFRLS) model that combined the merits of edge-based and region-based segmentation. To improve the SBGFRLS method by modifying the singed pressure force (SPF) term with multiple image information and demonstrate effectiveness of proposed method on clinical images. In original SBGFRLS model, the contour evolution direction mainly depends on the SPF. By introducing a directional term in SPF, the metric could control the evolution direction. The SPF is altered by statistic values enclosed by the contour. This concept can be extended to jointly incorporate multiple image information. The new SPF term is expected to bring a solution for blur edge problem in brain tumor segmentation. The proposed method is validated with clinical images including pre- and post-contrast magnetic resonance images. The accuracy and robustness is compared with sensitivity, specificity, DICE similarity coefficient and Jaccard similarity index. Experimental results show improvement, in particular the increase of sensitivity at the same specificity, in segmenting all types of tumors except for the diffused tumor. The novel brain tumor segmentation method is clinical-oriented with fast, robust and accurate implementation and a minimal user interaction. The method effectively segmented homogeneously enhanced, non-enhanced, heterogeneously-enhanced, and ring-enhanced tumor under MR imaging. Though the method is limited by identifying edema and diffuse tumor, several possible solutions are suggested to turn the curve evolution into a fully functional clinical diagnosis tool.

A new background distribution-based active contour model for three-dimensional lesion segmentation in breast DCE-MRI

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Hui; Liu, Yiping; Qiu, Tianshuang

2014-08-15

Purpose: To develop and evaluate a computerized semiautomatic segmentation method for accurate extraction of three-dimensional lesions from dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) of the breast. Methods: The authors propose a new background distribution-based active contour model using level set (BDACMLS) to segment lesions in breast DCE-MRIs. The method starts with manual selection of a region of interest (ROI) that contains the entire lesion in a single slice where the lesion is enhanced. Then the lesion volume from the volume data of interest, which is captured automatically, is separated. The core idea of BDACMLS is a new signed pressure functionmore » which is based solely on the intensity distribution combined with pathophysiological basis. To compare the algorithm results, two experienced radiologists delineated all lesions jointly to obtain the ground truth. In addition, results generated by other different methods based on level set (LS) are also compared with the authors’ method. Finally, the performance of the proposed method is evaluated by several region-based metrics such as the overlap ratio. Results: Forty-two studies with 46 lesions that contain 29 benign and 17 malignant lesions are evaluated. The dataset includes various typical pathologies of the breast such as invasive ductal carcinoma, ductal carcinomain situ, scar carcinoma, phyllodes tumor, breast cysts, fibroadenoma, etc. The overlap ratio for BDACMLS with respect to manual segmentation is 79.55% ± 12.60% (mean ± s.d.). Conclusions: A new active contour model method has been developed and shown to successfully segment breast DCE-MRI three-dimensional lesions. The results from this model correspond more closely to manual segmentation, solve the weak-edge-passed problem, and improve the robustness in segmenting different lesions.« less

Sequential addition of short DNA oligos in DNA-polymerase-based synthesis reactions

DOEpatents

Gardner, Shea N [San Leandro, CA; Mariella, Jr., Raymond P.; Christian, Allen T [Tracy, CA; Young, Jennifer A [Berkeley, CA; Clague, David S [Livermore, CA

2011-01-18

A method of fabricating a DNA molecule of user-defined sequence. The method comprises the steps of preselecting a multiplicity of DNA sequence segments that will comprise the DNA molecule of user-defined sequence, separating the DNA sequence segments temporally, and combining the multiplicity of DNA sequence segments with at least one polymerase enzyme wherein the multiplicity of DNA sequence segments join to produce the DNA molecule of user-defined sequence. Sequence segments may be of length n, where n is an even or odd integer. In one embodiment the length of desired hybridizing overlap is specified by the user and the sequences and the protocol for combining them are guided by computational (bioinformatics) predictions. In one embodiment sequence segments are combined from multiple reading frames to span the same region of a sequence, so that multiple desired hybridizations may occur with different overlap lengths. In one embodiment starting sequence fragments are of different lengths, n, n+1, n+2, etc.

A fully automatic approach for multimodal PET and MR image segmentation in gamma knife treatment planning.

PubMed

Rundo, Leonardo; Stefano, Alessandro; Militello, Carmelo; Russo, Giorgio; Sabini, Maria Gabriella; D'Arrigo, Corrado; Marletta, Francesco; Ippolito, Massimo; Mauri, Giancarlo; Vitabile, Salvatore; Gilardi, Maria Carla

2017-06-01

Nowadays, clinical practice in Gamma Knife treatments is generally based on MRI anatomical information alone. However, the joint use of MRI and PET images can be useful for considering both anatomical and metabolic information about the lesion to be treated. In this paper we present a co-segmentation method to integrate the segmented Biological Target Volume (BTV), using [ 11 C]-Methionine-PET (MET-PET) images, and the segmented Gross Target Volume (GTV), on the respective co-registered MR images. The resulting volume gives enhanced brain tumor information to be used in stereotactic neuro-radiosurgery treatment planning. GTV often does not match entirely with BTV, which provides metabolic information about brain lesions. For this reason, PET imaging is valuable and it could be used to provide complementary information useful for treatment planning. In this way, BTV can be used to modify GTV, enhancing Clinical Target Volume (CTV) delineation. A novel fully automatic multimodal PET/MRI segmentation method for Leksell Gamma Knife ® treatments is proposed. This approach improves and combines two computer-assisted and operator-independent single modality methods, previously developed and validated, to segment BTV and GTV from PET and MR images, respectively. In addition, the GTV is utilized to combine the superior contrast of PET images with the higher spatial resolution of MRI, obtaining a new BTV, called BTV MRI . A total of 19 brain metastatic tumors, undergone stereotactic neuro-radiosurgery, were retrospectively analyzed. A framework for the evaluation of multimodal PET/MRI segmentation is also presented. Overlap-based and spatial distance-based metrics were considered to quantify similarity concerning PET and MRI segmentation approaches. Statistics was also included to measure correlation among the different segmentation processes. Since it is not possible to define a gold-standard CTV according to both MRI and PET images without treatment response assessment, the feasibility and the clinical value of BTV integration in Gamma Knife treatment planning were considered. Therefore, a qualitative evaluation was carried out by three experienced clinicians. The achieved experimental results showed that GTV and BTV segmentations are statistically correlated (Spearman's rank correlation coefficient: 0.898) but they have low similarity degree (average Dice Similarity Coefficient: 61.87 ± 14.64). Therefore, volume measurements as well as evaluation metrics values demonstrated that MRI and PET convey different but complementary imaging information. GTV and BTV could be combined to enhance treatment planning. In more than 50% of cases the CTV was strongly or moderately conditioned by metabolic imaging. Especially, BTV MRI enhanced the CTV more accurately than BTV in 25% of cases. The proposed fully automatic multimodal PET/MRI segmentation method is a valid operator-independent methodology helping the clinicians to define a CTV that includes both metabolic and morphologic information. BTV MRI and GTV should be considered for a comprehensive treatment planning. Copyright © 2017 Elsevier B.V. All rights reserved.

Example based lesion segmentation

NASA Astrophysics Data System (ADS)

Roy, Snehashis; He, Qing; Carass, Aaron; Jog, Amod; Cuzzocreo, Jennifer L.; Reich, Daniel S.; Prince, Jerry; Pham, Dzung

2014-03-01

Automatic and accurate detection of white matter lesions is a significant step toward understanding the progression of many diseases, like Alzheimer's disease or multiple sclerosis. Multi-modal MR images are often used to segment T2 white matter lesions that can represent regions of demyelination or ischemia. Some automated lesion segmentation methods describe the lesion intensities using generative models, and then classify the lesions with some combination of heuristics and cost minimization. In contrast, we propose a patch-based method, in which lesions are found using examples from an atlas containing multi-modal MR images and corresponding manual delineations of lesions. Patches from subject MR images are matched to patches from the atlas and lesion memberships are found based on patch similarity weights. We experiment on 43 subjects with MS, whose scans show various levels of lesion-load. We demonstrate significant improvement in Dice coefficient and total lesion volume compared to a state of the art model-based lesion segmentation method, indicating more accurate delineation of lesions.

Comparative study on the performance of textural image features for active contour segmentation.

PubMed

Moraru, Luminita; Moldovanu, Simona

2012-07-01

We present a computerized method for the semi-automatic detection of contours in ultrasound images. The novelty of our study is the introduction of a fast and efficient image function relating to parametric active contour models. This new function is a combination of the gray-level information and first-order statistical features, called standard deviation parameters. In a comprehensive study, the developed algorithm and the efficiency of segmentation were first tested for synthetic images. Tests were also performed on breast and liver ultrasound images. The proposed method was compared with the watershed approach to show its efficiency. The performance of the segmentation was estimated using the area error rate. Using the standard deviation textural feature and a 5×5 kernel, our curve evolution was able to produce results close to the minimal area error rate (namely 8.88% for breast images and 10.82% for liver images). The image resolution was evaluated using the contrast-to-gradient method. The experiments showed promising segmentation results.

Fish habitat characterization and quantification using lidar and conventional topographic information in river survey

NASA Astrophysics Data System (ADS)

Marchamalo, Miguel; Bejarano, María-Dolores; García de Jalón, Diego; Martínez Marín, Rubén

2007-10-01

This study presents the application of LIDAR data to the evaluation and quantification of fluvial habitat in river systems, coupling remote sensing techniques with hydrological modeling and ecohydraulics. Fish habitat studies depend on the quality and continuity of the input topographic data. Conventional fish habitat studies are limited by the feasibility of field survey in time and budget. This limitation results in differences between the level of river management and the level of models. In order to facilitate upscaling processes from modeling to management units, meso-scale methods were developed (Maddock & Bird, 1996; Parasiewicz, 2001). LIDAR data of regulated River Cinca (Ebro Basin, Spain) were acquired in the low flow season, maximizing the recorded instream area. DTM meshes obtained from LIDAR were used as the input for hydraulic simulation for a range of flows using GUAD2D software. Velocity and depth outputs were combined with gradient data to produce maps reflecting the availability of each mesohabitat unit type for each modeled flow. Fish habitat was then estimated and quantified according to the preferences of main target species as brown trout (Salmo trutta). LIDAR data combined with hydraulic modeling allowed the analysis of fluvial habitat in long fluvial segments which would be time-consuming with traditional survey. LIDAR habitat assessment at mesoscale level avoids the problems of time efficiency and upscaling and is a recommended approach for large river basin management.

Semiautomated hybrid algorithm for estimation of three-dimensional liver surface in CT using dynamic cellular automata and level-sets

PubMed Central

Dakua, Sarada Prasad; Abinahed, Julien; Al-Ansari, Abdulla

2015-01-01

Abstract. Liver segmentation continues to remain a major challenge, largely due to its intense complexity with surrounding anatomical structures (stomach, kidney, and heart), high noise level and lack of contrast in pathological computed tomography (CT) data. We present an approach to reconstructing the liver surface in low contrast CT. The main contributions are: (1) a stochastic resonance-based methodology in discrete cosine transform domain is developed to enhance the contrast of pathological liver images, (2) a new formulation is proposed to prevent the object boundary, resulting from the cellular automata method, from leaking into the surrounding areas of similar intensity, and (3) a level-set method is suggested to generate intermediate segmentation contours from two segmented slices distantly located in a subject sequence. We have tested the algorithm on real datasets obtained from two sources, Hamad General Hospital and medical image computing and computer-assisted interventions grand challenge workshop. Various parameters in the algorithm, such as w, Δt, z, α, μ, α1, and α2, play imperative roles, thus their values are precisely selected. Both qualitative and quantitative evaluation performed on liver data show promising segmentation accuracy when compared with ground truth data reflecting the potential of the proposed method. PMID:26158101

Preliminary performance and life evaluations of a 2-kW arcjet

NASA Technical Reports Server (NTRS)

Morren, W. Earl; Curran, Francis M.

1991-01-01

The first results of a program to expand the operational envelope of low-power arcjets to higher specific impulse and power levels are presented. The performance of a kW-class laboratory model arcjet thruster was characterized at three mass flow rates of a 2:1 mixture of hydrogen and nitrogen at power levels ranging from 1.0 to 2.0 kW. This same thruster was then operated for a total of 300 h at a specific impulse and power level of 550 s and 2.0 kW, respectively, in three continuous 100-h sessions. Thruster operation during the three test segments was stable, and no measurable performance degradation was observed during the test series. Substantial cathode erosion was observed during an inspection following the second 100-h test segment. Most notable was the migration of material from the center of the cathode tip to a ring around a large crater. The anode sustained no significant damage during the endurance test segments. Some difficulty was encountered during start-up after disassembly and inspection following the second 100-h test segment, which caused constrictor erosion. This resulted in a reduced flow restriction and arc chamber pressure, which in turn caused a reduction in the arc impedance.

Inhaled Nitric Oxide in Acute Lung Disease.

DTIC Science & Technology

1995-01-01

play a pivotal and central cellular, NO. combines with the heme molecules role in many of the clinical manifestations of ARDS, it present in...blood flow to trate anion (NO3), and b) reaction with 02 to form poorly ventilated lung segments and further compro- nitrite anion (NO2) (23, 24). It...is the most consistent feature of creases in methemoglobin levels (Table 1). Plasma NO- therapy. Theoretically, inhaled NO. vasodilates nitrite levels

Delineating the boundary and structure of higher trophic level assemblages in the western North Pacific Ocean

NASA Astrophysics Data System (ADS)

Okuda, Takehiro; Kiyota, Masashi; Yonezaki, Shiroh; Murakami, Chisato; Kato, Yoshiki; Sakai, Mitsuo; Wakabayashi, Toshie; Okazaki, Makoto

2017-06-01

Understanding the community structure of oceanic higher trophic level (HTL) organisms (e.g., sharks, tunas, salmons, and squids) is fundamental to management of marine resources in a way that ensures their sustainable use and maintains marine ecosystem functionality and biodiversity. We analyzed the spatial structure of HTL assemblages in the western North Pacific Ocean using driftnet survey data collected at latitudes of 35-46 °N along transect lines at 144 °E, 155 °E, and 175.5 °E longitude in July and August 2011. We proposed a new dissimilarity metric segmentation procedure (Dissimilarity Segmentation) based on the differences of mean Bray-Curtis dissimilarity indices between two individual driftnet hauls within the same subarea or among different subareas. Dissimilarity Segmentation allowed us to divide the western North Pacific Ocean into three subareas: a northern subarea (>41 °N including 41 °N on the 175.5 °E transect), a transition subarea (37-41 °N), and a southern subarea (<37 °N). The HTL biomass in the northern subarea was high, and the species diversity was low; dominant and common species accounted for most of the biomass. The HTL assemblage in the southern subarea was composed of many species that were uncommon or rare; the biomass was lower, and the species diversity was higher than in the northern subarea. In the transition subarea, neon flying squid accounted for most of the biomass, and although the biomass was intermediate, species diversity was highest among the three subareas. Canonical correspondence analysis with oceanic environmental variables, principally chlorophyll a, sea surface salinity, and sea surface height, as the explanatory variables accounted for 43.6% of the variance of the HTL pelagic species composition. This result suggests that the HTL pelagic community in the western North Pacific is influenced largely by productivity and oceanic physical structure. These results suggest that an analytical approach based on Dissimilarity Segmentation combined with medium- to long-term survey datasets could facilitate the investigation of spatial-temporal variations in the spatial structure of HTL pelagic communities and the environmental causes thereof.

Modeling organizational justice improvements in a pediatric health service : a discrete-choice conjoint experiment.

PubMed

Cunningham, Charles E; Kostrzewa, Linda; Rimas, Heather; Chen, Yvonne; Deal, Ken; Blatz, Susan; Bowman, Alida; Buchanan, Don H; Calvert, Randy; Jennings, Barbara

2013-01-01

Patients value health service teams that function effectively. Organizational justice is linked to the performance, health, and emotional adjustment of the members of these teams. We used a discrete-choice conjoint experiment to study the organizational justice improvement preferences of pediatric health service providers. Using themes from a focus group with 22 staff, we composed 14 four-level organizational justice improvement attributes. A sample of 652 staff (76 % return) completed 30 choice tasks, each presenting three hospitals defined by experimentally varying the attribute levels. Latent class analysis yielded three segments. Procedural justice attributes were more important to the Decision Sensitive segment, 50.6 % of the sample. They preferred to contribute to and understand how all decisions were made and expected management to act promptly on more staff suggestions. Interactional justice attributes were more important to the Conduct Sensitive segment (38.5 %). A universal code of respectful conduct, consequences encouraging respectful interaction, and management's response when staff disagreed with them were more important to this segment. Distributive justice attributes were more important to the Benefit Sensitive segment, 10.9 % of the sample. Simulations predicted that, while Decision Sensitive (74.9 %) participants preferred procedural justice improvements, Conduct (74.6 %) and Benefit Sensitive (50.3 %) participants preferred interactional justice improvements. Overall, 97.4 % of participants would prefer an approach combining procedural and interactional justice improvements. Efforts to create the health service environments that patients value need to be comprehensive enough to address the preferences of segments of staff who are sensitive to different dimensions of organizational justice.

Consistency of Border-Ownership Cells across Artificial Stimuli, Natural Stimuli, and Stimuli with Ambiguous Contours.

PubMed

Hesse, Janis K; Tsao, Doris Y

2016-11-02

Segmentation and recognition of objects in a visual scene are two problems that are hard to solve separately from each other. When segmenting an ambiguous scene, it is helpful to already know the present objects and their shapes. However, for recognizing an object in clutter, one would like to consider its isolated segment alone to avoid confounds from features of other objects. Border-ownership cells (Zhou et al., 2000) appear to play an important role in segmentation, as they signal the side-of-figure of artificial stimuli. The present work explores the role of border-ownership cells in dorsal macaque visual areas V2 and V3 in the segmentation of natural object stimuli and locally ambiguous stimuli. We report two major results. First, compared with previous estimates, we found a smaller percentage of cells that were consistent across artificial stimuli used previously. Second, we found that the average response of those neurons that did respond consistently to the side-of-figure of artificial stimuli also consistently signaled, as a population, the side-of-figure for borders of single faces, occluding faces and, with higher latencies, even stimuli with illusory contours, such as Mooney faces and natural faces completely missing local edge information. In contrast, the local edge or the outlines of the face alone could not always evoke a significant border-ownership signal. Our results underscore that border ownership is coded by a population of cells, and indicate that these cells integrate a variety of cues, including low-level features and global object context, to compute the segmentation of the scene. To distinguish different objects in a natural scene, the brain must segment the image into regions corresponding to objects. The so-called "border-ownership" cells appear to be dedicated to this task, as they signal for a given edge on which side the object is that owns it. Here, we report that individual border-ownership cells are unreliable when tested across a battery of artificial stimuli used previously but can signal border-ownership consistently as a population. We show that these border-ownership population signals are also suited for signaling border-ownership for natural objects and at longer latency, even for stimuli without local edge information. Our results suggest that border-ownership cells integrate both local, low-level and global, high-level cues to segment the scene. Copyright © 2016 the authors 0270-6474/16/3611338-12$15.00/0.

Prostate segmentation by feature enhancement using domain knowledge and adaptive region based operations

NASA Astrophysics Data System (ADS)

Nanayakkara, Nuwan D.; Samarabandu, Jagath; Fenster, Aaron

2006-04-01

Estimation of prostate location and volume is essential in determining a dose plan for ultrasound-guided brachytherapy, a common prostate cancer treatment. However, manual segmentation is difficult, time consuming and prone to variability. In this paper, we present a semi-automatic discrete dynamic contour (DDC) model based image segmentation algorithm, which effectively combines a multi-resolution model refinement procedure together with the domain knowledge of the image class. The segmentation begins on a low-resolution image by defining a closed DDC model by the user. This contour model is then deformed progressively towards higher resolution images. We use a combination of a domain knowledge based fuzzy inference system (FIS) and a set of adaptive region based operators to enhance the edges of interest and to govern the model refinement using a DDC model. The automatic vertex relocation process, embedded into the algorithm, relocates deviated contour points back onto the actual prostate boundary, eliminating the need of user interaction after initialization. The accuracy of the prostate boundary produced by the proposed algorithm was evaluated by comparing it with a manually outlined contour by an expert observer. We used this algorithm to segment the prostate boundary in 114 2D transrectal ultrasound (TRUS) images of six patients scheduled for brachytherapy. The mean distance between the contours produced by the proposed algorithm and the manual outlines was 2.70 ± 0.51 pixels (0.54 ± 0.10 mm). We also showed that the algorithm is insensitive to variations of the initial model and parameter values, thus increasing the accuracy and reproducibility of the resulting boundaries in the presence of noise and artefacts.

Vertebra identification using template matching modelmp and K-means clustering.

PubMed

Larhmam, Mohamed Amine; Benjelloun, Mohammed; Mahmoudi, Saïd

2014-03-01

Accurate vertebra detection and segmentation are essential steps for automating the diagnosis of spinal disorders. This study is dedicated to vertebra alignment measurement, the first step in a computer-aided diagnosis tool for cervical spine trauma. Automated vertebral segment alignment determination is a challenging task due to low contrast imaging and noise. A software tool for segmenting vertebrae and detecting subluxations has clinical significance. A robust method was developed and tested for cervical vertebra identification and segmentation that extracts parameters used for vertebra alignment measurement. Our contribution involves a novel combination of a template matching method and an unsupervised clustering algorithm. In this method, we build a geometric vertebra mean model. To achieve vertebra detection, manual selection of the region of interest is performed initially on the input image. Subsequent preprocessing is done to enhance image contrast and detect edges. Candidate vertebra localization is then carried out by using a modified generalized Hough transform (GHT). Next, an adapted cost function is used to compute local voted centers and filter boundary data. Thereafter, a K-means clustering algorithm is applied to obtain clusters distribution corresponding to the targeted vertebrae. These clusters are combined with the vote parameters to detect vertebra centers. Rigid segmentation is then carried out by using GHT parameters. Finally, cervical spine curves are extracted to measure vertebra alignment. The proposed approach was successfully applied to a set of 66 high-resolution X-ray images. Robust detection was achieved in 97.5 % of the 330 tested cervical vertebrae. An automated vertebral identification method was developed and demonstrated to be robust to noise and occlusion. This work presents a first step toward an automated computer-aided diagnosis system for cervical spine trauma detection.

Unsupervised segmentation of lungs from chest radiographs

NASA Astrophysics Data System (ADS)

Ghosh, Payel; Antani, Sameer K.; Long, L. Rodney; Thoma, George R.

2012-03-01

This paper describes our preliminary investigations for deriving and characterizing coarse-level textural regions present in the lung field on chest radiographs using unsupervised grow-cut (UGC), a cellular automaton based unsupervised segmentation technique. The segmentation has been performed on a publicly available data set of chest radiographs. The algorithm is useful for this application because it automatically converges to a natural segmentation of the image from random seed points using low-level image features such as pixel intensity values and texture features. Our goal is to develop a portable screening system for early detection of lung diseases for use in remote areas in developing countries. This involves developing automated algorithms for screening x-rays as normal/abnormal with a high degree of sensitivity, and identifying lung disease patterns on chest x-rays. Automatically deriving and quantitatively characterizing abnormal regions present in the lung field is the first step toward this goal. Therefore, region-based features such as geometrical and pixel-value measurements were derived from the segmented lung fields. In the future, feature selection and classification will be performed to identify pathological conditions such as pulmonary tuberculosis on chest radiographs. Shape-based features will also be incorporated to account for occlusions of the lung field and by other anatomical structures such as the heart and diaphragm.

[Cardiac Synchronization Function Estimation Based on ASM Level Set Segmentation Method].

PubMed

Zhang, Yaonan; Gao, Yuan; Tang, Liang; He, Ying; Zhang, Huie

At present, there is no accurate and quantitative methods for the determination of cardiac mechanical synchronism, and quantitative determination of the synchronization function of the four cardiac cavities with medical images has a great clinical value. This paper uses the whole heart ultrasound image sequence, and segments the left & right atriums and left & right ventricles of each frame. After the segmentation, the number of pixels in each cavity and in each frame is recorded, and the areas of the four cavities of the image sequence are therefore obtained. The area change curves of the four cavities are further extracted, and the synchronous information of the four cavities is obtained. Because of the low SNR of Ultrasound images, the boundary lines of cardiac cavities are vague, so the extraction of cardiac contours is still a challenging problem. Therefore, the ASM model information is added to the traditional level set method to force the curve evolution process. According to the experimental results, the improved method improves the accuracy of the segmentation. Furthermore, based on the ventricular segmentation, the right and left ventricular systolic functions are evaluated, mainly according to the area changes. The synchronization of the four cavities of the heart is estimated based on the area changes and the volume changes.

Improved accuracy of quantitative parameter estimates in dynamic contrast-enhanced CT study with low temporal resolution

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, Sun Mo, E-mail: Sunmo.Kim@rmp.uhn.on.ca; Haider, Masoom A.; Jaffray, David A.

Purpose: A previously proposed method to reduce radiation dose to patient in dynamic contrast-enhanced (DCE) CT is enhanced by principal component analysis (PCA) filtering which improves the signal-to-noise ratio (SNR) of time-concentration curves in the DCE-CT study. The efficacy of the combined method to maintain the accuracy of kinetic parameter estimates at low temporal resolution is investigated with pixel-by-pixel kinetic analysis of DCE-CT data. Methods: The method is based on DCE-CT scanning performed with low temporal resolution to reduce the radiation dose to the patient. The arterial input function (AIF) with high temporal resolution can be generated with a coarselymore » sampled AIF through a previously published method of AIF estimation. To increase the SNR of time-concentration curves (tissue curves), first, a region-of-interest is segmented into squares composed of 3 × 3 pixels in size. Subsequently, the PCA filtering combined with a fraction of residual information criterion is applied to all the segmented squares for further improvement of their SNRs. The proposed method was applied to each DCE-CT data set of a cohort of 14 patients at varying levels of down-sampling. The kinetic analyses using the modified Tofts’ model and singular value decomposition method, then, were carried out for each of the down-sampling schemes between the intervals from 2 to 15 s. The results were compared with analyses done with the measured data in high temporal resolution (i.e., original scanning frequency) as the reference. Results: The patients’ AIFs were estimated to high accuracy based on the 11 orthonormal bases of arterial impulse responses established in the previous paper. In addition, noise in the images was effectively reduced by using five principal components of the tissue curves for filtering. Kinetic analyses using the proposed method showed superior results compared to those with down-sampling alone; they were able to maintain the accuracy in the quantitative histogram parameters of volume transfer constant [standard deviation (SD), 98th percentile, and range], rate constant (SD), blood volume fraction (mean, SD, 98th percentile, and range), and blood flow (mean, SD, median, 98th percentile, and range) for sampling intervals between 10 and 15 s. Conclusions: The proposed method of PCA filtering combined with the AIF estimation technique allows low frequency scanning for DCE-CT study to reduce patient radiation dose. The results indicate that the method is useful in pixel-by-pixel kinetic analysis of DCE-CT data for patients with cervical cancer.« less

Integrated β-catenin, BMP, PTEN, and Notch signalling patterns the nephron

PubMed Central

Lindström, Nils O; Lawrence, Melanie L; Burn, Sally F; Johansson, Jeanette A; Bakker, Elvira RM; Ridgway, Rachel A; Chang, C-Hong; Karolak, Michele J; Oxburgh, Leif; Headon, Denis J; Sansom, Owen J; Smits, Ron; Davies, Jamie A; Hohenstein, Peter

2015-01-01

The different segments of the nephron and glomerulus in the kidney balance the processes of water homeostasis, solute recovery, blood filtration, and metabolite excretion. When segment function is disrupted, a range of pathological features are presented. Little is known about nephron patterning during embryogenesis. In this study, we demonstrate that the early nephron is patterned by a gradient in β-catenin activity along the axis of the nephron tubule. By modifying β-catenin activity, we force cells within nephrons to differentiate according to the imposed β-catenin activity level, thereby causing spatial shifts in nephron segments. The β-catenin signalling gradient interacts with the BMP pathway which, through PTEN/PI3K/AKT signalling, antagonises β-catenin activity and promotes segment identities associated with low β-catenin activity. β-catenin activity and PI3K signalling also integrate with Notch signalling to control segmentation: modulating β-catenin activity or PI3K rescues segment identities normally lost by inhibition of Notch. Our data therefore identifies a molecular network for nephron patterning. DOI: http://dx.doi.org/10.7554/eLife.04000.001 PMID:25647637

Combining multi-atlas segmentation with brain surface estimation

NASA Astrophysics Data System (ADS)

Huo, Yuankai; Carass, Aaron; Resnick, Susan M.; Pham, Dzung L.; Prince, Jerry L.; Landman, Bennett A.

2016-03-01

Whole brain segmentation (with comprehensive cortical and subcortical labels) and cortical surface reconstruction are two essential techniques for investigating the human brain. The two tasks are typically conducted independently, however, which leads to spatial inconsistencies and hinders further integrated cortical analyses. To obtain self-consistent whole brain segmentations and surfaces, FreeSurfer segregates the subcortical and cortical segmentations before and after the cortical surface reconstruction. However, this "segmentation to surface to parcellation" strategy has shown limitation in various situations. In this work, we propose a novel "multi-atlas segmentation to surface" method called Multi-atlas CRUISE (MaCRUISE), which achieves self-consistent whole brain segmentations and cortical surfaces by combining multi-atlas segmentation with the cortical reconstruction method CRUISE. To our knowledge, this is the first work that achieves the reliability of state-of-the-art multi-atlas segmentation and labeling methods together with accurate and consistent cortical surface reconstruction. Compared with previous methods, MaCRUISE has three features: (1) MaCRUISE obtains 132 cortical/subcortical labels simultaneously from a single multi-atlas segmentation before reconstructing volume consistent surfaces; (2) Fuzzy tissue memberships are combined with multi-atlas segmentations to address partial volume effects; (3) MaCRUISE reconstructs topologically consistent cortical surfaces by using the sulci locations from multi-atlas segmentation. Two data sets, one consisting of five subjects with expertly traced landmarks and the other consisting of 100 volumes from elderly subjects are used for validation. Compared with CRUISE, MaCRUISE achieves self-consistent whole brain segmentation and cortical reconstruction without compromising on surface accuracy. MaCRUISE is comparably accurate to FreeSurfer while achieving greater robustness across an elderly population.

Combining Multi-atlas Segmentation with Brain Surface Estimation.

PubMed

Huo, Yuankai; Carass, Aaron; Resnick, Susan M; Pham, Dzung L; Prince, Jerry L; Landman, Bennett A

2016-02-27

Whole brain segmentation (with comprehensive cortical and subcortical labels) and cortical surface reconstruction are two essential techniques for investigating the human brain. The two tasks are typically conducted independently, however, which leads to spatial inconsistencies and hinders further integrated cortical analyses. To obtain self-consistent whole brain segmentations and surfaces, FreeSurfer segregates the subcortical and cortical segmentations before and after the cortical surface reconstruction. However, this "segmentation to surface to parcellation" strategy has shown limitations in various situations. In this work, we propose a novel "multi-atlas segmentation to surface" method called Multi-atlas CRUISE (MaCRUISE), which achieves self-consistent whole brain segmentations and cortical surfaces by combining multi-atlas segmentation with the cortical reconstruction method CRUISE. To our knowledge, this is the first work that achieves the reliability of state-of-the-art multi-atlas segmentation and labeling methods together with accurate and consistent cortical surface reconstruction. Compared with previous methods, MaCRUISE has three features: (1) MaCRUISE obtains 132 cortical/subcortical labels simultaneously from a single multi-atlas segmentation before reconstructing volume consistent surfaces; (2) Fuzzy tissue memberships are combined with multi-atlas segmentations to address partial volume effects; (3) MaCRUISE reconstructs topologically consistent cortical surfaces by using the sulci locations from multi-atlas segmentation. Two data sets, one consisting of five subjects with expertly traced landmarks and the other consisting of 100 volumes from elderly subjects are used for validation. Compared with CRUISE, MaCRUISE achieves self-consistent whole brain segmentation and cortical reconstruction without compromising on surface accuracy. MaCRUISE is comparably accurate to FreeSurfer while achieving greater robustness across an elderly population.

Kinetic magnetic resonance imaging analysis of lumbar segmental mobility in patients without significant spondylosis.

PubMed

Tan, Yanlin; Aghdasi, Bayan G; Montgomery, Scott R; Inoue, Hirokazu; Lu, Chang; Wang, Jeffrey C

2012-12-01

The purpose of this study was to examine lumbar segmental mobility using kinetic magnetic resonance imaging (MRI) in patients with minimal lumbar spondylosis. Mid-sagittal images of patients who underwent weight-bearing, multi-position kinetic MRI for symptomatic low back pain or radiculopathy were reviewed. Only patients with a Pfirrmann grade of I or II, indicating minimal disc disease, in all lumbar discs from L1-2 to L5-S1 were included for further analysis. Translational and angular motion was measured at each motion segment. The mean translational motion of the lumbar spine at each level was 1.38 mm at L1-L2, 1.41 mm at L2-L3, 1.14 mm at L3-L4, 1.10 mm at L4-L5 and 1.01 mm at L5-S1. Translational motion at L1-L2 and L2-L3 was significantly greater than L3-4, L4-L5 and L5-S1 levels (P < 0.007). The mean angular motion at each level was 7.34° at L1-L2, 8.56° at L2-L3, 8.34° at L3-L4, 8.87° at L4-L5, and 5.87° at L5-S1. The L5-S1 segment had significantly less angular motion when compared to all other levels (P < 0.006). The mean percentage contribution of each level to the total angular mobility of the lumbar spine was highest at L2-L3 (22.45 %) and least at L5/S1 (14.71 %) (P < 0.001). In the current study, we evaluated lumbar segmental mobility in patients without significant degenerative disc disease and found that translational motion was greatest in the proximal lumbar levels whereas angular motion was similar in the mid-lumbar levels but decreased at L1-L2 and L5-S1.

A modified approach combining FNEA and watershed algorithms for segmenting remotely-sensed optical images

NASA Astrophysics Data System (ADS)

Liu, Likun

2018-01-01

In the field of remote sensing image processing, remote sensing image segmentation is a preliminary step for later analysis of remote sensing image processing and semi-auto human interpretation, fully-automatic machine recognition and learning. Since 2000, a technique of object-oriented remote sensing image processing method and its basic thought prevails. The core of the approach is Fractal Net Evolution Approach (FNEA) multi-scale segmentation algorithm. The paper is intent on the research and improvement of the algorithm, which analyzes present segmentation algorithms and selects optimum watershed algorithm as an initialization. Meanwhile, the algorithm is modified by modifying an area parameter, and then combining area parameter with a heterogeneous parameter further. After that, several experiments is carried on to prove the modified FNEA algorithm, compared with traditional pixel-based method (FCM algorithm based on neighborhood information) and combination of FNEA and watershed, has a better segmentation result.

Applying the algorithm "assessing quality using image registration circuits" (AQUIRC) to multi-atlas segmentation

NASA Astrophysics Data System (ADS)

Datteri, Ryan; Asman, Andrew J.; Landman, Bennett A.; Dawant, Benoit M.

2014-03-01

Multi-atlas registration-based segmentation is a popular technique in the medical imaging community, used to transform anatomical and functional information from a set of atlases onto a new patient that lacks this information. The accuracy of the projected information on the target image is dependent on the quality of the registrations between the atlas images and the target image. Recently, we have developed a technique called AQUIRC that aims at estimating the error of a non-rigid registration at the local level and was shown to correlate to error in a simulated case. Herein, we extend upon this work by applying AQUIRC to atlas selection at the local level across multiple structures in cases in which non-rigid registration is difficult. AQUIRC is applied to 6 structures, the brainstem, optic chiasm, left and right optic nerves, and the left and right eyes. We compare the results of AQUIRC to that of popular techniques, including Majority Vote, STAPLE, Non-Local STAPLE, and Locally-Weighted Vote. We show that AQUIRC can be used as a method to combine multiple segmentations and increase the accuracy of the projected information on a target image, and is comparable to cutting edge methods in the multi-atlas segmentation field.

Reduced Abd-B Hox function during kidney development results in lineage infidelity.

PubMed

Magella, Bliss; Mahoney, Robert; Adam, Mike; Potter, S Steven

2018-06-15

Hox genes can function as key drivers of segment identity, with Hox mutations in Drosophila often resulting in dramatic homeotic transformations. In addition, however, they can serve other essential functions. In mammals, the study of Hox gene roles in development is complicated by the presence of four Hox clusters with a total of 39 genes showing extensive functional overlap. In this study, in order to better understand shared core Hox functions, we examined kidney development in mice with frameshift mutations of multiple Abd-B type Hox genes. The resulting phenotypes included dramatically reduced branching morphogenesis of the ureteric bud, premature depletion of nephron progenitors and abnormal development of the stromal compartment. Most unexpected, however, we also observed a cellular level lineage infidelity in nephron segments. Scattered cells within the proximal tubules, for example, expressed genes normally expressed only in collecting ducts. Multiple combinations of inappropriate nephron segment specific marker expression were found. In some cases, cells within a tubule showed incorrect identity, while in other cases cells showed ambiguous character, with simultaneous expression of genes associated with more than one nephron segment. These results give evidence that Hox genes have an overlapping core function at the cellular level in driving and/or maintaining correct differentiation decisions. Copyright © 2018 Elsevier Inc. All rights reserved.

Accurate segmentation framework for the left ventricle wall from cardiac cine MRI

NASA Astrophysics Data System (ADS)

Sliman, H.; Khalifa, F.; Elnakib, A.; Soliman, A.; Beache, G. M.; Gimel'farb, G.; Emam, A.; Elmaghraby, A.; El-Baz, A.

2013-10-01

We propose a novel, fast, robust, bi-directional coupled parametric deformable model to segment the left ventricle (LV) wall borders using first- and second-order visual appearance features. These features are embedded in a new stochastic external force that preserves the topology of LV wall to track the evolution of the parametric deformable models control points. To accurately estimate the marginal density of each deformable model control point, the empirical marginal grey level distributions (first-order appearance) inside and outside the boundary of the deformable model are modeled with adaptive linear combinations of discrete Gaussians (LCDG). The second order visual appearance of the LV wall is accurately modeled with a new rotationally invariant second-order Markov-Gibbs random field (MGRF). We tested the proposed segmentation approach on 15 data sets in 6 infarction patients using the Dice similarity coefficient (DSC) and the average distance (AD) between the ground truth and automated segmentation contours. Our approach achieves a mean DSC value of 0.926±0.022 and AD value of 2.16±0.60 compared to two other level set methods that achieve 0.904±0.033 and 0.885±0.02 for DSC; and 2.86±1.35 and 5.72±4.70 for AD, respectively.

Spatial gradients of protein-level time delays set the pace of the traveling segmentation clock waves

PubMed Central

Ay, Ahmet; Holland, Jack; Sperlea, Adriana; Devakanmalai, Gnanapackiam Sheela; Knierer, Stephan; Sangervasi, Sebastian; Stevenson, Angel; Özbudak, Ertuğrul M.

2014-01-01

The vertebrate segmentation clock is a gene expression oscillator controlling rhythmic segmentation of the vertebral column during embryonic development. The period of oscillations becomes longer as cells are displaced along the posterior to anterior axis, which results in traveling waves of clock gene expression sweeping in the unsegmented tissue. Although various hypotheses necessitating the inclusion of additional regulatory genes into the core clock network at different spatial locations have been proposed, the mechanism underlying traveling waves has remained elusive. Here, we combined molecular-level computational modeling and quantitative experimentation to solve this puzzle. Our model predicts the existence of an increasing gradient of gene expression time delays along the posterior to anterior direction to recapitulate spatiotemporal profiles of the traveling segmentation clock waves in different genetic backgrounds in zebrafish. We validated this prediction by measuring an increased time delay of oscillatory Her1 protein production along the unsegmented tissue. Our results refuted the need for spatial expansion of the core feedback loop to explain the occurrence of traveling waves. Spatial regulation of gene expression time delays is a novel way of creating dynamic patterns; this is the first report demonstrating such a control mechanism in any tissue and future investigations will explore the presence of analogous examples in other biological systems. PMID:25336742

Learning normalized inputs for iterative estimation in medical image segmentation.

PubMed

Drozdzal, Michal; Chartrand, Gabriel; Vorontsov, Eugene; Shakeri, Mahsa; Di Jorio, Lisa; Tang, An; Romero, Adriana; Bengio, Yoshua; Pal, Chris; Kadoury, Samuel

2018-02-01

In this paper, we introduce a simple, yet powerful pipeline for medical image segmentation that combines Fully Convolutional Networks (FCNs) with Fully Convolutional Residual Networks (FC-ResNets). We propose and examine a design that takes particular advantage of recent advances in the understanding of both Convolutional Neural Networks as well as ResNets. Our approach focuses upon the importance of a trainable pre-processing when using FC-ResNets and we show that a low-capacity FCN model can serve as a pre-processor to normalize medical input data. In our image segmentation pipeline, we use FCNs to obtain normalized images, which are then iteratively refined by means of a FC-ResNet to generate a segmentation prediction. As in other fully convolutional approaches, our pipeline can be used off-the-shelf on different image modalities. We show that using this pipeline, we exhibit state-of-the-art performance on the challenging Electron Microscopy benchmark, when compared to other 2D methods. We improve segmentation results on CT images of liver lesions, when contrasting with standard FCN methods. Moreover, when applying our 2D pipeline on a challenging 3D MRI prostate segmentation challenge we reach results that are competitive even when compared to 3D methods. The obtained results illustrate the strong potential and versatility of the pipeline by achieving accurate segmentations on a variety of image modalities and different anatomical regions. Copyright © 2017 Elsevier B.V. All rights reserved.

A knowledge-based object recognition system for applications in the space station

NASA Technical Reports Server (NTRS)

Dhawan, Atam P.

1988-01-01

A knowledge-based three-dimensional (3D) object recognition system is being developed. The system uses primitive-based hierarchical relational and structural matching for the recognition of 3D objects in the two-dimensional (2D) image for interpretation of the 3D scene. At present, the pre-processing, low-level preliminary segmentation, rule-based segmentation, and the feature extraction are completed. The data structure of the primitive viewing knowledge-base (PVKB) is also completed. Algorithms and programs based on attribute-trees matching for decomposing the segmented data into valid primitives were developed. The frame-based structural and relational descriptions of some objects were created and stored in a knowledge-base. This knowledge-base of the frame-based descriptions were developed on the MICROVAX-AI microcomputer in LISP environment. The simulated 3D scene of simple non-overlapping objects as well as real camera data of images of 3D objects of low-complexity have been successfully interpreted.

Imaging hydrothermal roots along the Endeavour segment of the Juan de Fuca ridge using elastic full waveform inversion.

NASA Astrophysics Data System (ADS)

Arnulf, A. F.; Harding, A. J.; Kent, G. M.

2016-12-01

The Endeavour segment is a 90 km-long, medium-spreading-rate, oceanic spreading center located on the northern Juan de Fuca ridge (JDFR). The central part of this segment forms a 25-km-long volcanic high that hosts five of the most hydrothermally active vent fields on the MOR system, namely (from north to south): Sasquatch, Salty Dawg, High Rise, Main Endeavour and Mothra. Mass, heat and chemical fluxes associated to vigorous hydrothermal venting are large, however the geometry of the fluid circulation system through the oceanic crust remains almost completely undefined. To produce high-resolution velocity/reflectivity structures along the axis of the Endeavour segment, here, we combined a synthetic ocean bottom experiment (SOBE), 2-D traveltime tomography, 2D elastic full waveform and reverse time migration (RTM). We present velocity and reflectivity sections along Endeavour segment at unprecedented spatial resolutions. We clearly image a set of independent, geometrically complex, elongated low-velocity regions linking the top of the magma chamber at depth to the hydrothermal vent fields on the seafloor. We interpret these narrow pipe-like units as focused regions of hydrothermal fluid up-flow, where acidic and corrosive fluids form pipe-like alteration zones as previously observed in Cyprus ophiolites. Furthermore, the amplitude of these low-velocity channels is shown to be highly variable, with the strongest velocity drops observed at Main Endeavour, Mothra and Salty Dawg hydrothermal vent fields, possibly suggesting more mature hydrothermal cells. Interestingly, the near-seafloor structure beneath those three sites is very similar and highlights a sharp lateral transition in velocity (north to south). On the other hand, the High-Rise hydrothermal vent field is characterized by several lower amplitudes up-flow zones and relatively slow near-surface velocities. Last, Sasquatch vent field is located in an area of high near-surface velocities and is not characterized by an obvious low-velocity up-flow region, in good agreement with an extinct vent field.

Subjective Ratings of Beauty and Aesthetics: Correlations With Statistical Image Properties in Western Oil Paintings

PubMed Central

Lehmann, Thomas; Redies, Christoph

2017-01-01

For centuries, oil paintings have been a major segment of the visual arts. The JenAesthetics data set consists of a large number of high-quality images of oil paintings of Western provenance from different art periods. With this database, we studied the relationship between objective image measures and subjective evaluations of the images, especially evaluations on aesthetics (defined as artistic value) and beauty (defined as individual liking). The objective measures represented low-level statistical image properties that have been associated with aesthetic value in previous research. Subjective rating scores on aesthetics and beauty correlated not only with each other but also with different combinations of the objective measures. Furthermore, we found that paintings from different art periods vary with regard to the objective measures, that is, they exhibit specific patterns of statistical image properties. In addition, clusters of participants preferred different combinations of these properties. In conclusion, the results of the present study provide evidence that statistical image properties vary between art periods and subject matters and, in addition, they correlate with the subjective evaluation of paintings by the participants. PMID:28694958

Definition of neutralizing sites on African horse sickness virus serotype 4 VP2 at the level of peptides.

PubMed

Martínez-Torrecuadrada, J L; Langeveld, J P; Meloen, R H; Casal, J I

2001-10-01

The antigenic structure of African horse sickness virus (AHSV) serotype 4 capsid protein VP2 has been determined at the peptide level by PEPSCAN analysis in combination with a large collection of polyclonal antisera and monoclonal antibodies. VP2, the determinant for the virus serotype and an important target in virus neutralization, was found to contain 15 antigenic sites. A major antigenic region containing 12 of the 15 sites was identified in the region between residues 223 and 400. A second domain between residues 568 and 681 contained the three remaining sites. These sites were used for the synthesis of peptides, which were later tested in rabbits. Of the 15 synthetic peptides, three were able to induce neutralizing antibodies for AHSV-4, defining two neutralizing epitopes, 'a' and 'b', between residues 321 and 339, and 377 and 400, respectively. A combination of peptides representing both sites induced a more effective neutralizing response. Still, the relatively low neutralization titres make the possibility of producing a synthetic vaccine for AHSV unlikely. The complex protein-protein interaction of the outer shell of the viral capsid would probably require the presence of either synthetic peptides in the correct conformation or peptide segments from the different proteins VP2, VP5 and VP7.

Segmented ion thruster

NASA Technical Reports Server (NTRS)

Brophy, John R. (Inventor)

1993-01-01

Apparatus and methods for large-area, high-power ion engines comprise dividing a single engine into a combination of smaller discharge chambers (or segments) configured to operate as a single large-area engine. This segmented ion thruster (SIT) approach enables the development of 100-kW class argon ion engines for operation at a specific impulse of 10,000 s. A combination of six 30-cm diameter ion chambers operating as a single engine can process over 100 kW. Such a segmented ion engine can be operated from a single power processor unit.

Tsunami impact to Washington and northern Oregon from segment ruptures on the southern Cascadia subduction zone

USGS Publications Warehouse

Priest, George R.; Zhang, Yinglong; Witter, Robert C.; Wang, Kelin; Goldfinger, Chris; Stimely, Laura

2014-01-01

This paper explores the size and arrival of tsunamis in Oregon and Washington from the most likely partial ruptures of the Cascadia subduction zone (CSZ) in order to determine (1) how quickly tsunami height declines away from sources, (2) evacuation time before significant inundation, and (3) extent of felt shaking that would trigger evacuation. According to interpretations of offshore turbidite deposits, the most frequent partial ruptures are of the southern CSZ. Combined recurrence of ruptures extending ~490 km from Cape Mendocino, California, to Waldport, Oregon (segment C) and ~320 km from Cape Mendocino to Cape Blanco, Oregon (segment D), is ~530 years. This recurrence is similar to frequency of full-margin ruptures on the CSZ inferred from paleoseismic data and to frequency of the largest distant tsunami sources threatening Washington and Oregon, ~Mw 9.2 earthquakes from the Gulf of Alaska. Simulated segment C and D ruptures produce relatively low-amplitude tsunamis north of source areas, even for extreme (20 m) peak slip on segment C. More than ~70 km north of segments C and D, the first tsunami arrival at the 10-m water depth has an amplitude of <1.9 m. The largest waves are trapped edge waves with amplitude ≤4.2 m that arrive ≥2 h after the earthquake. MM V–VI shaking could trigger evacuation of educated populaces as far north as Newport, Oregon for segment D events and Grays Harbor, Washington for segment C events. The NOAA and local warning systems will be the only warning at greater distances from sources.

Influenza Virus Reassortment Is Enhanced by Semi-infectious Particles but Can Be Suppressed by Defective Interfering Particles

PubMed Central

Tao, Hui; Steel, John; Lowen, Anice C.

2015-01-01

A high particle to infectivity ratio is a feature common to many RNA viruses, with ~90–99% of particles unable to initiate a productive infection under low multiplicity conditions. A recent publication by Brooke et al. revealed that, for influenza A virus (IAV), a proportion of these seemingly non-infectious particles are in fact semi-infectious. Semi-infectious (SI) particles deliver an incomplete set of viral genes to the cell, and therefore cannot support a full cycle of replication unless complemented through co-infection. In addition to SI particles, IAV populations often contain defective-interfering (DI) particles, which actively interfere with production of infectious progeny. With the aim of understanding the significance to viral evolution of these incomplete particles, we tested the hypothesis that SI and DI particles promote diversification through reassortment. Our approach combined computational simulations with experimental determination of infection, co-infection and reassortment levels following co-inoculation of cultured cells with two distinct influenza A/Panama/2007/99 (H3N2)-based viruses. Computational results predicted enhanced reassortment at a given % infection or multiplicity of infection with increasing semi-infectious particle content. Comparison of experimental data to the model indicated that the likelihood that a given segment is missing varies among the segments and that most particles fail to deliver ≥1 segment. To verify the prediction that SI particles augment reassortment, we performed co-infections using viruses exposed to low dose UV. As expected, the introduction of semi-infectious particles with UV-induced lesions enhanced reassortment. In contrast to SI particles, inclusion of DI particles in modeled virus populations could not account for observed reassortment outcomes. DI particles were furthermore found experimentally to suppress detectable reassortment, relative to that seen with standard virus stocks, most likely by interfering with production of infectious progeny from co-infected cells. These data indicate that semi-infectious particles increase the rate of reassortment and may therefore accelerate adaptive evolution of IAV. PMID:26440404

Association of educational level with delay of prehospital care before reperfusion in STEMI.

PubMed

Heo, Ju Yeon; Hong, Ki Jeong; Shin, Sang Do; Song, Kyoung Jun; Ro, Young Sun

2015-12-01

Rapid access to reperfusion is important in ST-segment elevation myocardial infarction (STEMI). The goal of this study is to assess the association of the educational level of patients with STEMI and prehospital and inhospital delay before reperfusion. We used a nationwide database of 31 emergency departments for cardiovascular disease surveillance operated by the Korean Centers for Disease Control and Prevention. ST-segment elevation myocardial infarction cases registered from November 2007 to December 2012 were enrolled. Cases younger than 18 years old or missing educational history were excluded. Educational level was characterized as low (completion of elementary school or less), medium (completion of middle or high school), and high (completion of a bachelor's degree or higher). We compared prehospital and inhospital delay for 3 educational groups. A general linear regression was used to assess the association of educational level with the time from symptom to hospital arrival. A total of 9028 patients with STEMI were included. The median time from symptom to hospital arrival was 144, 76, and 68 minutes in the low, medium, and high education groups, respectively (P < .01). Educational level had no significant effect on the door-to-balloon time. Low and medium education groups experienced significant delays of symptom to hospital to high education group (low: β = 27.2 minutes; 95% confidence interval, 15.9-38.5; medium: β = 19.1 minutes; 95% confidence interval, 15.9-38.5). In patients with STEMI, the time from symptom to hospital arrival increased as educational level decreased, but educational level had no significant association with the inhospital delay to reperfusion. Copyright © 2015 Elsevier Inc. All rights reserved.

Revisiting the segmental organization of the human spinal cord.

PubMed

Leijnse, J N; D'Herde, K

2016-09-01

In classic anatomic atlases, the spinal cord is standardly represented in its anatomical form with symmetrically emerging anterior and posterior roots, which at the level of the intervertebral foramen combine into the spinal nerves. The parts of the cord delimited by the boundaries of the roots are called segments or myelomeres. Associated with their regular repetitive appearance is the notion that the cord is segmentally organized. This segmental view is reinforced by clinical practice. Spinal cord roots innervate specific body parts. The level of cord trauma is diagnosed by the de-innervation symptoms of these parts. However, systemically, the case for a segmentally organized cord is not so clear. To date, developmental and genetic research points to a regionally rather than a segmentally organized cord. In the present study, to what degree the fila radicularia are segmentally implanted along the cord was investigated. The research hypothesis was that if the fila radicularia were non-segmentally implanted at the cord surface, it would be unlikely that the internal neuron stratum would be segmented. The visual segmented aspect of the myelomeres would then be the consequence of the necessary bundling of axons towards the vertebral foramen as the only exits of the vertebral canal, rather than of an underlying segment organization of the cord itself. To investigate the research hypothesis, the fila radicularia in the cervical-upper thoracic part of five spinal cords were detached from their spinal nerves and dissected in detail. The principal research question was if the fila radicularia are separated from their spinal nerves and dissected from their connective tissues up to the cord, would it be possible to reconstruct the original spinal segments from the morphology and interspaces of the fila? The dissections revealed that the anterior fila radicularia emerge from the cord at regular regionally modulated interspaces without systematic segmental delineations. The posterior fila radicularia are somewhat more segmentally implanted, but the pattern is individually inconsistent. The posterior and anterior roots have notable morphological differences, and hypotheses are presented to help explain these. The macroscopic observations are consistent with a regionally but not a segmentally organized cord. This conclusion was visually summarized in photographs of spinal cords with ipsilateral intact roots and contralateral individually dissected fila radicularia. It was suggested that this dual view of the spinal cord be added to the standard anatomic textbooks to counterbalance the current possibly biased view of a segmented cord. © 2016 Anatomical Society.

JANUS — A setup for low-energy Coulomb excitation at ReA3

DOE PAGES

Lunderberg, E.; Belarge, J.; Bender, P. C.; ...

2017-12-21

We report that a new experimental setup for low-energy Coulomb excitation experiments was constructed in a collaboration between the National Superconducting Cyclotron Laboratory (NSCL), Lawrence Livermore National Laboratory (LLNL), and the University of Rochester and was commissioned at the general purpose beam line of NSCL's ReA3 reaccelerator facility. The so-called JANUS setup combines γ-ray detection with the Segmented Ge Array (SeGA) and scattered particle detection using a pair of segmented double-sided Si detectors (Bambino 2). The low-energy Coulomb excitation program that JANUS enables will complement intermediate-energy Coulomb excitation studies that have long been performed at NSCL by providing access tomore » observables that quantify collectivity beyond the first excited state, including the sign and magnitude of excited-state quadrupole moments. Here, in this work, the setup and its performance will be described based on the commissioning run that used stable 78Kr impinging onto a 1.09 mg/cm 2 208Pb target at a beam energy of 3.9 MeV/u.« less

The influence of predegenerated nerve grafts on axonal regeneration from prelesioned peripheral nerves.

PubMed

Hasan, N A; Neumann, M M; de Souky, M A; So, K F; Bedi, K S

1996-10-01

Recent in vitro work has indicated that predegenerated segments of peripheral nerve are more capable of supporting neurite growth from adult neurons than fresh segments of nerve, whereas previous in vivo studies which investigated whether predegenerated nerve segments used as grafts are capable of enhancing axonal regeneration produced conflicting results. We have reinvestigated this question by using predegenerated nerve grafts in combination with conditioning lesions of the host nerve to determine the optimal conditions for obtaining the maximal degree of regeneration of myelinated axons. The sciatic nerve of adult Dark Agouti rats were sectioned at midthigh level, and the distal portion was allowed to predegenerate for 0, 6 or 12 d in situ. 10-15 mm lengths of these distal nerve segments were then syngenically grafted onto the central stumps of sciatic nerves which had themselves received a conditioning lesion 0, 6, and 12 d previously, making a total of 9 different donor-host combinations. The grafts were assessed histologically 3 or 8 wk after grafting. Axonal regeneration in the 9 different donor-host combinations was determined by counting the numbers of myelinated axons in transverse sections through the grafts. All grafts examined contained regenerating myelinated axons. The rats given a 3 wk postgrafting survival period had an average of between 1400 and 5300 such axons. The rats given an 8 wk postgrafting survival period had between about 13,000 and 25,000 regenerating myelinated axons. Analysis of variance revealed significant main effects for both the Donor and Host conditions as well as Weeks (i.e. survival period after grafting). These results indicate that both a conditioning lesion of the host neurons and the degree of predegeneration of peripheral nerve segments to be used as grafts are of importance in influencing the degree of axonal regeneration. Of these 2 factors the conditioning lesion of the host appears to have the greater effect on the final number of regenerating myelinated axons.

Development of Image Segmentation Methods for Intracranial Aneurysms

PubMed Central

Qian, Yi; Morgan, Michael

2013-01-01

Though providing vital means for the visualization, diagnosis, and quantification of decision-making processes for the treatment of vascular pathologies, vascular segmentation remains a process that continues to be marred by numerous challenges. In this study, we validate eight aneurysms via the use of two existing segmentation methods; the Region Growing Threshold and Chan-Vese model. These methods were evaluated by comparison of the results obtained with a manual segmentation performed. Based upon this validation study, we propose a new Threshold-Based Level Set (TLS) method in order to overcome the existing problems. With divergent methods of segmentation, we discovered that the volumes of the aneurysm models reached a maximum difference of 24%. The local artery anatomical shapes of the aneurysms were likewise found to significantly influence the results of these simulations. In contrast, however, the volume differences calculated via use of the TLS method remained at a relatively low figure, at only around 5%, thereby revealing the existence of inherent limitations in the application of cerebrovascular segmentation. The proposed TLS method holds the potential for utilisation in automatic aneurysm segmentation without the setting of a seed point or intensity threshold. This technique will further enable the segmentation of anatomically complex cerebrovascular shapes, thereby allowing for more accurate and efficient simulations of medical imagery. PMID:23606905

Traffic-related trace elements in soils along six highway segments on the Tibetan Plateau: Influence factors and spatial variation.

PubMed

Wang, Guanxing; Zeng, Chen; Zhang, Fan; Zhang, Yili; Scott, Christopher A; Yan, Xuedong

2017-03-01

The accumulation of traffic-related trace elements in soil as the result of anthropogenic activities raises serious concerns about environmental pollution and public health. Traffic is the main source of trace elements in roadside soil on the Tibetan Plateau, an area otherwise devoid of industrial emissions. Indeed, the rapid development of tourism and transportation in this region means it is becoming increasingly important to identify the accumulation levels, influence distance, spatial distribution, and other relevant factors influencing trace elements. In this study, 229 soil samples along six segments of the major transportation routes on the Tibetan Plateau (highways G214, S308, and G109), were collected for analysis of eight trace elements (Cr, Co, Ni, As, Cu, Zn, Cd, and Pb). The results of statistical analyses showed that of the eight trace elements in soils, Cu, Zn, Cd, and Pb were primarily derived from traffic. The relationship between the trace element accumulation levels and the distance from the roadside followed an exponential decline, with the exception of Segment 3, the only unpaved gravel road studied. In addition, the distance of influence from the roadside varied by trace element and segment, ranging from 16m to 144m. Background values for each segment were different because of soil heterogeneity, while a number of other potential influencing factors (including traffic volume, road surface material, roadside distance, land cover, terrain, and altitude) all had significant effects on trace-element concentrations. Overall, however, concentrations along most of the road segments investigated were at, or below, levels defined as low on the Nemero Synthesis index. Copyright © 2017 Elsevier B.V. All rights reserved.

Removal of a putative inhibitory element reduces the calcium-dependent calmodulin activation of neuronal nitric-oxide synthase.

PubMed

Montgomery, H J; Romanov, V; Guillemette, J G

2000-02-18

Neuronal nitric-oxide synthase (NOS) and endothelial NOS are constitutive NOS isoforms that are activated by binding calmodulin in response to elevated intracellular calcium. In contrast, the inducible NOS isoform binds calmodulin at low basal levels of calcium in resting cells. Primary sequence comparisons show that each constitutive NOS isozyme contains a polypeptide segment within its reductase domain, which is absent in the inducible NOS enzyme. To study a possible link between the presence of these additional polypeptide segments in constitutive NOS enzymes and their calcium-dependent calmodulin activation, three deletion mutants were created. The putative inhibitory insert was removed from the FMN binding regions of the neuronal NOS holoenzyme and from two truncated neuronal NOS reductase enzymes in which the calmodulin binding region was either included or deleted. All three mutant enzymes showed reduced incorporation of FMN and required reconstitution with exogenous FMN for activity. The combined removal of both the calmodulin binding domain and the putative inhibitory insert did not result in a calmodulin-independent neuronal NOS reductase. Thus, although the putative inhibitory element has an effect on the calcium-dependent calmodulin activation of neuronal NOS, it does not have the properties of the typical autoinhibitory domain found in calmodulin-activated enzymes.

Room-temperature InP/InAsP Quantum Discs-in-Nanowire Infrared Photodetectors.

PubMed

Karimi, Mohammad; Jain, Vishal; Heurlin, Magnus; Nowzari, Ali; Hussain, Laiq; Lindgren, David; Stehr, Jan Eric; Buyanova, Irina A; Gustafsson, Anders; Samuelson, Lars; Borgström, Magnus T; Pettersson, Håkan

2017-06-14

The possibility to engineer nanowire heterostructures with large bandgap variations is particularly interesting for technologically important broadband photodetector applications. Here we report on a combined study of design, fabrication, and optoelectronic properties of infrared photodetectors comprising four million n + -i-n + InP nanowires periodically ordered in arrays. The nanowires were grown by metal-organic vapor phase epitaxy on InP substrates, with either a single or 20 InAsP quantum discs embedded in the i-segment. By Zn compensation of the residual n-dopants in the i-segment, the room-temperature dark current is strongly suppressed to a level of pA/NW at 1 V bias. The low dark current is manifested in the spectrally resolved photocurrent measurements, which reveal strong photocurrent contributions from the InAsP quantum discs at room temperature with a threshold wavelength of about 2.0 μm and a bias-tunable responsivity reaching 7 A/W@1.38 μm at 2 V bias. Two different processing schemes were implemented to study the effects of radial self-gating in the nanowires induced by the nanowire/SiO x /ITO wrap-gate geometry. Summarized, our results show that properly designed axial InP/InAsP nanowire heterostructures are promising candidates for broadband photodetectors.

Sampling-based ensemble segmentation against inter-operator variability

NASA Astrophysics Data System (ADS)

Huo, Jing; Okada, Kazunori; Pope, Whitney; Brown, Matthew

2011-03-01

Inconsistency and a lack of reproducibility are commonly associated with semi-automated segmentation methods. In this study, we developed an ensemble approach to improve reproducibility and applied it to glioblastoma multiforme (GBM) brain tumor segmentation on T1-weigted contrast enhanced MR volumes. The proposed approach combines samplingbased simulations and ensemble segmentation into a single framework; it generates a set of segmentations by perturbing user initialization and user-specified internal parameters, then fuses the set of segmentations into a single consensus result. Three combination algorithms were applied: majority voting, averaging and expectation-maximization (EM). The reproducibility of the proposed framework was evaluated by a controlled experiment on 16 tumor cases from a multicenter drug trial. The ensemble framework had significantly better reproducibility than the individual base Otsu thresholding method (p<.001).

Combining heterogenous features for 3D hand-held object recognition

NASA Astrophysics Data System (ADS)

Lv, Xiong; Wang, Shuang; Li, Xiangyang; Jiang, Shuqiang

2014-10-01

Object recognition has wide applications in the area of human-machine interaction and multimedia retrieval. However, due to the problem of visual polysemous and concept polymorphism, it is still a great challenge to obtain reliable recognition result for the 2D images. Recently, with the emergence and easy availability of RGB-D equipment such as Kinect, this challenge could be relieved because the depth channel could bring more information. A very special and important case of object recognition is hand-held object recognition, as hand is a straight and natural way for both human-human interaction and human-machine interaction. In this paper, we study the problem of 3D object recognition by combining heterogenous features with different modalities and extraction techniques. For hand-craft feature, although it reserves the low-level information such as shape and color, it has shown weakness in representing hiconvolutionalgh-level semantic information compared with the automatic learned feature, especially deep feature. Deep feature has shown its great advantages in large scale dataset recognition but is not always robust to rotation or scale variance compared with hand-craft feature. In this paper, we propose a method to combine hand-craft point cloud features and deep learned features in RGB and depth channle. First, hand-held object segmentation is implemented by using depth cues and human skeleton information. Second, we combine the extracted hetegerogenous 3D features in different stages using linear concatenation and multiple kernel learning (MKL). Then a training model is used to recognize 3D handheld objects. Experimental results validate the effectiveness and gerneralization ability of the proposed method.

Pulmonary vessel segmentation utilizing curved planar reformation and optimal path finding (CROP) in computed tomographic pulmonary angiography (CTPA) for CAD applications

NASA Astrophysics Data System (ADS)

Zhou, Chuan; Chan, Heang-Ping; Kuriakose, Jean W.; Chughtai, Aamer; Wei, Jun; Hadjiiski, Lubomir M.; Guo, Yanhui; Patel, Smita; Kazerooni, Ella A.

2012-03-01

Vessel segmentation is a fundamental step in an automated pulmonary embolism (PE) detection system. The purpose of this study is to improve the segmentation scheme for pulmonary vessels affected by PE and other lung diseases. We have developed a multiscale hierarchical vessel enhancement and segmentation (MHES) method for pulmonary vessel tree extraction based on the analysis of eigenvalues of Hessian matrices. However, it is difficult to segment the pulmonary vessels accurately under suboptimal conditions, such as vessels occluded by PEs, surrounded by lymphoid tissues or lung diseases, and crossing with other vessels. In this study, we developed a new vessel refinement method utilizing curved planar reformation (CPR) technique combined with optimal path finding method (MHES-CROP). The MHES segmented vessels straightened in the CPR volume was refined using adaptive gray level thresholding where the local threshold was obtained from least-square estimation of a spline curve fitted to the gray levels of the vessel along the straightened volume. An optimal path finding method based on Dijkstra's algorithm was finally used to trace the correct path for the vessel of interest. Two and eight CTPA scans were randomly selected as training and test data sets, respectively. Forty volumes of interest (VOIs) containing "representative" vessels were manually segmented by a radiologist experienced in CTPA interpretation and used as reference standard. The results show that, for the 32 test VOIs, the average percentage volume error relative to the reference standard was improved from 32.9+/-10.2% using the MHES method to 9.9+/-7.9% using the MHES-CROP method. The accuracy of vessel segmentation was improved significantly (p<0.05). The intraclass correlation coefficient (ICC) of the segmented vessel volume between the automated segmentation and the reference standard was improved from 0.919 to 0.988. Quantitative comparison of the MHES method and the MHES-CROP method with the reference standard was also evaluated by the Bland-Altman plot. This preliminary study indicates that the MHES-CROP method has the potential to improve PE detection.

Segmentation of breast ultrasound images based on active contours using neutrosophic theory.

PubMed

Lotfollahi, Mahsa; Gity, Masoumeh; Ye, Jing Yong; Mahlooji Far, A

2018-04-01

Ultrasound imaging is an effective approach for diagnosing breast cancer, but it is highly operator-dependent. Recent advances in computer-aided diagnosis have suggested that it can assist physicians in diagnosis. Definition of the region of interest before computer analysis is still needed. Since manual outlining of the tumor contour is tedious and time-consuming for a physician, developing an automatic segmentation method is important for clinical application. The present paper represents a novel method to segment breast ultrasound images. It utilizes a combination of region-based active contour and neutrosophic theory to overcome the natural properties of ultrasound images including speckle noise and tissue-related textures. First, due to inherent speckle noise and low contrast of these images, we have utilized a non-local means filter and fuzzy logic method for denoising and image enhancement, respectively. This paper presents an improved weighted region-scalable active contour to segment breast ultrasound images using a new feature derived from neutrosophic theory. This method has been applied to 36 breast ultrasound images. It generates true-positive and false-positive results, and similarity of 95%, 6%, and 90%, respectively. The purposed method indicates clear advantages over other conventional methods of active contour segmentation, i.e., region-scalable fitting energy and weighted region-scalable fitting energy.

Thermoplastic Polyurethanes with Isosorbide Chain Extender

DOE Office of Scientific and Technical Information (OSTI.GOV)

Javni, Ivan; Bilic, Olivera; Bilic, Nikola

2015-12-15

Isosorbide, a renewable diol derived from starch, was used alone or in combination with butane diol (BD) as the chain extender in two series of thermoplastic polyurethanes (TPU) with 50 and 70% polytetramethylene ether glycol (PTMEG) soft segment concentration (SSC), respectively. In the synthesized TPUs, the hard segment composition was systematically varied in both series following BD/isosorbide molar ratios of 100 : 0; 75 : 25; 50 : 50; 25 : 75, and 0 : 100 to examine in detail the effect of chain extenders on properties of segmented polyurethane elastomers with different morphologies. We found that polyurethanes with 50%more » SSC were hard elastomers with Shore D hardness of around 50, which is consistent with assumed co-continuous morphology. Polymers with 70% SSC displayed lower Shore A hardness of 74–79 (Shore D around 25) as a result of globular hard domains dispersed in the soft matrix. Insertion of isosorbide increased rigidity, melting point and glass transition temperature of hard segments and tensile strength of elastomers with 50% SSC. These effects were weaker or non-existent in 70% SSC series due to the short hard segments and low content of isosorbide. We also found that the thermal stability was lowered by increasing isosorbide content in both series.« less

Mass balance modelling of contaminants in river basins: a flexible matrix approach.

PubMed

Warren, Christopher; Mackay, Don; Whelan, Mick; Fox, Kay

2005-12-01

A novel and flexible approach is described for simulating the behaviour of chemicals in river basins. A number (n) of river reaches are defined and their connectivity is described by entries in an n x n matrix. Changes in segmentation can be readily accommodated by altering the matrix entries, without the need for model revision. Two models are described. The simpler QMX-R model only considers advection and an overall loss due to the combined processes of volatilization, net transfer to sediment and degradation. The rate constant for the overall loss is derived from fugacity calculations for a single segment system. The more rigorous QMX-F model performs fugacity calculations for each segment and explicitly includes the processes of advection, evaporation, water-sediment exchange and degradation in both water and sediment. In this way chemical exposure in all compartments (including equilibrium concentrations in biota) can be estimated. Both models are designed to serve as intermediate-complexity exposure assessment tools for river basins with relatively low data requirements. By considering the spatially explicit nature of emission sources and the changes in concentration which occur with transport in the channel system, the approach offers significant advantages over simple one-segment simulations while being more readily applicable than more sophisticated, highly segmented, GIS-based models.

Affective video retrieval: violence detection in Hollywood movies by large-scale segmental feature extraction.

PubMed

Eyben, Florian; Weninger, Felix; Lehment, Nicolas; Schuller, Björn; Rigoll, Gerhard

2013-01-01

Without doubt general video and sound, as found in large multimedia archives, carry emotional information. Thus, audio and video retrieval by certain emotional categories or dimensions could play a central role for tomorrow's intelligent systems, enabling search for movies with a particular mood, computer aided scene and sound design in order to elicit certain emotions in the audience, etc. Yet, the lion's share of research in affective computing is exclusively focusing on signals conveyed by humans, such as affective speech. Uniting the fields of multimedia retrieval and affective computing is believed to lend to a multiplicity of interesting retrieval applications, and at the same time to benefit affective computing research, by moving its methodology "out of the lab" to real-world, diverse data. In this contribution, we address the problem of finding "disturbing" scenes in movies, a scenario that is highly relevant for computer-aided parental guidance. We apply large-scale segmental feature extraction combined with audio-visual classification to the particular task of detecting violence. Our system performs fully data-driven analysis including automatic segmentation. We evaluate the system in terms of mean average precision (MAP) on the official data set of the MediaEval 2012 evaluation campaign's Affect Task, which consists of 18 original Hollywood movies, achieving up to .398 MAP on unseen test data in full realism. An in-depth analysis of the worth of individual features with respect to the target class and the system errors is carried out and reveals the importance of peak-related audio feature extraction and low-level histogram-based video analysis.

Affective Video Retrieval: Violence Detection in Hollywood Movies by Large-Scale Segmental Feature Extraction

PubMed Central

Eyben, Florian; Weninger, Felix; Lehment, Nicolas; Schuller, Björn; Rigoll, Gerhard

2013-01-01

Without doubt general video and sound, as found in large multimedia archives, carry emotional information. Thus, audio and video retrieval by certain emotional categories or dimensions could play a central role for tomorrow's intelligent systems, enabling search for movies with a particular mood, computer aided scene and sound design in order to elicit certain emotions in the audience, etc. Yet, the lion's share of research in affective computing is exclusively focusing on signals conveyed by humans, such as affective speech. Uniting the fields of multimedia retrieval and affective computing is believed to lend to a multiplicity of interesting retrieval applications, and at the same time to benefit affective computing research, by moving its methodology “out of the lab” to real-world, diverse data. In this contribution, we address the problem of finding “disturbing” scenes in movies, a scenario that is highly relevant for computer-aided parental guidance. We apply large-scale segmental feature extraction combined with audio-visual classification to the particular task of detecting violence. Our system performs fully data-driven analysis including automatic segmentation. We evaluate the system in terms of mean average precision (MAP) on the official data set of the MediaEval 2012 evaluation campaign's Affect Task, which consists of 18 original Hollywood movies, achieving up to .398 MAP on unseen test data in full realism. An in-depth analysis of the worth of individual features with respect to the target class and the system errors is carried out and reveals the importance of peak-related audio feature extraction and low-level histogram-based video analysis. PMID:24391704

An improved wavelet neural network medical image segmentation algorithm with combined maximum entropy

NASA Astrophysics Data System (ADS)

Hu, Xiaoqian; Tao, Jinxu; Ye, Zhongfu; Qiu, Bensheng; Xu, Jinzhang

2018-05-01

In order to solve the problem of medical image segmentation, a wavelet neural network medical image segmentation algorithm based on combined maximum entropy criterion is proposed. Firstly, we use bee colony algorithm to optimize the network parameters of wavelet neural network, get the parameters of network structure, initial weights and threshold values, and so on, we can quickly converge to higher precision when training, and avoid to falling into relative extremum; then the optimal number of iterations is obtained by calculating the maximum entropy of the segmented image, so as to achieve the automatic and accurate segmentation effect. Medical image segmentation experiments show that the proposed algorithm can reduce sample training time effectively and improve convergence precision, and segmentation effect is more accurate and effective than traditional BP neural network (back propagation neural network : a multilayer feed forward neural network which trained according to the error backward propagation algorithm.

Measurements agreement between low-cost and high-level handheld 3D scanners to scan the knee for designing a 3D printed knee brace

PubMed Central

2018-01-01

Use of additive manufacturing is growing rapidly in the orthotics field. This technology allows orthotics to be designed directly on digital scans of limbs. However, little information is available about scanners and 3D scans. The aim of this study is to look at the agreement between manual measurements, high-level and low-cost handheld 3D scanners. We took two manual measurements and three 3D scans with each scanner from 14 lower limbs. The lower limbs were divided into 17 sections of 30mm each from 180mm above the mid-patella to 300mm below. Time to record and to process the three 3D scans for scanners methods were compared with Student t-test while Bland-Altman plots were used to study agreement between circumferences of each section from the three methods. The record time was 97s shorter with high-level scanner than with the low-cost (p = .02) while the process time was nine times quicker with the low-cost scanner (p < .01). An overestimation of 2.5mm was found in high-level scanner compared to manual measurement, but with a better repeatability between measurements. The low-cost scanner tended to overestimate the circumferences from 0.1% to 1.5%, overestimation being greater for smaller circumferences. In conclusion, 3D scanners provide more information about the shape of the lower limb, but the reliability depends on the 3D scanner and the size of the scanned segment. Low-cost scanners could be useful for clinicians because of the simple and fast process, but attention should be focused on accuracy, which depends on the scanned body segment. PMID:29320560

Measurements agreement between low-cost and high-level handheld 3D scanners to scan the knee for designing a 3D printed knee brace.

PubMed

Dessery, Yoann; Pallari, Jari

2018-01-01

Use of additive manufacturing is growing rapidly in the orthotics field. This technology allows orthotics to be designed directly on digital scans of limbs. However, little information is available about scanners and 3D scans. The aim of this study is to look at the agreement between manual measurements, high-level and low-cost handheld 3D scanners. We took two manual measurements and three 3D scans with each scanner from 14 lower limbs. The lower limbs were divided into 17 sections of 30mm each from 180mm above the mid-patella to 300mm below. Time to record and to process the three 3D scans for scanners methods were compared with Student t-test while Bland-Altman plots were used to study agreement between circumferences of each section from the three methods. The record time was 97s shorter with high-level scanner than with the low-cost (p = .02) while the process time was nine times quicker with the low-cost scanner (p < .01). An overestimation of 2.5mm was found in high-level scanner compared to manual measurement, but with a better repeatability between measurements. The low-cost scanner tended to overestimate the circumferences from 0.1% to 1.5%, overestimation being greater for smaller circumferences. In conclusion, 3D scanners provide more information about the shape of the lower limb, but the reliability depends on the 3D scanner and the size of the scanned segment. Low-cost scanners could be useful for clinicians because of the simple and fast process, but attention should be focused on accuracy, which depends on the scanned body segment.

Localized Principal Component Analysis based Curve Evolution: A Divide and Conquer Approach

PubMed Central

Appia, Vikram; Ganapathy, Balaji; Yezzi, Anthony; Faber, Tracy

2014-01-01

We propose a novel localized principal component analysis (PCA) based curve evolution approach which evolves the segmenting curve semi-locally within various target regions (divisions) in an image and then combines these locally accurate segmentation curves to obtain a global segmentation. The training data for our approach consists of training shapes and associated auxiliary (target) masks. The masks indicate the various regions of the shape exhibiting highly correlated variations locally which may be rather independent of the variations in the distant parts of the global shape. Thus, in a sense, we are clustering the variations exhibited in the training data set. We then use a parametric model to implicitly represent each localized segmentation curve as a combination of the local shape priors obtained by representing the training shapes and the masks as a collection of signed distance functions. We also propose a parametric model to combine the locally evolved segmentation curves into a single hybrid (global) segmentation. Finally, we combine the evolution of these semilocal and global parameters to minimize an objective energy function. The resulting algorithm thus provides a globally accurate solution, which retains the local variations in shape. We present some results to illustrate how our approach performs better than the traditional approach with fully global PCA. PMID:25520901

Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field.

PubMed

Nie, Jingxin; Xue, Zhong; Liu, Tianming; Young, Geoffrey S; Setayesh, Kian; Guo, Lei; Wong, Stephen T C

2009-09-01

A variety of algorithms have been proposed for brain tumor segmentation from multi-channel sequences, however, most of them require isotropic or pseudo-isotropic resolution of the MR images. Although co-registration and interpolation of low-resolution sequences, such as T2-weighted images, onto the space of the high-resolution image, such as T1-weighted image, can be performed prior to the segmentation, the results are usually limited by partial volume effects due to interpolation of low-resolution images. To improve the quality of tumor segmentation in clinical applications where low-resolution sequences are commonly used together with high-resolution images, we propose the algorithm based on Spatial accuracy-weighted Hidden Markov random field and Expectation maximization (SHE) approach for both automated tumor and enhanced-tumor segmentation. SHE incorporates the spatial interpolation accuracy of low-resolution images into the optimization procedure of the Hidden Markov Random Field (HMRF) to segment tumor using multi-channel MR images with different resolutions, e.g., high-resolution T1-weighted and low-resolution T2-weighted images. In experiments, we evaluated this algorithm using a set of simulated multi-channel brain MR images with known ground-truth tissue segmentation and also applied it to a dataset of MR images obtained during clinical trials of brain tumor chemotherapy. The results show that more accurate tumor segmentation results can be obtained by comparing with conventional multi-channel segmentation algorithms.

Measured noise reductions resulting from modified approach procedures for business jet aircraft

NASA Technical Reports Server (NTRS)

Burcham, F. W., Jr.; Putnam, T. W.; Lasagna, P. L.; Parish, O. O.

1975-01-01

Five business jet airplanes were flown to determine the noise reductions that result from the use of modified approach procedures. The airplanes tested were a Gulfstream 2, JetStar, Hawker Siddeley 125-400, Sabreliner-60 and LearJet-24. Noise measurements were made 3, 5, and 7 nautical miles from the touchdown point. In addition to a standard 3 deg glide slope approach, a 4 deg glide slope approach, a 3 deg glide slope approach in a low-drag configuration, and a two-segment approach were flown. It was found that the 4 deg approach was about 4 EPNdB quieter than the standard 3 deg approach. Noise reductions for the low-drag 3 deg approach varied widely among the airplanes tested, with an average of 8.5 EPNdB on a fleet-weighted basis. The two-segment approach resulted in noise reductions of 7 to 8 EPNdB at 3 and 5 nautical miles from touchdown, but only 3 EPNdB at 7 nautical miles from touchdown when the airplanes were still in level flight prior to glide slope intercept. Pilot ratings showed progressively increasing workload for the 4 deg, low-drag 3 deg, and two-segment approaches.

Texture segmentation of non-cooperative spacecrafts images based on wavelet and fractal dimension

NASA Astrophysics Data System (ADS)

Wu, Kanzhi; Yue, Xiaokui

2011-06-01

With the increase of on-orbit manipulations and space conflictions, missions such as tracking and capturing the target spacecrafts are aroused. Unlike cooperative spacecrafts, fixing beacons or any other marks on the targets is impossible. Due to the unknown shape and geometry features of non-cooperative spacecraft, in order to localize the target and obtain the latitude, we need to segment the target image and recognize the target from the background. The data and errors during the following procedures such as feature extraction and matching can also be reduced. Multi-resolution analysis of wavelet theory reflects human beings' recognition towards images from low resolution to high resolution. In addition, spacecraft is the only man-made object in the image compared to the natural background and the differences will be certainly observed between the fractal dimensions of target and background. Combined wavelet transform and fractal dimension, in this paper, we proposed a new segmentation algorithm for the images which contains complicated background such as the universe and planet surfaces. At first, Daubechies wavelet basis is applied to decompose the image in both x axis and y axis, thus obtain four sub-images. Then, calculate the fractal dimensions in four sub-images using different methods; after analyzed the results of fractal dimensions in sub-images, we choose Differential Box Counting in low resolution image as the principle to segment the texture which has the greatest divergences between different sub-images. This paper also presents the results of experiments by using the algorithm above. It is demonstrated that an accurate texture segmentation result can be obtained using the proposed technique.

The validation index: a new metric for validation of segmentation algorithms using two or more expert outlines with application to radiotherapy planning.

PubMed

Juneja, Prabhjot; Evans, Philp M; Harris, Emma J

2013-08-01

Validation is required to ensure automated segmentation algorithms are suitable for radiotherapy target definition. In the absence of true segmentation, algorithmic segmentation is validated against expert outlining of the region of interest. Multiple experts are used to overcome inter-expert variability. Several approaches have been studied in the literature, but the most appropriate approach to combine the information from multiple expert outlines, to give a single metric for validation, is unclear. None consider a metric that can be tailored to case-specific requirements in radiotherapy planning. Validation index (VI), a new validation metric which uses experts' level of agreement was developed. A control parameter was introduced for the validation of segmentations required for different radiotherapy scenarios: for targets close to organs-at-risk and for difficult to discern targets, where large variation between experts is expected. VI was evaluated using two simulated idealized cases and data from two clinical studies. VI was compared with the commonly used Dice similarity coefficient (DSCpair - wise) and found to be more sensitive than the DSCpair - wise to the changes in agreement between experts. VI was shown to be adaptable to specific radiotherapy planning scenarios.

Segmentation of bone structures in 3D CT images based on continuous max-flow optimization

NASA Astrophysics Data System (ADS)

Pérez-Carrasco, J. A.; Acha-Piñero, B.; Serrano, C.

2015-03-01

In this paper an algorithm to carry out the automatic segmentation of bone structures in 3D CT images has been implemented. Automatic segmentation of bone structures is of special interest for radiologists and surgeons to analyze bone diseases or to plan some surgical interventions. This task is very complicated as bones usually present intensities overlapping with those of surrounding tissues. This overlapping is mainly due to the composition of bones and to the presence of some diseases such as Osteoarthritis, Osteoporosis, etc. Moreover, segmentation of bone structures is a very time-consuming task due to the 3D essence of the bones. Usually, this segmentation is implemented manually or with algorithms using simple techniques such as thresholding and thus providing bad results. In this paper gray information and 3D statistical information have been combined to be used as input to a continuous max-flow algorithm. Twenty CT images have been tested and different coefficients have been computed to assess the performance of our implementation. Dice and Sensitivity values above 0.91 and 0.97 respectively were obtained. A comparison with Level Sets and thresholding techniques has been carried out and our results outperformed them in terms of accuracy.

Thermal inactivation of Escherichia coli O157:H7 in blade-tenderized beef steaks cooked on a commercial open-flame gas grill.

PubMed

Luchansky, John B; Porto-Fett, Anna C S; Shoyer, Bradley; Phebus, Randall K; Thippareddi, Harshavardhan; Call, Jeffrey E

2009-07-01

Beef subprimals were inoculated on the lean side with ca. 4.0 log CFU/g of a cocktail of rifampin-resistant (Rif(r)) Escherichia coli O157:H7 strains and then passed once through a mechanical blade tenderizer with the lean side facing upward. Inoculated subprimals that were not tenderized served as controls. Two core samples were removed from each of three tenderized subprimals and cut into six consecutive segments starting from the inoculated side. A total of six cores were also obtained from control subprimals, but only segment 1 (topmost) was sampled. Levels of E. coli O157:H7 recovered from segment 1 were 3.81 log CFU/g for the control subprimals and 3.36 log CFU/g for tenderized subprimals. The percentage of cells recovered in segment 2 was ca. 25-fold lower than levels recovered from segment 1, but E. coli O157:H7 was recovered from all six segments of the cores obtained from tenderized subprimals. In phase II, lean-side-inoculated (ca. 4.0 log CFU/g), single-pass tenderized subprimals were cut into steaks of various thicknesses (1.91 cm [0.75 in.], 2.54 cm [1.0 in.], and 3.18 cm [1.25 in.]) that were subsequently cooked on a commercial open-flame gas grill to internal temperatures of 48.8 degrees C (120 degrees F), 54.4 degrees C (130 degrees F), and 60 degrees C (140 degrees F). In general, regardless of temperature or thickness, we observed about a 2.6- to 4.2-log CFU/g reduction in pathogen levels following cooking. These data validate that cooking on a commercial gas grill is effective at eliminating relatively low levels of the pathogen that may be distributed throughout a blade-tenderized steak.

A Marked Poisson Process Driven Latent Shape Model for 3D Segmentation of Reflectance Confocal Microscopy Image Stacks of Human Skin.

PubMed

Ghanta, Sindhu; Jordan, Michael I; Kose, Kivanc; Brooks, Dana H; Rajadhyaksha, Milind; Dy, Jennifer G

2017-01-01

Segmenting objects of interest from 3D data sets is a common problem encountered in biological data. Small field of view and intrinsic biological variability combined with optically subtle changes of intensity, resolution, and low contrast in images make the task of segmentation difficult, especially for microscopy of unstained living or freshly excised thick tissues. Incorporating shape information in addition to the appearance of the object of interest can often help improve segmentation performance. However, the shapes of objects in tissue can be highly variable and design of a flexible shape model that encompasses these variations is challenging. To address such complex segmentation problems, we propose a unified probabilistic framework that can incorporate the uncertainty associated with complex shapes, variable appearance, and unknown locations. The driving application that inspired the development of this framework is a biologically important segmentation problem: the task of automatically detecting and segmenting the dermal-epidermal junction (DEJ) in 3D reflectance confocal microscopy (RCM) images of human skin. RCM imaging allows noninvasive observation of cellular, nuclear, and morphological detail. The DEJ is an important morphological feature as it is where disorder, disease, and cancer usually start. Detecting the DEJ is challenging, because it is a 2D surface in a 3D volume which has strong but highly variable number of irregularly spaced and variably shaped "peaks and valleys." In addition, RCM imaging resolution, contrast, and intensity vary with depth. Thus, a prior model needs to incorporate the intrinsic structure while allowing variability in essentially all its parameters. We propose a model which can incorporate objects of interest with complex shapes and variable appearance in an unsupervised setting by utilizing domain knowledge to build appropriate priors of the model. Our novel strategy to model this structure combines a spatial Poisson process with shape priors and performs inference using Gibbs sampling. Experimental results show that the proposed unsupervised model is able to automatically detect the DEJ with physiologically relevant accuracy in the range 10- 20 μm .

A Marked Poisson Process Driven Latent Shape Model for 3D Segmentation of Reflectance Confocal Microscopy Image Stacks of Human Skin

PubMed Central

Ghanta, Sindhu; Jordan, Michael I.; Kose, Kivanc; Brooks, Dana H.; Rajadhyaksha, Milind; Dy, Jennifer G.

2016-01-01

Segmenting objects of interest from 3D datasets is a common problem encountered in biological data. Small field of view and intrinsic biological variability combined with optically subtle changes of intensity, resolution and low contrast in images make the task of segmentation difficult, especially for microscopy of unstained living or freshly excised thick tissues. Incorporating shape information in addition to the appearance of the object of interest can often help improve segmentation performance. However, shapes of objects in tissue can be highly variable and design of a flexible shape model that encompasses these variations is challenging. To address such complex segmentation problems, we propose a unified probabilistic framework that can incorporate the uncertainty associated with complex shapes, variable appearance and unknown locations. The driving application which inspired the development of this framework is a biologically important segmentation problem: the task of automatically detecting and segmenting the dermal-epidermal junction (DEJ) in 3D reflectance confocal microscopy (RCM) images of human skin. RCM imaging allows noninvasive observation of cellular, nuclear and morphological detail. The DEJ is an important morphological feature as it is where disorder, disease and cancer usually start. Detecting the DEJ is challenging because it is a 2D surface in a 3D volume which has strong but highly variable number of irregularly spaced and variably shaped “peaks and valleys”. In addition, RCM imaging resolution, contrast and intensity vary with depth. Thus a prior model needs to incorporate the intrinsic structure while allowing variability in essentially all its parameters. We propose a model which can incorporate objects of interest with complex shapes and variable appearance in an unsupervised setting by utilizing domain knowledge to build appropriate priors of the model. Our novel strategy to model this structure combines a spatial Poisson process with shape priors and performs inference using Gibbs sampling. Experimental results show that the proposed unsupervised model is able to automatically detect the DEJ with physiologically relevant accuracy in the range 10 – 20µm. PMID:27723590

Effects of stacked quantitative resistances to downy mildew in lettuce do not simply add up.

PubMed

den Boer, Erik; Pelgrom, Koen T B; Zhang, Ningwen W; Visser, Richard G F; Niks, Rients E; Jeuken, Marieke J W

2014-08-01

In a stacking study of eight resistance QTLs in lettuce against downy mildew, only three out of ten double combinations showed an increased resistance effect under field conditions. Complete race nonspecific resistance to lettuce downy mildew, as observed for the nonhost wild lettuce species Lactuca saligna, is desired in lettuce cultivation. Genetic dissection of L. saligna's complete resistance has revealed several quantitative loci (QTL) for resistance with field infection reductions of 30-50 %. To test the effect of stacking these QTL, we analyzed interactions between homozygous L. saligna CGN05271 chromosome segments introgressed into the genetic background of L. sativa cv. Olof. Eight different backcross inbred lines (BILs) with single introgressions of 30-70 cM and selected predominately for quantitative resistance in field situations were intercrossed. Ten developed homozygous lines with stacked introgression segments (double combinations) were evaluated for resistance in the field. Seven double combinations showed a similar infection as the individual most resistant parental BIL, revealing epistatic interactions with 'less-than-additive' effects. Three double combinations showed an increased resistance level compared to their parental BILs and their interactions were additive, 'less-than-additive' epistatic and 'more-than-additive' epistatic, respectively. The additive interaction reduced field infection by 73 %. The double combination with a 'more-than-additive' epistatic effect, derived from a combination between a susceptible and a resistant BIL with 0 and 30 % infection reduction, respectively, showed an average field infection reduction of 52 %. For the latter line, an attempt to genetically dissect its underlying epistatic loci by substitution mapping did not result in smaller mapping intervals as none of the 22 substitution lines reached a similar high resistance level. Implications for breeding and the inheritance of L. saligna's complete resistance are discussed.

Statistical evaluation of manual segmentation of a diffuse low-grade glioma MRI dataset.

PubMed

Ben Abdallah, Meriem; Blonski, Marie; Wantz-Mezieres, Sophie; Gaudeau, Yann; Taillandier, Luc; Moureaux, Jean-Marie

2016-08-01

Software-based manual segmentation is critical to the supervision of diffuse low-grade glioma patients and to the optimal treatment's choice. However, manual segmentation being time-consuming, it is difficult to include it in the clinical routine. An alternative to circumvent the time cost of manual segmentation could be to share the task among different practitioners, providing it can be reproduced. The goal of our work is to assess diffuse low-grade gliomas' manual segmentation's reproducibility on MRI scans, with regard to practitioners, their experience and field of expertise. A panel of 13 experts manually segmented 12 diffuse low-grade glioma clinical MRI datasets using the OSIRIX software. A statistical analysis gave promising results, as the practitioner factor, the medical specialty and the years of experience seem to have no significant impact on the average values of the tumor volume variable.

Modeling and stability of segmented reflector telescopes - A decentralized approach

NASA Technical Reports Server (NTRS)

Ryaciotaki-Boussalis, Helen A.; Ih, Che-Hang Charles

1990-01-01

The decentralization of a segmented reflector telescope based on a finite-element model of its structure is considered. The decentralization of the system at the panel level is considered. Each panel is originally treated as an isolated subsystem so that the controller design is performed independently at the local level, and then applied to the composite system for stability analysis. The panel-level control laws were designed by means of pole placement using local output feedback. Simulation results show a better 1000:1 vibration attenuation in panel position when compared to the open-loop system. It is shown that the overall closed-loop system is exponentially stable provided that certain conditions are met. The advantage to the decentralized approach is that the design is performed in terms of the low-dimensionality subsystems, thus drastically reducing the design computational complexities.

Improved speed and stability of ST-segment recovery with reduced-dose tenecteplase and eptifibatide compared with full-dose tenecteplase for acute ST-segment elevation myocardial infarction.

PubMed

Roe, Matthew T; Green, Cynthia L; Giugliano, Robert P; Gibson, C Michael; Baran, Kenneth; Greenberg, Mark; Palmeri, Sebastian T; Crater, Suzanne; Trollinger, Kathleen; Hannan, Karen; Harrington, Robert A; Krucoff, Mitchell W

2004-02-18

This sub-study of the Integrilin and Tenecteplase in Acute Myocardial Infarction (INTEGRITI) trial evaluated of the impact of combination reperfusion therapy with reduced-dose tenecteplase plus eptifibatide on continuous ST-segment recovery and angiographic results. Combination therapy with reduced-dose fibrinolytics and glycoprotein IIb/IIIa inhibitors for ST-segment elevation myocardial infarction improves biomarkers of reperfusion success but has not reduced mortality when compared with full-dose fibrinolytics. We evaluated 140 patients enrolled in the INTEGRITI trial with 24-h continuous 12-lead ST-segment monitoring and angiography at 60 min. The dose-combination regimen of 50% of standard-dose tenecteplase (0.27 microg/kg) plus high-dose eptifibatide (2 boluses of 180 microg/kg separated by 10 min, 2.0 microg/kg/min infusion) was compared with full-dose tenecteplase (0.53 microg/kg). The dose-confirmation regimen of reduced-dose tenecteplase plus high-dose eptifibatide was associated with a faster median time to stable ST-segment recovery (55 vs. 98 min, p = 0.06), improved stable ST-segment recovery by 2 h (89.6% vs. 67.7%, p = 0.02), and less recurrent ischemia (34.0% vs. 57.1%, p = 0.05) when compared with full-dose tenecteplase. Continuously updated ST-segment recovery analyses demonstrated a modest trend toward greater ST-segment recovery at 30 min (57.7% vs. 40.6%, p = 0.13) and 60 min (82.7% vs. 65.6%, p = 0.08) with this regimen. These findings correlated with improved angiographic results at 60 min. Combination therapy with reduced-dose tenecteplase and eptifibatide leads to faster, more stable ST-segment recovery and improved angiographic flow patterns, compared with full-dose tenecteplase. These findings question the relationship between biomarkers of reperfusion success and clinical outcomes.

Brain tissue segmentation based on DTI data

PubMed Central

Liu, Tianming; Li, Hai; Wong, Kelvin; Tarokh, Ashley; Guo, Lei; Wong, Stephen T.C.

2008-01-01

We present a method for automated brain tissue segmentation based on the multi-channel fusion of diffusion tensor imaging (DTI) data. The method is motivated by the evidence that independent tissue segmentation based on DTI parametric images provides complementary information of tissue contrast to the tissue segmentation based on structural MRI data. This has important applications in defining accurate tissue maps when fusing structural data with diffusion data. In the absence of structural data, tissue segmentation based on DTI data provides an alternative means to obtain brain tissue segmentation. Our approach to the tissue segmentation based on DTI data is to classify the brain into two compartments by utilizing the tissue contrast existing in a single channel. Specifically, because the apparent diffusion coefficient (ADC) values in the cerebrospinal fluid (CSF) are more than twice that of gray matter (GM) and white matter (WM), we use ADC images to distinguish CSF and non-CSF tissues. Additionally, fractional anisotropy (FA) images are used to separate WM from non-WM tissues, as highly directional white matter structures have much larger fractional anisotropy values. Moreover, other channels to separate tissue are explored, such as eigenvalues of the tensor, relative anisotropy (RA), and volume ratio (VR). We developed an approach based on the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm that combines these two-class maps to obtain a complete tissue segmentation map of CSF, GM, and WM. Evaluations are provided to demonstrate the performance of our approach. Experimental results of applying this approach to brain tissue segmentation and deformable registration of DTI data and spoiled gradient-echo (SPGR) data are also provided. PMID:17804258

Deep Convolutional Neural Networks for Multi-Modality Isointense Infant Brain Image Segmentation

PubMed Central

Zhang, Wenlu; Li, Rongjian; Deng, Houtao; Wang, Li; Lin, Weili; Ji, Shuiwang; Shen, Dinggang

2015-01-01

The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development in health and disease. In the isointense stage (approximately 6–8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, making the tissue segmentation very challenging. Only a small number of existing methods have been designed for tissue segmentation in this isointense stage; however, they only used a single T1 or T2 images, or the combination of T1 and T2 images. In this paper, we propose to use deep convolutional neural networks (CNNs) for segmenting isointense stage brain tissues using multi-modality MR images. CNNs are a type of deep models in which trainable filters and local neighborhood pooling operations are applied alternatingly on the raw input images, resulting in a hierarchy of increasingly complex features. Specifically, we used multimodality information from T1, T2, and fractional anisotropy (FA) images as inputs and then generated the segmentation maps as outputs. The multiple intermediate layers applied convolution, pooling, normalization, and other operations to capture the highly nonlinear mappings between inputs and outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense stage brain images. Results showed that our proposed model significantly outperformed prior methods on infant brain tissue segmentation. In addition, our results indicated that integration of multi-modality images led to significant performance improvement. PMID:25562829

Change Detection of Remote Sensing Images by Dt-Cwt and Mrf

NASA Astrophysics Data System (ADS)

Ouyang, S.; Fan, K.; Wang, H.; Wang, Z.

2017-05-01

Aiming at the significant loss of high frequency information during reducing noise and the pixel independence in change detection of multi-scale remote sensing image, an unsupervised algorithm is proposed based on the combination between Dual-tree Complex Wavelet Transform (DT-CWT) and Markov random Field (MRF) model. This method first performs multi-scale decomposition for the difference image by the DT-CWT and extracts the change characteristics in high-frequency regions by using a MRF-based segmentation algorithm. Then our method estimates the final maximum a posterior (MAP) according to the segmentation algorithm of iterative condition model (ICM) based on fuzzy c-means(FCM) after reconstructing the high-frequency and low-frequency sub-bands of each layer respectively. Finally, the method fuses the above segmentation results of each layer by using the fusion rule proposed to obtain the mask of the final change detection result. The results of experiment prove that the method proposed is of a higher precision and of predominant robustness properties.

A segmentation/clustering model for the analysis of array CGH data.

PubMed

Picard, F; Robin, S; Lebarbier, E; Daudin, J-J

2007-09-01

Microarray-CGH (comparative genomic hybridization) experiments are used to detect and map chromosomal imbalances. A CGH profile can be viewed as a succession of segments that represent homogeneous regions in the genome whose representative sequences share the same relative copy number on average. Segmentation methods constitute a natural framework for the analysis, but they do not provide a biological status for the detected segments. We propose a new model for this segmentation/clustering problem, combining a segmentation model with a mixture model. We present a new hybrid algorithm called dynamic programming-expectation maximization (DP-EM) to estimate the parameters of the model by maximum likelihood. This algorithm combines DP and the EM algorithm. We also propose a model selection heuristic to select the number of clusters and the number of segments. An example of our procedure is presented, based on publicly available data sets. We compare our method to segmentation methods and to hidden Markov models, and we show that the new segmentation/clustering model is a promising alternative that can be applied in the more general context of signal processing.

Learning a generative model of images by factoring appearance and shape.

PubMed

Le Roux, Nicolas; Heess, Nicolas; Shotton, Jamie; Winn, John

2011-03-01

Computer vision has grown tremendously in the past two decades. Despite all efforts, existing attempts at matching parts of the human visual system's extraordinary ability to understand visual scenes lack either scope or power. By combining the advantages of general low-level generative models and powerful layer-based and hierarchical models, this work aims at being a first step toward richer, more flexible models of images. After comparing various types of restricted Boltzmann machines (RBMs) able to model continuous-valued data, we introduce our basic model, the masked RBM, which explicitly models occlusion boundaries in image patches by factoring the appearance of any patch region from its shape. We then propose a generative model of larger images using a field of such RBMs. Finally, we discuss how masked RBMs could be stacked to form a deep model able to generate more complicated structures and suitable for various tasks such as segmentation or object recognition.

Cellular RNA binding proteins NS1-BP and hnRNP K regulate influenza A virus RNA splicing.

PubMed

Tsai, Pei-Ling; Chiou, Ni-Ting; Kuss, Sharon; García-Sastre, Adolfo; Lynch, Kristen W; Fontoura, Beatriz M A

2013-01-01

Influenza A virus is a major human pathogen with a genome comprised of eight single-strand, negative-sense, RNA segments. Two viral RNA segments, NS1 and M, undergo alternative splicing and yield several proteins including NS1, NS2, M1 and M2 proteins. However, the mechanisms or players involved in splicing of these viral RNA segments have not been fully studied. Here, by investigating the interacting partners and function of the cellular protein NS1-binding protein (NS1-BP), we revealed novel players in the splicing of the M1 segment. Using a proteomics approach, we identified a complex of RNA binding proteins containing NS1-BP and heterogeneous nuclear ribonucleoproteins (hnRNPs), among which are hnRNPs involved in host pre-mRNA splicing. We found that low levels of NS1-BP specifically impaired proper alternative splicing of the viral M1 mRNA segment to yield the M2 mRNA without affecting splicing of mRNA3, M4, or the NS mRNA segments. Further biochemical analysis by formaldehyde and UV cross-linking demonstrated that NS1-BP did not interact directly with viral M1 mRNA but its interacting partners, hnRNPs A1, K, L, and M, directly bound M1 mRNA. Among these hnRNPs, we identified hnRNP K as a major mediator of M1 mRNA splicing. The M1 mRNA segment generates the matrix protein M1 and the M2 ion channel, which are essential proteins involved in viral trafficking, release into the cytoplasm, and budding. Thus, reduction of NS1-BP and/or hnRNP K levels altered M2/M1 mRNA and protein ratios, decreasing M2 levels and inhibiting virus replication. Thus, NS1-BP-hnRNPK complex is a key mediator of influenza A virus gene expression.

Economic vulnerability to sea-level rise along the northern U.S. Gulf Coast

USGS Publications Warehouse

Thatcher, Cindy A.; Brock, John C.; Pendleton, Elizabeth A.

2013-01-01

The northern Gulf of Mexico coast of the United States has been identified as highly vulnerable to sea-level rise, based on a combination of physical and societal factors. Vulnerability of human populations and infrastructure to projected increases in sea level is a critical area of uncertainty for communities in the extremely low-lying and flat northern gulf coastal zone. A rapidly growing population along some parts of the northern Gulf of Mexico coastline is further increasing the potential societal and economic impacts of projected sea-level rise in the region, where observed relative rise rates range from 0.75 to 9.95 mm per year on the Gulf coasts of Texas, Louisiana, Mississippi, Alabama, and Florida. A 1-m elevation threshold was chosen as an inclusive designation of the coastal zone vulnerable to relative sea-level rise, because of uncertainty associated with sea-level rise projections. This study applies a Coastal Economic Vulnerability Index (CEVI) to the northern Gulf of Mexico region, which includes both physical and economic factors that contribute to societal risk of impacts from rising sea level. The economic variables incorporated in the CEVI include human population, urban land cover, economic value of key types of infrastructure, and residential and commercial building values. The variables are standardized and combined to produce a quantitative index value for each 1-km coastal segment, highlighting areas where human populations and the built environment are most at risk. This information can be used by coastal managers as they allocate limited resources for ecosystem restoration, beach nourishment, and coastal-protection infrastructure. The study indicates a large amount of variability in index values along the northern Gulf of Mexico coastline, and highlights areas where long-term planning to enhance resiliency is particularly needed.

Disjunctive Normal Shape and Appearance Priors with Applications to Image Segmentation.

PubMed

Mesadi, Fitsum; Cetin, Mujdat; Tasdizen, Tolga

2015-10-01

The use of appearance and shape priors in image segmentation is known to improve accuracy; however, existing techniques have several drawbacks. Active shape and appearance models require landmark points and assume unimodal shape and appearance distributions. Level set based shape priors are limited to global shape similarity. In this paper, we present a novel shape and appearance priors for image segmentation based on an implicit parametric shape representation called disjunctive normal shape model (DNSM). DNSM is formed by disjunction of conjunctions of half-spaces defined by discriminants. We learn shape and appearance statistics at varying spatial scales using nonparametric density estimation. Our method can generate a rich set of shape variations by locally combining training shapes. Additionally, by studying the intensity and texture statistics around each discriminant of our shape model, we construct a local appearance probability map. Experiments carried out on both medical and natural image datasets show the potential of the proposed method.

Local Generation and Propagation of Ripples along the Septotemporal Axis of the Hippocampus

PubMed Central

Patel, Jagdish; Schomburg, Erik W.; Berényi, Antal; Fujisawa, Shigeyoshi

2013-01-01

A topographical relationship exists between the septotemporal segments of the hippocampus and their entorhinal–neocortical targets, but the physiological organization of activity along the septotemporal axis is poorly understood. We recorded sharp-wave ripple patterns in rats during sleep from the entire septotemporal axis of the CA1 pyramidal layer. Qualitatively similar ripples emerged at all levels. From the local seed, ripples traveled septally or temporally at a speed of ∼0.35 m/s, and the spatial spread depended on ripple magnitude. Ripples propagated smoothly across the septal and intermediate segments of the hippocampus, but ripples in the temporal segment often remained isolated. These findings show that ripples can combine information from the septal and intermediate hippocampus and transfer integrated signals downstream. In contrast, ripples that emerged in the temporal pole broadcast largely independent information to their cortical and subcortical targets. PMID:24155307

Low-cost telepresence for collaborative virtual environments.

PubMed

Rhee, Seon-Min; Ziegler, Remo; Park, Jiyoung; Naef, Martin; Gross, Markus; Kim, Myoung-Hee

2007-01-01

We present a novel low-cost method for visual communication and telepresence in a CAVE -like environment, relying on 2D stereo-based video avatars. The system combines a selection of proven efficient algorithms and approximations in a unique way, resulting in a convincing stereoscopic real-time representation of a remote user acquired in a spatially immersive display. The system was designed to extend existing projection systems with acquisition capabilities requiring minimal hardware modifications and cost. The system uses infrared-based image segmentation to enable concurrent acquisition and projection in an immersive environment without a static background. The system consists of two color cameras and two additional b/w cameras used for segmentation in the near-IR spectrum. There is no need for special optics as the mask and color image are merged using image-warping based on a depth estimation. The resulting stereo image stream is compressed, streamed across a network, and displayed as a frame-sequential stereo texture on a billboard in the remote virtual environment.

Dual-modality brain PET-CT image segmentation based on adaptive use of functional and anatomical information.

PubMed

Xia, Yong; Eberl, Stefan; Wen, Lingfeng; Fulham, Michael; Feng, David Dagan

2012-01-01

Dual medical imaging modalities, such as PET-CT, are now a routine component of clinical practice. Medical image segmentation methods, however, have generally only been applied to single modality images. In this paper, we propose the dual-modality image segmentation model to segment brain PET-CT images into gray matter, white matter and cerebrospinal fluid. This model converts PET-CT image segmentation into an optimization process controlled simultaneously by PET and CT voxel values and spatial constraints. It is innovative in the creation and application of the modality discriminatory power (MDP) coefficient as a weighting scheme to adaptively combine the functional (PET) and anatomical (CT) information on a voxel-by-voxel basis. Our approach relies upon allowing the modality with higher discriminatory power to play a more important role in the segmentation process. We compared the proposed approach to three other image segmentation strategies, including PET-only based segmentation, combination of the results of independent PET image segmentation and CT image segmentation, and simultaneous segmentation of joint PET and CT images without an adaptive weighting scheme. Our results in 21 clinical studies showed that our approach provides the most accurate and reliable segmentation for brain PET-CT images. Copyright © 2011 Elsevier Ltd. All rights reserved.

Modeling of the interaction between grip force and vibration transmissibility of a finger.

PubMed

Wu, John Z; Welcome, Daniel E; McDowell, Thomas W; Xu, Xueyan S; Dong, Ren G

2017-07-01

It is known that the vibration characteristics of the fingers and hand and the level of grip action interacts when operating a power tool. In the current study, we developed a hybrid finger model to simulate the vibrations of the hand-finger system when gripping a vibrating handle covered with soft materials. The hybrid finger model combines the characteristics of conventional finite element (FE) models, multi-body musculoskeletal models, and lumped mass models. The distal, middle, and proximal finger segments were constructed using FE models, the finger segments were connected via three flexible joint linkages (i.e., distal interphalangeal joint (DIP), proximal interphalangeal joint (PIP), and metacarpophalangeal (MCP) joint), and the MCP joint was connected to the ground and handle via lumped parameter elements. The effects of the active muscle forces were accounted for via the joint moments. The bone, nail, and hard connective tissues were assumed to be linearly elastic whereas the soft tissues, which include the skin and subcutaneous tissues, were considered as hyperelastic and viscoelastic. The general trends of the model predictions agree well with the previous experimental measurements in that the resonant frequency increased from proximal to the middle and to the distal finger segments for the same grip force, that the resonant frequency tends to increase with increasing grip force for the same finger segment, especially for the distal segment, and that the magnitude of vibration transmissibility tends to increase with increasing grip force, especially for the proximal segment. The advantage of the proposed model over the traditional vibration models is that it can predict the local vibration behavior of the finger to a tissue level, while taking into account the effects of the active musculoskeletal force, the effects of the contact conditions on vibrations, the global vibration characteristics. Published by Elsevier Ltd.

Interactive Tooth Separation from Dental Model Using Segmentation Field

PubMed Central

2016-01-01

Tooth segmentation on dental model is an essential step of computer-aided-design systems for orthodontic virtual treatment planning. However, fast and accurate identifying cutting boundary to separate teeth from dental model still remains a challenge, due to various geometrical shapes of teeth, complex tooth arrangements, different dental model qualities, and varying degrees of crowding problems. Most segmentation approaches presented before are not able to achieve a balance between fine segmentation results and simple operating procedures with less time consumption. In this article, we present a novel, effective and efficient framework that achieves tooth segmentation based on a segmentation field, which is solved by a linear system defined by a discrete Laplace-Beltrami operator with Dirichlet boundary conditions. A set of contour lines are sampled from the smooth scalar field, and candidate cutting boundaries can be detected from concave regions with large variations of field data. The sensitivity to concave seams of the segmentation field facilitates effective tooth partition, as well as avoids obtaining appropriate curvature threshold value, which is unreliable in some case. Our tooth segmentation algorithm is robust to dental models with low quality, as well as is effective to dental models with different levels of crowding problems. The experiments, including segmentation tests of varying dental models with different complexity, experiments on dental meshes with different modeling resolutions and surface noises and comparison between our method and the morphologic skeleton segmentation method are conducted, thus demonstrating the effectiveness of our method. PMID:27532266

Segmentation of organs at risk in CT volumes of head, thorax, abdomen, and pelvis

NASA Astrophysics Data System (ADS)

Han, Miaofei; Ma, Jinfeng; Li, Yan; Li, Meiling; Song, Yanli; Li, Qiang

2015-03-01

Accurate segmentation of organs at risk (OARs) is a key step in treatment planning system (TPS) of image guided radiation therapy. We are developing three classes of methods to segment 17 organs at risk throughout the whole body, including brain, brain stem, eyes, mandible, temporomandibular joints, parotid glands, spinal cord, lungs, trachea, heart, livers, kidneys, spleen, prostate, rectum, femoral heads, and skin. The three classes of segmentation methods include (1) threshold-based methods for organs of large contrast with adjacent structures such as lungs, trachea, and skin; (2) context-driven Generalized Hough Transform-based methods combined with graph cut algorithm for robust localization and segmentation of liver, kidneys and spleen; and (3) atlas and registration-based methods for segmentation of heart and all organs in CT volumes of head and pelvis. The segmentation accuracy for the seventeen organs was subjectively evaluated by two medical experts in three levels of score: 0, poor (unusable in clinical practice); 1, acceptable (minor revision needed); and 2, good (nearly no revision needed). A database was collected from Ruijin Hospital, Huashan Hospital, and Xuhui Central Hospital in Shanghai, China, including 127 head scans, 203 thoracic scans, 154 abdominal scans, and 73 pelvic scans. The percentages of "good" segmentation results were 97.6%, 92.9%, 81.1%, 87.4%, 85.0%, 78.7%, 94.1%, 91.1%, 81.3%, 86.7%, 82.5%, 86.4%, 79.9%, 72.6%, 68.5%, 93.2%, 96.9% for brain, brain stem, eyes, mandible, temporomandibular joints, parotid glands, spinal cord, lungs, trachea, heart, livers, kidneys, spleen, prostate, rectum, femoral heads, and skin, respectively. Various organs at risk can be reliably segmented from CT scans by use of the three classes of segmentation methods.

Semiautomated hybrid algorithm for estimation of three-dimensional liver surface in CT using dynamic cellular automata and level-sets.

PubMed

Dakua, Sarada Prasad; Abinahed, Julien; Al-Ansari, Abdulla

2015-04-01

Liver segmentation continues to remain a major challenge, largely due to its intense complexity with surrounding anatomical structures (stomach, kidney, and heart), high noise level and lack of contrast in pathological computed tomography (CT) data. We present an approach to reconstructing the liver surface in low contrast CT. The main contributions are: (1) a stochastic resonance-based methodology in discrete cosine transform domain is developed to enhance the contrast of pathological liver images, (2) a new formulation is proposed to prevent the object boundary, resulting from the cellular automata method, from leaking into the surrounding areas of similar intensity, and (3) a level-set method is suggested to generate intermediate segmentation contours from two segmented slices distantly located in a subject sequence. We have tested the algorithm on real datasets obtained from two sources, Hamad General Hospital and medical image computing and computer-assisted interventions grand challenge workshop. Various parameters in the algorithm, such as [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text], play imperative roles, thus their values are precisely selected. Both qualitative and quantitative evaluation performed on liver data show promising segmentation accuracy when compared with ground truth data reflecting the potential of the proposed method.

Semantic Segmentation of Forest Stands of Pure Species as a Global Optimization Problem

NASA Astrophysics Data System (ADS)

Dechesne, C.; Mallet, C.; Le Bris, A.; Gouet-Brunet, V.

2017-05-01

Forest stand delineation is a fundamental task for forest management purposes, that is still mainly manually performed through visual inspection of geospatial (very) high spatial resolution images. Stand detection has been barely addressed in the literature which has mainly focused, in forested environments, on individual tree extraction and tree species classification. From a methodological point of view, stand detection can be considered as a semantic segmentation problem. It offers two advantages. First, one can retrieve the dominant tree species per segment. Secondly, one can benefit from existing low-level tree species label maps from the literature as a basis for high-level object extraction. Thus, the semantic segmentation issue becomes a regularization issue in a weakly structured environment and can be formulated in an energetical framework. This papers aims at investigating which regularization strategies of the literature are the most adapted to delineate and classify forest stands of pure species. Both airborne lidar point clouds and multispectral very high spatial resolution images are integrated for that purpose. The local methods (such as filtering and probabilistic relaxation) are not adapted for such problem since the increase of the classification accuracy is below 5%. The global methods, based on an energy model, tend to be more efficient with an accuracy gain up to 15%. The segmentation results using such models have an accuracy ranging from 96% to 99%.

Cooling system for three hook ring segment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Campbell, Christian X.; Eng, Darryl; Lee, Ching-Pang

2014-08-26

A triple hook ring segment including forward, midsection and aft mounting hooks for engagement with respective hangers formed on a ring segment carrier for supporting a ring segment panel, and defining a forward high pressure chamber and an aft low pressure chamber on opposing sides of the midsection mounting hook. An isolation plate is provided on the aft side of the midsection mounting hook to form an isolation chamber between the aft low pressure chamber and the ring segment panel. High pressure air is supplied to the forward chamber and flows to the isolation chamber through crossover passages in themore » midsection hook. The isolation chamber provides convection cooling air to an aft portion of the ring segment panel and enables a reduction of air pressure in the aft low pressure chamber to reduce leakage flow of cooling air from the ring segment.« less

The Labor Market in the Regions of Belarus: An Analysis of Employment Tendencies

ERIC Educational Resources Information Center

Sokolova, G. N.

2013-01-01

In Belarus, the ways in which statistics are compiled, the complex rules for registering as unemployed, and the segmentation of the labor market and job-seeking activities, all combine to hide the actual levels of employment and unemployment. This in turn makes it difficult to develop appropriate and effective labor policies, and to have support…

Physiological interpretation of Doppler shift waveforms: the femorodistal segment in combined disease.

PubMed

Campbell, W B; Baird, R N; Cole, S E; Evans, J M; Skidmore, R; Woodcock, J P

1983-01-01

A new method is presented for assessing the femorodistal segment in multisegmental arterial disease, using the Laplace transform technique of Doppler waveform analysis. Blood velocity/time waveforms were obtained at femoral and ankle levels in three groups of limbs--50 without arterial disease, 12 with isolated aortoiliac stenoses, and 32 with femoropopliteal occlusions, with and without proximal disease. The waveforms were analysed for Laplace transform and pulsatility index values. The omega 0 coefficients of the Laplace transform analysis at femoral and ankle levels were compared in each subject, as the omega 0 gradient (femoral/ankle omega 0): and pulsatility index damping factor (femoral/ankle P1) was also calculated. The omega 0 gradient was shown to detect femoropopliteal occlusion in the presence of multisegmental arterial disease and to give some indication of its haemodynamic significance. The diagnostic accuracy of the omega 0 gradient was superior to that of pulsatility index damping factor. When combined with its existing ability to detect aortoiliac stenosis, this new application of the Laplace transform method offers the possibility both of a system for complete localisation of significant arterial lesions, and potential for follow-up of vascular surgical procedures in the lower limb, from two simple Doppler recordings.

Fusion-nonfusion hybrid construct versus anterior cervical hybrid decompression and fusion: a comparative study for 3-level cervical degenerative disc diseases.

PubMed

Ding, Fan; Jia, Zhiwei; Wu, Yaohong; Li, Chao; He, Qing; Ruan, Dike

2014-11-01

A retrospective analysis. This study aimed to compare the safety and efficacy between the fusion-nonfusion hybrid construct (HC: anterior cervical corpectomy and fusion plus artificial disc replacement, ACCF plus cADR) and anterior cervical hybrid decompression and fusion (ACHDF: anterior cervical corpectomy and fusion plus discectomy and fusion, ACCF plus ACDF) for 3-level cervical degenerative disc diseases (cDDD). The optimal anterior technique for 3-level cDDD remains uncertain. Long-segment fusion substantially induced biomechanical changes at adjacent levels, which may lead to symptomatic adjacent segment degeneration. Hybrid surgery consisting of ACDF and cADR has been reported with good results for 2-level cDDD. In this context, ACCF combining with cADR may be an alternative to ACHDF for 3-level cDDD. Between 2009 and 2012, 28 patients with 3-level cDDD who underwent HC (n=13) and ACHDF (15) were retrospectively reviewed. Clinical assessments were based on Neck Disability Index, Japanese Orthopedic Association disability scale, visual analogue scale, Japanese Orthopedic Association recovery rate, and Odom criteria. Radiological analysis included range of motion of C2-C7 and adjacent segments and cervical lordosis. Perioperative parameters, radiological adjacent-level changes, and the complications were also assessed. HC showed better Neck Disability Index improvement at 12 and 24 months, as well as Japanese Orthopedic Association and visual analogue scale improvement at 24 months postoperatively (P<0.05). HC had better outcome according to Odom criteria but not significantly (P>0.05). The range of motion of C2-C7 and adjacent segments was less compromised in HC (P<0.05). Both 2 groups showed significant lordosis recovery postoperatively (P<0.05), but no difference was found between groups (P>0.05). The incidence of adjacent-level degenerative changes and complications was higher in ACHDF but not significantly (P>0.05). HC may be an alternative to ACHDF for 3-level cDDD due to the equivalent or superior early clinical outcomes, less compromised C2-C7 range of motion, and less impact at adjacent levels. 3.

SuBSENSE: a universal change detection method with local adaptive sensitivity.

PubMed

St-Charles, Pierre-Luc; Bilodeau, Guillaume-Alexandre; Bergevin, Robert

2015-01-01

Foreground/background segmentation via change detection in video sequences is often used as a stepping stone in high-level analytics and applications. Despite the wide variety of methods that have been proposed for this problem, none has been able to fully address the complex nature of dynamic scenes in real surveillance tasks. In this paper, we present a universal pixel-level segmentation method that relies on spatiotemporal binary features as well as color information to detect changes. This allows camouflaged foreground objects to be detected more easily while most illumination variations are ignored. Besides, instead of using manually set, frame-wide constants to dictate model sensitivity and adaptation speed, we use pixel-level feedback loops to dynamically adjust our method's internal parameters without user intervention. These adjustments are based on the continuous monitoring of model fidelity and local segmentation noise levels. This new approach enables us to outperform all 32 previously tested state-of-the-art methods on the 2012 and 2014 versions of the ChangeDetection.net dataset in terms of overall F-Measure. The use of local binary image descriptors for pixel-level modeling also facilitates high-speed parallel implementations: our own version, which used no low-level or architecture-specific instruction, reached real-time processing speed on a midlevel desktop CPU. A complete C++ implementation based on OpenCV is available online.

Combined modified en bloc corpectomy with replacement of the aorta in curative interdisciplinary treatment of a large osteosarcoma infiltrating the aorta.

PubMed

Pilger, Amrei; Tsilimparis, Nikolaos; Bockhorn, Maximilian; Trepel, Martin; Dreimann, Marc

2016-05-01

We report a case of a large three-level spinal osteosarcoma infiltrating the adjacent aorta. This is the first case in which a combined modified three-level en bloc corpectomy with resection and replacement of the adjacent aorta was successful as a part of interdisciplinary curative treatment. Case report. The surgical procedure was performed as a two-step treatment. A heart lung machine (HLM) was not used, in order to avoid cerebral and spinal ischemia and to decrease the risk of hematogenous tumor metastases. Instead, a bypass from the left subclavian artery the distal descending aorta was used. We modified the en bloc corpectomy procedure, leaving a dorsal segment of the vertebral bodies to enable rapid surgery. The procedure was successful and the en bloc resection of the vertebral body with aortal resection could be achieved. Except for pallhypesthesia in the left dermatomes Th7-Th10, the patient does not have any postoperative neurologic deficits. Combined corpectomy with aortic replacement should be considered as a reasonable option in the curative treatment of osteosarcoma with consideration of the immense surgical risks. The use of an HLM is not necessary, especially considering the inherent risk of hematogenous tumor metastases. Modified corpectomy leaving a dorsal vertebral body segment was considered a reasonable variation since tumor-free margins could still be expected.

Brain tumor segmentation from multimodal magnetic resonance images via sparse representation.

PubMed

Li, Yuhong; Jia, Fucang; Qin, Jing

2016-10-01

Accurately segmenting and quantifying brain gliomas from magnetic resonance (MR) images remains a challenging task because of the large spatial and structural variability among brain tumors. To develop a fully automatic and accurate brain tumor segmentation algorithm, we present a probabilistic model of multimodal MR brain tumor segmentation. This model combines sparse representation and the Markov random field (MRF) to solve the spatial and structural variability problem. We formulate the tumor segmentation problem as a multi-classification task by labeling each voxel as the maximum posterior probability. We estimate the maximum a posteriori (MAP) probability by introducing the sparse representation into a likelihood probability and a MRF into the prior probability. Considering the MAP as an NP-hard problem, we convert the maximum posterior probability estimation into a minimum energy optimization problem and employ graph cuts to find the solution to the MAP estimation. Our method is evaluated using the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013) and obtained Dice coefficient metric values of 0.85, 0.75, and 0.69 on the high-grade Challenge data set, 0.73, 0.56, and 0.54 on the high-grade Challenge LeaderBoard data set, and 0.84, 0.54, and 0.57 on the low-grade Challenge data set for the complete, core, and enhancing regions. The experimental results show that the proposed algorithm is valid and ranks 2nd compared with the state-of-the-art tumor segmentation algorithms in the MICCAI BRATS 2013 challenge. Copyright © 2016 Elsevier B.V. All rights reserved.

Automatic Semantic Segmentation of Brain Gliomas from MRI Images Using a Deep Cascaded Neural Network.

PubMed

Cui, Shaoguo; Mao, Lei; Jiang, Jingfeng; Liu, Chang; Xiong, Shuyu

2018-01-01

Brain tumors can appear anywhere in the brain and have vastly different sizes and morphology. Additionally, these tumors are often diffused and poorly contrasted. Consequently, the segmentation of brain tumor and intratumor subregions using magnetic resonance imaging (MRI) data with minimal human interventions remains a challenging task. In this paper, we present a novel fully automatic segmentation method from MRI data containing in vivo brain gliomas. This approach can not only localize the entire tumor region but can also accurately segment the intratumor structure. The proposed work was based on a cascaded deep learning convolutional neural network consisting of two subnetworks: (1) a tumor localization network (TLN) and (2) an intratumor classification network (ITCN). The TLN, a fully convolutional network (FCN) in conjunction with the transfer learning technology, was used to first process MRI data. The goal of the first subnetwork was to define the tumor region from an MRI slice. Then, the ITCN was used to label the defined tumor region into multiple subregions. Particularly, ITCN exploited a convolutional neural network (CNN) with deeper architecture and smaller kernel. The proposed approach was validated on multimodal brain tumor segmentation (BRATS 2015) datasets, which contain 220 high-grade glioma (HGG) and 54 low-grade glioma (LGG) cases. Dice similarity coefficient (DSC), positive predictive value (PPV), and sensitivity were used as evaluation metrics. Our experimental results indicated that our method could obtain the promising segmentation results and had a faster segmentation speed. More specifically, the proposed method obtained comparable and overall better DSC values (0.89, 0.77, and 0.80) on the combined (HGG + LGG) testing set, as compared to other methods reported in the literature. Additionally, the proposed approach was able to complete a segmentation task at a rate of 1.54 seconds per slice.

Locally-constrained Boundary Regression for Segmentation of Prostate and Rectum in the Planning CT Images

PubMed Central

Shao, Yeqin; Gao, Yaozong; Wang, Qian; Yang, Xin; Shen, Dinggang

2015-01-01

Automatic and accurate segmentation of the prostate and rectum in planning CT images is a challenging task due to low image contrast, unpredictable organ (relative) position, and uncertain existence of bowel gas across different patients. Recently, regression forest was adopted for organ deformable segmentation on 2D medical images by training one landmark detector for each point on the shape model. However, it seems impractical for regression forest to guide 3D deformable segmentation as a landmark detector, due to large number of vertices in the 3D shape model as well as the difficulty in building accurate 3D vertex correspondence for each landmark detector. In this paper, we propose a novel boundary detection method by exploiting the power of regression forest for prostate and rectum segmentation. The contributions of this paper are as follows: 1) we introduce regression forest as a local boundary regressor to vote the entire boundary of a target organ, which avoids training a large number of landmark detectors and building an accurate 3D vertex correspondence for each landmark detector; 2) an auto-context model is integrated with regression forest to improve the accuracy of the boundary regression; 3) we further combine a deformable segmentation method with the proposed local boundary regressor for the final organ segmentation by integrating organ shape priors. Our method is evaluated on a planning CT image dataset with 70 images from 70 different patients. The experimental results show that our proposed boundary regression method outperforms the conventional boundary classification method in guiding the deformable model for prostate and rectum segmentations. Compared with other state-of-the-art methods, our method also shows a competitive performance. PMID:26439938

An interactive toolbox for atlas-based segmentation and coding of volumetric images

NASA Astrophysics Data System (ADS)

Menegaz, G.; Luti, S.; Duay, V.; Thiran, J.-Ph.

2007-03-01

Medical imaging poses the great challenge of having compression algorithms that are lossless for diagnostic and legal reasons and yet provide high compression rates for reduced storage and transmission time. The images usually consist of a region of interest representing the part of the body under investigation surrounded by a "background", which is often noisy and not of diagnostic interest. In this paper, we propose a ROI-based 3D coding system integrating both the segmentation and the compression tools. The ROI is extracted by an atlas based 3D segmentation method combining active contours with information theoretic principles, and the resulting segmentation map is exploited for ROI based coding. The system is equipped with a GUI allowing the medical doctors to supervise the segmentation process and eventually reshape the detected contours at any point. The process is initiated by the user through the selection of either one pre-de.ned reference image or one image of the volume to be used as the 2D "atlas". The object contour is successively propagated from one frame to the next where it is used as the initial border estimation. In this way, the entire volume is segmented based on a unique 2D atlas. The resulting 3D segmentation map is exploited for adaptive coding of the different image regions. Two coding systems were considered: the JPEG3D standard and the 3D-SPITH. The evaluation of the performance with respect to both segmentation and coding proved the high potential of the proposed system in providing an integrated, low-cost and computationally effective solution for CAD and PAC systems.

Unsupervised motion-based object segmentation refined by color

NASA Astrophysics Data System (ADS)

Piek, Matthijs C.; Braspenning, Ralph; Varekamp, Chris

2003-06-01

For various applications, such as data compression, structure from motion, medical imaging and video enhancement, there is a need for an algorithm that divides video sequences into independently moving objects. Because our focus is on video enhancement and structure from motion for consumer electronics, we strive for a low complexity solution. For still images, several approaches exist based on colour, but these lack in both speed and segmentation quality. For instance, colour-based watershed algorithms produce a so-called oversegmentation with many segments covering each single physical object. Other colour segmentation approaches exist which somehow limit the number of segments to reduce this oversegmentation problem. However, this often results in inaccurate edges or even missed objects. Most likely, colour is an inherently insufficient cue for real world object segmentation, because real world objects can display complex combinations of colours. For video sequences, however, an additional cue is available, namely the motion of objects. When different objects in a scene have different motion, the motion cue alone is often enough to reliably distinguish objects from one another and the background. However, because of the lack of sufficient resolution of efficient motion estimators, like the 3DRS block matcher, the resulting segmentation is not at pixel resolution, but at block resolution. Existing pixel resolution motion estimators are more sensitive to noise, suffer more from aperture problems or have less correspondence to the true motion of objects when compared to block-based approaches or are too computationally expensive. From its tendency to oversegmentation it is apparent that colour segmentation is particularly effective near edges of homogeneously coloured areas. On the other hand, block-based true motion estimation is particularly effective in heterogeneous areas, because heterogeneous areas improve the chance a block is unique and thus decrease the chance of the wrong position producing a good match. Consequently, a number of methods exist which combine motion and colour segmentation. These methods use colour segmentation as a base for the motion segmentation and estimation or perform an independent colour segmentation in parallel which is in some way combined with the motion segmentation. The presented method uses both techniques to complement each other by first segmenting on motion cues and then refining the segmentation with colour. To our knowledge few methods exist which adopt this approach. One example is te{meshrefine}. This method uses an irregular mesh, which hinders its efficient implementation in consumer electronics devices. Furthermore, the method produces a foreground/background segmentation, while our applications call for the segmentation of multiple objects. NEW METHOD As mentioned above we start with motion segmentation and refine the edges of this segmentation with a pixel resolution colour segmentation method afterwards. There are several reasons for this approach: + Motion segmentation does not produce the oversegmentation which colour segmentation methods normally produce, because objects are more likely to have colour discontinuities than motion discontinuities. In this way, the colour segmentation only has to be done at the edges of segments, confining the colour segmentation to a smaller part of the image. In such a part, it is more likely that the colour of an object is homogeneous. + This approach restricts the computationally expensive pixel resolution colour segmentation to a subset of the image. Together with the very efficient 3DRS motion estimation algorithm, this helps to reduce the computational complexity. + The motion cue alone is often enough to reliably distinguish objects from one another and the background. To obtain the motion vector fields, a variant of the 3DRS block-based motion estimator which analyses three frames of input was used. The 3DRS motion estimator is known for its ability to estimate motion vectors which closely resemble the true motion. BLOCK-BASED MOTION SEGMENTATION As mentioned above we start with a block-resolution segmentation based on motion vectors. The presented method is inspired by the well-known K-means segmentation method te{K-means}. Several other methods (e.g. te{kmeansc}) adapt K-means for connectedness by adding a weighted shape-error. This adds the additional difficulty of finding the correct weights for the shape-parameters. Also, these methods often bias one particular pre-defined shape. The presented method, which we call K-regions, encourages connectedness because only blocks at the edges of segments may be assigned to another segment. This constrains the segmentation method to such a degree that it allows the method to use least squares for the robust fitting of affine motion models for each segment. Contrary to te{parmkm}, the segmentation step still operates on vectors instead of model parameters. To make sure the segmentation is temporally consistent, the segmentation of the previous frame will be used as initialisation for every new frame. We also present a scheme which makes the algorithm independent of the initially chosen amount of segments. COLOUR-BASED INTRA-BLOCK SEGMENTATION The block resolution motion-based segmentation forms the starting point for the pixel resolution segmentation. The pixel resolution segmentation is obtained from the block resolution segmentation by reclassifying pixels only at the edges of clusters. We assume that an edge between two objects can be found in either one of two neighbouring blocks that belong to different clusters. This assumption allows us to do the pixel resolution segmentation on each pair of such neighbouring blocks separately. Because of the local nature of the segmentation, it largely avoids problems with heterogeneously coloured areas. Because no new segments are introduced in this step, it also does not suffer from oversegmentation problems. The presented method has no problems with bifurcations. For the pixel resolution segmentation itself we reclassify pixels such that we optimize an error norm which favour similarly coloured regions and straight edges. SEGMENTATION MEASURE To assist in the evaluation of the proposed algorithm we developed a quality metric. Because the problem does not have an exact specification, we decided to define a ground truth output which we find desirable for a given input. We define the measure for the segmentation quality as being how different the segmentation is from the ground truth. Our measure enables us to evaluate oversegmentation and undersegmentation seperately. Also, it allows us to evaluate which parts of a frame suffer from oversegmentation or undersegmentation. The proposed algorithm has been tested on several typical sequences. CONCLUSIONS In this abstract we presented a new video segmentation method which performs well in the segmentation of multiple independently moving foreground objects from each other and the background. It combines the strong points of both colour and motion segmentation in the way we expected. One of the weak points is that the segmentation method suffers from undersegmentation when adjacent objects display similar motion. In sequences with detailed backgrounds the segmentation will sometimes display noisy edges. Apart from these results, we think that some of the techniques, and in particular the K-regions technique, may be useful for other two-dimensional data segmentation problems.

Segmentation in low-penetration and low-involvement categories: an application to lottery games.

PubMed

Guesalaga, Rodrigo; Marshall, Pablo

2013-09-01

Market segmentation is accepted as a fundamental concept in marketing and several authors have recently proposed a segmentation model where personal and environmental variables intersect with each other to form motivating conditions that drive behavior and preferences. This model of segmentation has been applied to packaged goods. This paper extends this literature by proposing a segmentation model for low-penetration and low involvement (LP-LI) products. An application to the lottery games in Chile supports the proposed model. The results of the study show that in this type of products (LP-LI), the attitude towards the product category is the most important factor that distinguishes consumers from non consumers, and heavy users from light users, and consequently, a critical segmentation variable. In addition, a cluster analysis shows the existence of three segments: (1) the impulsive dreamers, who believe in chance, and in that lottery games can change their life, (2) the skeptical, that do not believe in chance, nor in that lottery games can change their life and (3) the willing, who value the benefits of playing.

Coupled dictionary learning for joint MR image restoration and segmentation

NASA Astrophysics Data System (ADS)

Yang, Xuesong; Fan, Yong

2018-03-01

To achieve better segmentation of MR images, image restoration is typically used as a preprocessing step, especially for low-quality MR images. Recent studies have demonstrated that dictionary learning methods could achieve promising performance for both image restoration and image segmentation. These methods typically learn paired dictionaries of image patches from different sources and use a common sparse representation to characterize paired image patches, such as low-quality image patches and their corresponding high quality counterparts for the image restoration, and image patches and their corresponding segmentation labels for the image segmentation. Since learning these dictionaries jointly in a unified framework may improve the image restoration and segmentation simultaneously, we propose a coupled dictionary learning method to concurrently learn dictionaries for joint image restoration and image segmentation based on sparse representations in a multi-atlas image segmentation framework. Particularly, three dictionaries, including a dictionary of low quality image patches, a dictionary of high quality image patches, and a dictionary of segmentation label patches, are learned in a unified framework so that the learned dictionaries of image restoration and segmentation can benefit each other. Our method has been evaluated for segmenting the hippocampus in MR T1 images collected with scanners of different magnetic field strengths. The experimental results have demonstrated that our method achieved better image restoration and segmentation performance than state of the art dictionary learning and sparse representation based image restoration and image segmentation methods.

Extraction of composite visual objects from audiovisual materials

NASA Astrophysics Data System (ADS)

Durand, Gwenael; Thienot, Cedric; Faudemay, Pascal

1999-08-01

An effective analysis of Visual Objects appearing in still images and video frames is required in order to offer fine grain access to multimedia and audiovisual contents. In previous papers, we showed how our method for segmenting still images into visual objects could improve content-based image retrieval and video analysis methods. Visual Objects are used in particular for extracting semantic knowledge about the contents. However, low-level segmentation methods for still images are not likely to extract a complex object as a whole but instead as a set of several sub-objects. For example, a person would be segmented into three visual objects: a face, hair, and a body. In this paper, we introduce the concept of Composite Visual Object. Such an object is hierarchically composed of sub-objects called Component Objects.

Estimating the concentration of gold nanoparticles incorporated on natural rubber membranes using multi-level starlet optimal segmentation

NASA Astrophysics Data System (ADS)

de Siqueira, A. F.; Cabrera, F. C.; Pagamisse, A.; Job, A. E.

2014-12-01

This study consolidates multi-level starlet segmentation (MLSS) and multi-level starlet optimal segmentation (MLSOS) techniques for photomicrograph segmentation, based on starlet wavelet detail levels to separate areas of interest in an input image. Several segmentation levels can be obtained using MLSS; after that, Matthews correlation coefficient is used to choose an optimal segmentation level, giving rise to MLSOS. In this paper, MLSOS is employed to estimate the concentration of gold nanoparticles with diameter around 47 nm, reduced on natural rubber membranes. These samples were used for the construction of SERS/SERRS substrates and in the study of the influence of natural rubber membranes with incorporated gold nanoparticles on the physiology of Leishmania braziliensis. Precision, recall, and accuracy are used to evaluate the segmentation performance, and MLSOS presents an accuracy greater than 88 % for this application.

Contextualising AIDS and human development: Long-term illness and death among adults in labouring low-caste groups in India.

PubMed

Priya, R; Sathyamala, C

2007-01-01

This study compared evidence from two low caste labouring communities in India: a relatively modernized urban group and a rural group in a backward region. It explored their levels of ill health, their capacities to respond to adult illness and the support they received. In each region, a baseline survey of approximately 1,000 households provided background quantitative evidence with qualitative evidence was collected from about 55 families. HIV infection and AIDS deaths were found to occur in the 'less poor' segments of the study group in both regions. In keeping with the official data, they formed a small proportion of the overall mortality and morbidity in this group. Stigma and discrimination were found to be low but fear of stigma was high, generated by the medical response to AIDS and used opportunistically for personal gains. The study provides insights into the structural determinants of health and coping mechanisms in these communities. The best conditions for a healthy life were found in the group that had a rooted community setting, collective political power, migrant economic support and improved working conditions--the less poor rural group. While improved economic status was associated with better health status, this relationship was stronger when combined with the presence of improved working conditions, with social cohesion at family and community levels and with political power as indicated by levels of organized collective representation and identity formation in workplace, local- and state-level politics. However, the traditional forms of social cohesion are under stress and new forms, moderated by commercial relations, are proving inadequate to meet major household shocks, like adult mortality.

Validation of refraction and anterior segment parameters by a new multi-diagnostic platform (VX120).

PubMed

Gordon-Shaag, Ariela; Piñero, David P; Kahloun, Cyril; Markov, David; Parnes, Tzadok; Gantz, Liat; Shneor, Einat

2018-03-08

The VX120 (Visionix Luneau, France) is a novel multi-diagnostic platform that combines Hartmann-Shack based autorefraction, Placido-disk based corneal-topography and anterior segment measurements made with a stationary-Scheimpflug camera. We investigate the agreement between different parameters measured by the VX120 with accepted or gold-standard techniques to test if they are interchangeable, as well as to evaluate the repeatability and reproducibility. The right-eyes of healthy subjects were included in the study. Autorefraction of the VX120 was compared to subjective refraction. Agreement of anterior segment parameters was compared to the Sirius (CSO, Italy) including autokeratometry, central corneal thickness (CCT), iridiocorneal angle (IA). Inter and intra-test repeatability of the above parameters was assessed. Results were analyzed using Bland and Altman analyses. A total of 164 eyes were evaluated. The mean difference between VX120 autorefraction and subjective refraction for sphere, spherical equivalent (SE), and cylinder was 0.01±0.43D, 0.14±0.47D, and -0.26±0.30D, respectively and high correlation was found to all parameter (r>0.75) except for J 45 (r=0.61). The mean difference between VX120 and the Sirius system for CCT, IA, and keratometry (k1 and k2) was -3.51±8.64μm, 7.6±4.2°, 0.003±0.06mm and 0.004±0.04mm, respectively and high correlation was found to all parameter (r>0.97) except for IA (r=0.67). Intrasession repeatability of VX120 refraction, CCT, IA and keratometry yielded low within-subject standard deviations. Inter-session repeatability showed no statistically significant difference for most of the parameters measured. The VX120 provides consistent refraction and most anterior segment measurements in normal healthy eyes, with high levels of intra and inter-session repeatability. Copyright © 2018. Published by Elsevier España, S.L.U.

Rule-based fuzzy vector median filters for 3D phase contrast MRI segmentation

NASA Astrophysics Data System (ADS)

Sundareswaran, Kartik S.; Frakes, David H.; Yoganathan, Ajit P.

2008-02-01

Recent technological advances have contributed to the advent of phase contrast magnetic resonance imaging (PCMRI) as standard practice in clinical environments. In particular, decreased scan times have made using the modality more feasible. PCMRI is now a common tool for flow quantification, and for more complex vector field analyses that target the early detection of problematic flow conditions. Segmentation is one component of this type of application that can impact the accuracy of the final product dramatically. Vascular segmentation, in general, is a long-standing problem that has received significant attention. Segmentation in the context of PCMRI data, however, has been explored less and can benefit from object-based image processing techniques that incorporate fluids specific information. Here we present a fuzzy rule-based adaptive vector median filtering (FAVMF) algorithm that in combination with active contour modeling facilitates high-quality PCMRI segmentation while mitigating the effects of noise. The FAVMF technique was tested on 111 synthetically generated PC MRI slices and on 15 patients with congenital heart disease. The results were compared to other multi-dimensional filters namely the adaptive vector median filter, the adaptive vector directional filter, and the scalar low pass filter commonly used in PC MRI applications. FAVMF significantly outperformed the standard filtering methods (p < 0.0001). Two conclusions can be drawn from these results: a) Filtering should be performed after vessel segmentation of PC MRI; b) Vector based filtering methods should be used instead of scalar techniques.

Variational-based segmentation of bio-pores in tomographic images

NASA Astrophysics Data System (ADS)

Bauer, Benjamin; Cai, Xiaohao; Peth, Stephan; Schladitz, Katja; Steidl, Gabriele

2017-01-01

X-ray computed tomography (CT) combined with a quantitative analysis of the resulting volume images is a fruitful technique in soil science. However, the variations in X-ray attenuation due to different soil components keep the segmentation of single components within these highly heterogeneous samples a challenging problem. Particularly demanding are bio-pores due to their elongated shape and the low gray value difference to the surrounding soil structure. Recently, variational models in connection with algorithms from convex optimization were successfully applied for image segmentation. In this paper we apply these methods for the first time for the segmentation of bio-pores in CT images of soil samples. We introduce a novel convex model which enforces smooth boundaries of bio-pores and takes the varying attenuation values in the depth into account. Segmentation results are reported for different real-world 3D data sets as well as for simulated data. These results are compared with two gray value thresholding methods, namely indicator kriging and a global thresholding procedure, and with a morphological approach. Pros and cons of the methods are assessed by considering geometric features of the segmented bio-pore systems. The variational approach features well-connected smooth pores while not detecting smaller or shallower pores. This is an advantage in cases where the main bio-pores network is of interest and where infillings, e.g., excrements of earthworms, would result in losing pore connections as observed for the other thresholding methods.

Predicting critical care unit-level complications after long-segment fusion procedures for adult spinal deformity.

PubMed

De la Garza-Ramos, Rafael; Nakhla, Jonathan; Gelfand, Yaroslav; Echt, Murray; Scoco, Aleka N; Kinon, Merritt D; Yassari, Reza

2018-03-01

To identify predictive factors for critical care unit-level complications (CCU complication) after long-segment fusion procedures for adult spinal deformity (ASD). The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database [2010-2014] was reviewed. Only adult patients who underwent fusion of 7 or more spinal levels for ASD were included. CCU complications included intraoperative arrest/infarction, ventilation >48 hours, pulmonary embolism, renal failure requiring dialysis, cardiac arrest, myocardial infarction, unplanned intubation, septic shock, stroke, coma, or new neurological deficit. A stepwise multivariate regression was used to identify independent predictors of CCU complications. Among 826 patients, the rate of CCU complications was 6.4%. On multivariate regression analysis, dependent functional status (P=0.004), combined approach (P=0.023), age (P=0.044), diabetes (P=0.048), and surgery for over 8 hours (P=0.080) were significantly associated with complication development. A simple scoring system was developed to predict complications with 0 points for patients aged <50, 1 point for patients between 50-70, 2 points for patients 70 or over, 1 point for diabetes, 2 points dependent functional status, 1 point for combined approach, and 1 point for surgery over 8 hours. The rate of CCU complications was 0.7%, 3.2%, 9.0%, and 12.6% for patients with 0, 1, 2, and 3+ points, respectively (P<0.001). The findings in this study suggest that older patients, patients with diabetes, patients who depend on others for activities of daily living, and patients who undergo combined approaches or surgery for over 8 hours may be at a significantly increased risk of developing a CCU-level complication after ASD surgery.

Ensemble Semi-supervised Frame-work for Brain Magnetic Resonance Imaging Tissue Segmentation.

PubMed

Azmi, Reza; Pishgoo, Boshra; Norozi, Narges; Yeganeh, Samira

2013-04-01

Brain magnetic resonance images (MRIs) tissue segmentation is one of the most important parts of the clinical diagnostic tools. Pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. Supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow to obtain. Moreover, they cannot use unlabeled data to train classifiers. On the other hand, unsupervised segmentation methods have no prior knowledge and lead to low level of performance. However, semi-supervised learning which uses a few labeled data together with a large amount of unlabeled data causes higher accuracy with less trouble. In this paper, we propose an ensemble semi-supervised frame-work for segmenting of brain magnetic resonance imaging (MRI) tissues that it has been used results of several semi-supervised classifiers simultaneously. Selecting appropriate classifiers has a significant role in the performance of this frame-work. Hence, in this paper, we present two semi-supervised algorithms expectation filtering maximization and MCo_Training that are improved versions of semi-supervised methods expectation maximization and Co_Training and increase segmentation accuracy. Afterward, we use these improved classifiers together with graph-based semi-supervised classifier as components of the ensemble frame-work. Experimental results show that performance of segmentation in this approach is higher than both supervised methods and the individual semi-supervised classifiers.

Scale-space for empty catheter segmentation in PCI fluoroscopic images.

PubMed

Bacchuwar, Ketan; Cousty, Jean; Vaillant, Régis; Najman, Laurent

2017-07-01

In this article, we present a method for empty guiding catheter segmentation in fluoroscopic X-ray images. The guiding catheter, being a commonly visible landmark, its segmentation is an important and a difficult brick for Percutaneous Coronary Intervention (PCI) procedure modeling. In number of clinical situations, the catheter is empty and appears as a low contrasted structure with two parallel and partially disconnected edges. To segment it, we work on the level-set scale-space of image, the min tree, to extract curve blobs. We then propose a novel structural scale-space, a hierarchy built on these curve blobs. The deep connected component, i.e. the cluster of curve blobs on this hierarchy, that maximizes the likelihood to be an empty catheter is retained as final segmentation. We evaluate the performance of the algorithm on a database of 1250 fluoroscopic images from 6 patients. As a result, we obtain very good qualitative and quantitative segmentation performance, with mean precision and recall of 80.48 and 63.04% respectively. We develop a novel structural scale-space to segment a structured object, the empty catheter, in challenging situations where the information content is very sparse in the images. Fully-automatic empty catheter segmentation in X-ray fluoroscopic images is an important and preliminary step in PCI procedure modeling, as it aids in tagging the arrival and removal location of other interventional tools.

Recovery of locomotion in the cat following spinal cord lesions.

PubMed

Rossignol, S; Bouyer, L; Barthélemy, D; Langlet, C; Leblond, H

2002-10-01

In most species, locomotor function beneath the level of a spinal cord lesion can be restored even if the cord is completely transected. This suggests that there is, within the spinal cord, an autonomous network of neurons capable of generating a locomotor pattern independently of supraspinal inputs. Recent studies suggest that several physiological and neurochemical changes have to occur in the neuronal networks located caudally to the lesion to allow the expression of spinal locomotion. Some evidence of this plasticity will be addressed in this review. In addition, original data on the functional organisation of the lumbar spinal cord will also be presented. Recent works in our lab show that segmental responsiveness of the spinal cord of the cat to locally micro-injected drugs in different lumbar segments, in combination with complete lesions at various level of the spinal cord, suggest a rostro-caudal organisation of spinal locomotor control. Moreover, the integrity of midlumbar segments seems to be crucial for the expression of spinal locomotion. These data suggest that the regions of critical importance for locomotion can be confined to a restricted portion of the spinal cord. Later, these midlumbar segments could be targeted by electrical stimulation or grafts to improve recovery of function. Understanding the changes in spinal cord neurophysiology and neurochemistry after a lesion is of critical importance to the improvement of treatments for locomotor rehabilitation in spinal-cord-injured patients.

Segmentation by fusion of histogram-based k-means clusters in different color spaces.

PubMed

Mignotte, Max

2008-05-01

This paper presents a new, simple, and efficient segmentation approach, based on a fusion procedure which aims at combining several segmentation maps associated to simpler partition models in order to finally get a more reliable and accurate segmentation result. The different label fields to be fused in our application are given by the same and simple (K-means based) clustering technique on an input image expressed in different color spaces. Our fusion strategy aims at combining these segmentation maps with a final clustering procedure using as input features, the local histogram of the class labels, previously estimated and associated to each site and for all these initial partitions. This fusion framework remains simple to implement, fast, general enough to be applied to various computer vision applications (e.g., motion detection and segmentation), and has been successfully applied on the Berkeley image database. The experiments herein reported in this paper illustrate the potential of this approach compared to the state-of-the-art segmentation methods recently proposed in the literature.

Biomechanics of Artificial Disc Replacements Adjacent to a 2-Level Fusion in 4-Level Hybrid Constructs: An In Vitro Investigation

PubMed Central

Liao, Zhenhua; Fogel, Guy R.; Wei, Na; Gu, Hongsheng; Liu, Weiqiang

2015-01-01

Background The ideal procedure for multilevel cervical degenerative disc diseases remains controversial. Recent studies on hybrid surgery combining anterior cervical discectomy and fusion (ACDF) and artificial cervical disc replacement (ACDR) for 2-level and 3-level constructs have been reported in the literature. The purpose of this study was to estimate the biomechanics of 3 kinds of 4-level hybrid constructs, which are more likely to be used clinically compared to 4-level arthrodesis. Material/Methods Eighteen human cadaveric spines (C2–T1) were evaluated in different testing conditions: intact, with 3 kinds of 4-level hybrid constructs (hybrid C3–4 ACDR+C4–6 ACDF+C6–7ACDR; hybrid C3–5ACDF+C5–6ACDR+C6–7ACDR; hybrid C3–4ACDR+C4–5ACDR+C5–7ACDF); and 4-level fusion. Results Four-level fusion resulted in significant decrease in the C3–C7 ROM compared with the intact spine. The 3 different 4-level hybrid treatment groups caused only slight change at the instrumented levels compared to intact except for flexion. At the adjacent levels, 4-level fusion resulted in significant increase of contribution of both upper and lower adjacent levels. However, for the 3 hybrid constructs, significant changes of motion increase far lower than 4P at adjacent levels were only noted in partial loading conditions. No destabilizing effect or hypermobility were observed in any 4-level hybrid construct. Conclusions Four-level fusion significantly eliminated motion within the construct and increased motion at the adjacent segments. For all 3 different 4-level hybrid constructs, ACDR normalized motion of the index segment and adjacent segments with no significant hypermobility. Compared with the 4-level ACDF condition, the artificial discs in 4-level hybrid constructs had biomechanical advantages compared to fusion in normalizing adjacent level motion. PMID:26694835

Biomechanics of Artificial Disc Replacements Adjacent to a 2-Level Fusion in 4-Level Hybrid Constructs: An In Vitro Investigation.

PubMed

Liao, Zhenhua; Fogel, Guy R; Wei, Na; Gu, Hongsheng; Liu, Weiqiang

2015-12-23

BACKGROUND The ideal procedure for multilevel cervical degenerative disc diseases remains controversial. Recent studies on hybrid surgery combining anterior cervical discectomy and fusion (ACDF) and artificial cervical disc replacement (ACDR) for 2-level and 3-level constructs have been reported in the literature. The purpose of this study was to estimate the biomechanics of 3 kinds of 4-level hybrid constructs, which are more likely to be used clinically compared to 4-level arthrodesis. MATERIAL AND METHODS Eighteen human cadaveric spines (C2-T1) were evaluated in different testing conditions: intact, with 3 kinds of 4-level hybrid constructs (hybrid C3-4 ACDR+C4-6 ACDF+C6-7ACDR; hybrid C3-5ACDF+C5-6ACDR+C6-7ACDR; hybrid C3-4ACDR+C4-5ACDR+C5-7ACDF); and 4-level fusion. RESULTS Four-level fusion resulted in significant decrease in the C3-C7 ROM compared with the intact spine. The 3 different 4-level hybrid treatment groups caused only slight change at the instrumented levels compared to intact except for flexion. At the adjacent levels, 4-level fusion resulted in significant increase of contribution of both upper and lower adjacent levels. However, for the 3 hybrid constructs, significant changes of motion increase far lower than 4P at adjacent levels were only noted in partial loading conditions. No destabilizing effect or hypermobility were observed in any 4-level hybrid construct. CONCLUSIONS Four-level fusion significantly eliminated motion within the construct and increased motion at the adjacent segments. For all 3 different 4-level hybrid constructs, ACDR normalized motion of the index segment and adjacent segments with no significant hypermobility. Compared with the 4-level ACDF condition, the artificial discs in 4-level hybrid constructs had biomechanical advantages compared to fusion in normalizing adjacent level motion.

Colonization of dodder, Cuscuta indecora, by 'Candidatus Liberibacter asiaticus' and 'Ca. L. americanus'.

PubMed

Hartung, John S; Paul, Cristina; Achor, Diann; Brlansky, R H

2010-08-01

Huanglongbing, or citrus greening, threatens the global citrus industry. The presumptive pathogens, 'Candidatus Liberibacter asiaticus' and 'Ca. L. americanus' can be transferred from citrus to more easily studied experimental hosts by using holoparasitic dodder plants. However, the interaction between 'Candidatus Liberibacter' spp. and the dodder has not been studied. We combined quantitative polymerase chain reaction with electron microscopy to show that only 65% of tendrils of Cuscuta indecora grown on 'Ca. Liberibacter' spp.-infected host plants had detectable levels of the pathogen. Among tendrils that were colonized by Liberibacter in at least one 2 cm segment, most were not colonized in all segments. Furthermore, the estimated population levels of the pathogen present in serial 2 cm segments of dodder tendrils varied widely and without any consistent pattern. Thus, there was generally not a concentration gradient of the pathogen from the source plant towards the recipient and populations of the pathogen were sometimes found in the distal segments of the dodder plant but not in the proximal or middle segments. Populations of the pathogens ranged from 2 x 10(2) to 3.0 x 10(8) cells per 2 cm segment. On a fresh weight basis, populations as high as 1.4 x 10(10) cells per g of tissue were observed demonstrating that 'Ca. Liberibacter' spp. multiplies well in Cuscuta indecora. However, 55% of individual stem segments did not contain detectable levels of the pathogen, consistent with a pattern of nonuniform colonization similar to that observed in the much more anatomically complex citrus tree. Colonization of dodder by the pathogen is also nonuniform at the ultrastructural level, with adjacent phloem vessel elements being completely full of the pathogen or free of the pathogen. We also observed bacteria in the phloem vessels that belonged to two distinct size classes based on the diameters of cross sections of cells. In other sections from the same tendrils we observed single bacterial cells that were apparently in the process of differentiating between the large and round forms to the long and thin forms (or vice versa). The process controlling this morphological differentiation of the pathogen is not known. The highly reduced and simplified anatomy of the dodder plant as well as its rapid growth rate compared with citrus, and the ability of the plant to support multiplication of the pathogen to high levels, makes it an interesting host plant for further studies of host-pathogen interactions.

Study on a New Combination Method and High Efficiency Outer Rotor Type Permanent Magnet Motors

NASA Astrophysics Data System (ADS)

Enomoto, Yuji; Kitamura, Masashi; Motegi, Yasuaki; Andoh, Takashi; Ochiai, Makoto; Abukawa, Toshimi

The segment stator core, high space factor coil, and high efficiency magnet are indispensable technologies in the development of compact and a high efficiency motors. But adoption of the segment stator core and high space factor coil has not progressed in the field of outer rotor type motors, for the reason that the inner components cannot be laser welded together. Therefore, we have examined a segment stator core combination technology for the purposes of getting a large increase in efficiency and realizing miniaturization. We have also developed a characteristic estimation method which provides the most suitable performance for segment stator core motors.

High-resolution comparative modeling with RosettaCM.

PubMed

Song, Yifan; DiMaio, Frank; Wang, Ray Yu-Ruei; Kim, David; Miles, Chris; Brunette, Tj; Thompson, James; Baker, David

2013-10-08

We describe an improved method for comparative modeling, RosettaCM, which optimizes a physically realistic all-atom energy function over the conformational space defined by homologous structures. Given a set of sequence alignments, RosettaCM assembles topologies by recombining aligned segments in Cartesian space and building unaligned regions de novo in torsion space. The junctions between segments are regularized using a loop closure method combining fragment superposition with gradient-based minimization. The energies of the resulting models are optimized by all-atom refinement, and the most representative low-energy model is selected. The CASP10 experiment suggests that RosettaCM yields models with more accurate side-chain and backbone conformations than other methods when the sequence identity to the templates is greater than ∼15%. Copyright © 2013 Elsevier Ltd. All rights reserved.

Medical Image Segmentation by Combining Graph Cut and Oriented Active Appearance Models

PubMed Central

Chen, Xinjian; Udupa, Jayaram K.; Bağcı, Ulaş; Zhuge, Ying; Yao, Jianhua

2017-01-01

In this paper, we propose a novel 3D segmentation method based on the effective combination of the active appearance model (AAM), live wire (LW), and graph cut (GC). The proposed method consists of three main parts: model building, initialization, and segmentation. In the model building part, we construct the AAM and train the LW cost function and GC parameters. In the initialization part, a novel algorithm is proposed for improving the conventional AAM matching method, which effectively combines the AAM and LW method, resulting in Oriented AAM (OAAM). A multi-object strategy is utilized to help in object initialization. We employ a pseudo-3D initialization strategy, and segment the organs slice by slice via multi-object OAAM method. For the segmentation part, a 3D shape constrained GC method is proposed. The object shape generated from the initialization step is integrated into the GC cost computation, and an iterative GC-OAAM method is used for object delineation. The proposed method was tested in segmenting the liver, kidneys, and spleen on a clinical CT dataset and also tested on the MICCAI 2007 grand challenge for liver segmentation training dataset. The results show the following: (a) An overall segmentation accuracy of true positive volume fraction (TPVF) > 94.3%, false positive volume fraction (FPVF) < 0.2% can be achieved. (b) The initialization performance can be improved by combining AAM and LW. (c) The multi-object strategy greatly facilitates the initialization. (d) Compared to the traditional 3D AAM method, the pseudo 3D OAAM method achieves comparable performance while running 12 times faster. (e) The performance of proposed method is comparable to the state of the art liver segmentation algorithm. The executable version of 3D shape constrained GC with user interface can be downloaded from website http://xinjianchen.wordpress.com/research/. PMID:22311862

Recurrent Word Combinations in EAP Test-Taker Writing: Differences between High- and Low-Proficiency Levels

ERIC Educational Resources Information Center

Appel, Randy; Wood, David

2016-01-01

The correct use of frequently occurring word combinations represents an important part of language proficiency in spoken and written discourse. This study investigates the use of English-language recurrent word combinations in low-level and high-level L2 English academic essays sourced from the Canadian Academic English Language (CAEL) assessment.…

Health evaluation of pronghorns (Antilocapra americana) on Hart Mountain National Antelope Refuge in southeastern Oregon, 1996-1997.

USGS Publications Warehouse

Dunbar, Michael R.; Velarde, Roser

1998-01-01

One hundred four neonatal (fawns) and 40 adult female (does) pronghorn antelope (pronghorns) (Antilocapra americana) were captured on the Hart Mountain National Antelope Refuge (HMNAR) in Lake County, southeastern Oregon, between 13 May 1996 and 26 May 1997. Blood and fecal samples were taken for an investigation of low fawn survival that may be due to disease and/or poor nutrition. No abnormalities were found in hematological parameters of adult does (n = 40) or fawns (n = 44 to 67). In general, there were lower serum total proteins (TP) and Blood Urea Nitrogen (BUN) concentrations in this population than in other populations from Alberta, Canada; Idaho; and Baker City, Oregon. Mean BUN values in does were significantly lower (p < 0.001) in December than March. The duration of this apparently low protein content of the December diet may affect the weight of the newborn and consequent survivability if it should continue into late gestation (March-May). Serum copper (Cu) levels in does (range 0.39 to 0.74 ppm) were considered marginal when compared to domestic animals and some wild ungulates. Fawns had low but apparently normal Cu levels at birth and reached the does' marginal values in about three days. Whole blood and serum Selenium (Se) levels (<100 ng/ml) were considered to be marginal to low in most segments of the pronghorn population in this study. However, serum levels of vitamin E (range 1.98 - 3.27 pg/ml), as determined from the does captured in March, are apparently sufficient to offset any signs of deficiency due to low Se levels. No clinical signs of Cu or Se deficiency were observed. Does captured in December 1996 were tested for neutralizing antibodies to Brucella spp. (n = 20, neg.), Leptospira spp. (n = 20, neg.), bluetongue virus (n = 20, 35% pos.), epizootic hemorrhagic disease virus (n = 20, 30% pos.), respiratory syncytial virus (n = 18, neg.), parainfluenza virus type 3 (n = 18, 67% pos.), infectious bovine rhinotracheitis (n = 18, neg.), and bovine viral diarrhea (n = 18, neg.). Seventeen fawns (9F,8M), nine in 1996 and eight in 1997, survived until at least mid-July each year. Fifty-five of 87 dead fawns were necropsied. Predation by coyotes (Canis latrans) accounted for the majority of fawn mortality (62%), as determined by necropsy, during the two combined summer periods. Other causes of mortality for the combined years included predation by eagle (4%), dystocia (2%), septicemic pasteurellosis (5%), starvation/weak fawn syndrome

Classification of CT examinations for COPD visual severity analysis

NASA Astrophysics Data System (ADS)

Tan, Jun; Zheng, Bin; Wang, Xingwei; Pu, Jiantao; Gur, David; Sciurba, Frank C.; Leader, J. Ken

2012-03-01

In this study we present a computational method of CT examination classification into visual assessed emphysema severity. The visual severity categories ranged from 0 to 5 and were rated by an experienced radiologist. The six categories were none, trace, mild, moderate, severe and very severe. Lung segmentation was performed for every input image and all image features are extracted from the segmented lung only. We adopted a two-level feature representation method for the classification. Five gray level distribution statistics, six gray level co-occurrence matrix (GLCM), and eleven gray level run-length (GLRL) features were computed for each CT image depicted segment lung. Then we used wavelets decomposition to obtain the low- and high-frequency components of the input image, and again extract from the lung region six GLCM features and eleven GLRL features. Therefore our feature vector length is 56. The CT examinations were classified using the support vector machine (SVM) and k-nearest neighbors (KNN) and the traditional threshold (density mask) approach. The SVM classifier had the highest classification performance of all the methods with an overall sensitivity of 54.4% and a 69.6% sensitivity to discriminate "no" and "trace visually assessed emphysema. We believe this work may lead to an automated, objective method to categorically classify emphysema severity on CT exam.

Change detection of polarimetric SAR images based on the KummerU Distribution

NASA Astrophysics Data System (ADS)

Chen, Quan; Zou, Pengfei; Li, Zhen; Zhang, Ping

2014-11-01

In the society of PolSAR image segmentation, change detection and classification, the classical Wishart distribution has been used for a long time, but it especially suit to low-resolution SAR image, because in traditional sensors, only a small number of scatterers are present in each resolution cell. With the improving of SAR systems these years, the classical statistical models can therefore be reconsidered for high resolution and polarimetric information contained in the images acquired by these advanced systems. In this study, SAR image segmentation algorithm based on level-set method, added with distance regularized level-set evolution (DRLSE) is performed using Envisat/ASAR single-polarization data and Radarsat-2 polarimetric images, respectively. KummerU heterogeneous clutter model is used in the later to overcome the homogeneous hypothesis at high resolution cell. An enhanced distance regularized level-set evolution (DRLSE-E) is also applied in the later, to ensure accurate computation and stable level-set evolution. Finally, change detection based on four polarimetric Radarsat-2 time series images is carried out at Genhe area of Inner Mongolia Autonomous Region, NorthEastern of China, where a heavy flood disaster occurred during the summer of 2013, result shows the recommend segmentation method can detect the change of watershed effectively.

Evaluating fuzzy operators of an object-based image analysis for detecting landslides and their changes

NASA Astrophysics Data System (ADS)

Feizizadeh, Bakhtiar; Blaschke, Thomas; Tiede, Dirk; Moghaddam, Mohammad Hossein Rezaei

2017-09-01

This article presents a method of object-based image analysis (OBIA) for landslide delineation and landslide-related change detection from multi-temporal satellite images. It uses both spatial and spectral information on landslides, through spectral analysis, shape analysis, textural measurements using a gray-level co-occurrence matrix (GLCM), and fuzzy logic membership functionality. Following an initial segmentation step, particular combinations of various information layers were investigated to generate objects. This was achieved by applying multi-resolution segmentation to IRS-1D, SPOT-5, and ALOS satellite imagery in sequential steps of feature selection and object classification, and using slope and flow direction derivatives from a digital elevation model together with topographically-oriented gray level co-occurrence matrices. Fuzzy membership values were calculated for 11 different membership functions using 20 landslide objects from a landslide training data. Six fuzzy operators were used for the final classification and the accuracies of the resulting landslide maps were compared. A Fuzzy Synthetic Evaluation (FSE) approach was adapted for validation of the results and for an accuracy assessment using the landslide inventory database. The FSE approach revealed that the AND operator performed best with an accuracy of 93.87% for 2005 and 94.74% for 2011, closely followed by the MEAN Arithmetic operator, while the OR and AND (*) operators yielded relatively low accuracies. An object-based change detection was then applied to monitor landslide-related changes that occurred in northern Iran between 2005 and 2011. Knowledge rules to detect possible landslide-related changes were developed by evaluating all possible landslide-related objects for both time steps.

Design of a bullet beam pattern of a micro ultrasound transducer (Conference Presentation)

NASA Astrophysics Data System (ADS)

Roh, Yongrae; Lee, Seongmin

2016-04-01

Ultrasonic imaging transducer is often required to compose a beam pattern of a low sidelobe level and a small beam width over a long focal region to achieve good image resolution. Normal ultrasound transducers have many channels along its azimuth, which allows easy formation of the sound beam into a desired shape. However, micro-array transducers have no control of the beam pattern along their elevation. In this work, a new method is proposed to manipulate the beam pattern by using an acoustic multifocal lens and a shaded electrode on top of the piezoelectric layer. The shading technique split an initial uniform electrode into several segments and combined those segments to compose a desired beam pattern. For a given elevation width and frequency, the optimal pattern of the split electrodes was determined by means of the OptQuest-Nonlinear Program (OQ-NLP) algorithm to achieve the lowest sidelobe level. The requirement to achieve a small beam width with a long focal region was satisfied by employing an acoustic lens of three multiple focuses. Optimal geometry of the multifocal lens such as the radius of curvature and aperture diameter for each focal point was also determined by the OQ-NLP algorithm. For the optimization, a new index was devised to evaluate the on-axis response: focal region ratio = focal region / minimum beam width. The larger was the focal region ratio, the better was the beam pattern. Validity of the design has been verified through fabricating and characterizing an experimental prototype of the transducer.

Neural network fusion: a novel CT-MR aortic aneurysm image segmentation method

NASA Astrophysics Data System (ADS)

Wang, Duo; Zhang, Rui; Zhu, Jin; Teng, Zhongzhao; Huang, Yuan; Spiga, Filippo; Du, Michael Hong-Fei; Gillard, Jonathan H.; Lu, Qingsheng; Liò, Pietro

2018-03-01

Medical imaging examination on patients usually involves more than one imaging modalities, such as Computed Tomography (CT), Magnetic Resonance (MR) and Positron Emission Tomography(PET) imaging. Multimodal imaging allows examiners to benefit from the advantage of each modalities. For example, for Abdominal Aortic Aneurysm, CT imaging shows calcium deposits in the aorta clearly while MR imaging distinguishes thrombus and soft tissues better.1 Analysing and segmenting both CT and MR images to combine the results will greatly help radiologists and doctors to treat the disease. In this work, we present methods on using deep neural network models to perform such multi-modal medical image segmentation. As CT image and MR image of the abdominal area cannot be well registered due to non-affine deformations, a naive approach is to train CT and MR segmentation network separately. However, such approach is time-consuming and resource-inefficient. We propose a new approach to fuse the high-level part of the CT and MR network together, hypothesizing that neurons recognizing the high level concepts of Aortic Aneurysm can be shared across multiple modalities. Such network is able to be trained end-to-end with non-registered CT and MR image using shorter training time. Moreover network fusion allows a shared representation of Aorta in both CT and MR images to be learnt. Through experiments we discovered that for parts of Aorta showing similar aneurysm conditions, their neural presentations in neural network has shorter distances. Such distances on the feature level is helpful for registering CT and MR image.

Reproducibility measurements of three methods for calculating in vivo MR-based knee kinematics.

PubMed

Lansdown, Drew A; Zaid, Musa; Pedoia, Valentina; Subburaj, Karupppasamy; Souza, Richard; Benjamin, C; Li, Xiaojuan

2015-08-01

To describe three quantification methods for magnetic resonance imaging (MRI)-based knee kinematic evaluation and to report on the reproducibility of these algorithms. T2 -weighted, fast-spin echo images were obtained of the bilateral knees in six healthy volunteers. Scans were repeated for each knee after repositioning to evaluate protocol reproducibility. Semiautomatic segmentation defined regions of interest for the tibia and femur. The posterior femoral condyles and diaphyseal axes were defined using the previously defined tibia and femur. All segmentation was performed twice to evaluate segmentation reliability. Anterior tibial translation (ATT) and internal tibial rotation (ITR) were calculated using three methods: a tibial-based registration system, a combined tibiofemoral-based registration method with all manual segmentation, and a combined tibiofemoral-based registration method with automatic definition of condyles and axes. Intraclass correlation coefficients and standard deviations across multiple measures were determined. Reproducibility of segmentation was excellent (ATT = 0.98; ITR = 0.99) for both combined methods. ATT and ITR measurements were also reproducible across multiple scans in the combined registration measurements with manual (ATT = 0.94; ITR = 0.94) or automatic (ATT = 0.95; ITR = 0.94) condyles and axes. The combined tibiofemoral registration with automatic definition of the posterior femoral condyle and diaphyseal axes allows for improved knee kinematics quantification with excellent in vivo reproducibility. © 2014 Wiley Periodicals, Inc.

A kind of color image segmentation algorithm based on super-pixel and PCNN

NASA Astrophysics Data System (ADS)

Xu, GuangZhu; Wang, YaWen; Zhang, Liu; Zhao, JingJing; Fu, YunXia; Lei, BangJun

2018-04-01

Image segmentation is a very important step in the low-level visual computing. Although image segmentation has been studied for many years, there are still many problems. PCNN (Pulse Coupled Neural network) has biological background, when it is applied to image segmentation it can be viewed as a region-based method, but due to the dynamics properties of PCNN, many connectionless neurons will pulse at the same time, so it is necessary to identify different regions for further processing. The existing PCNN image segmentation algorithm based on region growing is used for grayscale image segmentation, cannot be directly used for color image segmentation. In addition, the super-pixel can better reserve the edges of images, and reduce the influences resulted from the individual difference between the pixels on image segmentation at the same time. Therefore, on the basis of the super-pixel, the original PCNN algorithm based on region growing is improved by this paper. First, the color super-pixel image was transformed into grayscale super-pixel image which was used to seek seeds among the neurons that hadn't been fired. And then it determined whether to stop growing by comparing the average of each color channel of all the pixels in the corresponding regions of the color super-pixel image. Experiment results show that the proposed algorithm for the color image segmentation is fast and effective, and has a certain effect and accuracy.

A novel pipeline for adrenal tumour segmentation.

PubMed

Koyuncu, Hasan; Ceylan, Rahime; Erdogan, Hasan; Sivri, Mesut

2018-06-01

Adrenal tumours occur on adrenal glands surrounded by organs and osteoid. These tumours can be categorized as either functional, non-functional, malign, or benign. Depending on their appearance in the abdomen, adrenal tumours can arise from one adrenal gland (unilateral) or from both adrenal glands (bilateral) and can connect with other organs, including the liver, spleen, pancreas, etc. This connection phenomenon constitutes the most important handicap against adrenal tumour segmentation. Size change, variety of shape, diverse location, and low contrast (similar grey values between the various tissues) are other disadvantages compounding segmentation difficulty. Few studies have considered adrenal tumour segmentation, and no significant improvement has been achieved for unilateral, bilateral, adherent, or noncohesive tumour segmentation. There is also no recognised segmentation pipeline or method for adrenal tumours including different shape, size, or location information. This study proposes an adrenal tumour segmentation (ATUS) pipeline designed to eliminate the above disadvantages for adrenal tumour segmentation. ATUS incorporates a number of image methods, including contrast limited adaptive histogram equalization, split and merge based on quadtree decomposition, mean shift segmentation, large grey level eliminator, and region growing. Performance assessment of ATUS was realised on 32 arterial and portal phase computed tomography images using six metrics: dice, jaccard, sensitivity, specificity, accuracy, and structural similarity index. ATUS achieved remarkable segmentation performance, and was not affected by the discussed handicaps, on particularly adherence to other organs, with success rates of 83.06%, 71.44%, 86.44%, 99.66%, 99.43%, and 98.51% for the metrics, respectively, for images including sufficient contrast uptake. The proposed ATUS system realises detailed adrenal tumour segmentation, and avoids known disadvantages preventing accurate segmentation. Copyright © 2018 Elsevier B.V. All rights reserved.

Texture variations suppress suprathreshold brightness and colour variations.

PubMed

Schofield, Andrew J; Kingdom, Frederick A A

2014-01-01

Discriminating material changes from illumination changes is a key function of early vision. Luminance cues are ambiguous in this regard, but can be disambiguated by co-incident changes in colour and texture. Thus, colour and texture are likely to be given greater prominence than luminance for object segmentation, and better segmentation should in turn produce stronger grouping. We sought to measure the relative strengths of combined luminance, colour and texture contrast using a suprathreshhold, psychophysical grouping task. Stimuli comprised diagonal grids of circular patches bordered by a thin black line and contained combinations of luminance decrements with either violet, red, or texture increments. There were two tasks. In the Separate task the different cues were presented separately in a two-interval design, and participants indicated which interval contained the stronger orientation structure. In the Combined task the cues were combined to produce competing orientation structure in a single image. Participants had to indicate which orientation, and therefore which cue was dominant. Thus we established the relative grouping strength of each cue pair presented separately, and compared this to their relative grouping strength when combined. In this way we observed suprathreshold interactions between cues and were able to assess cue dominance at ecologically relevant signal levels. Participants required significantly more luminance and colour compared to texture contrast in the Combined compared to Separate conditions (contrast ratios differed by about 0.1 log units), showing that suprathreshold texture dominates colour and luminance when the different cues are presented in combination.

Combining population and patient-specific characteristics for prostate segmentation on 3D CT images

NASA Astrophysics Data System (ADS)

Ma, Ling; Guo, Rongrong; Tian, Zhiqiang; Venkataraman, Rajesh; Sarkar, Saradwata; Liu, Xiabi; Tade, Funmilayo; Schuster, David M.; Fei, Baowei

2016-03-01

Prostate segmentation on CT images is a challenging task. In this paper, we explore the population and patient-specific characteristics for the segmentation of the prostate on CT images. Because population learning does not consider the inter-patient variations and because patient-specific learning may not perform well for different patients, we are combining the population and patient-specific information to improve segmentation performance. Specifically, we train a population model based on the population data and train a patient-specific model based on the manual segmentation on three slice of the new patient. We compute the similarity between the two models to explore the influence of applicable population knowledge on the specific patient. By combining the patient-specific knowledge with the influence, we can capture the population and patient-specific characteristics to calculate the probability of a pixel belonging to the prostate. Finally, we smooth the prostate surface according to the prostate-density value of the pixels in the distance transform image. We conducted the leave-one-out validation experiments on a set of CT volumes from 15 patients. Manual segmentation results from a radiologist serve as the gold standard for the evaluation. Experimental results show that our method achieved an average DSC of 85.1% as compared to the manual segmentation gold standard. This method outperformed the population learning method and the patient-specific learning approach alone. The CT segmentation method can have various applications in prostate cancer diagnosis and therapy.

Occurrence and distribution of fecal indicator bacteria, and physical and chemical indicators of water quality in streams receiving discharge from Dallas/Fort Worth International Airport and vicinity, North-Central Texas, 2008

USGS Publications Warehouse

Harwell, Glenn R.; Mobley, Craig A.

2009-01-01

This report, done by the U.S. Geological Survey in cooperation with Dallas/Fort Worth International (DFW) Airport in 2008, describes the occurrence and distribution of fecal indicator bacteria (fecal coliform and Escherichia [E.] coli), and the physical and chemical indicators of water quality (relative to Texas Surface Water Quality Standards), in streams receiving discharge from DFW Airport and vicinity. At sampling sites in the lower West Fork Trinity River watershed during low-flow conditions, geometric mean E. coli counts for five of the eight West Fork Trinity River watershed sampling sites exceeded the Texas Commission on Environmental Quality E. coli criterion, thus not fully supporting contact recreation. Two of the five sites with geometric means that exceeded the contact recreation criterion are airport discharge sites, which here means that the major fraction of discharge at those sites is from DFW Airport. At sampling sites in the Elm Fork Trinity River watershed during low-flow conditions, geometric mean E. coli counts exceeded the geometric mean contact recreation criterion for seven (four airport, three non-airport) of 13 sampling sites. Under low-flow conditions in the lower West Fork Trinity River watershed, E. coli counts for airport discharge sites were significantly different from (lower than) E. coli counts for non-airport sites. Under low-flow conditions in the Elm Fork Trinity River watershed, there was no significant difference between E. coli counts for airport sites and non-airport sites. During stormflow conditions, fecal indicator bacteria counts at the most downstream (integrator) sites in each watershed were considerably higher than counts at those two sites during low-flow conditions. When stormflow sample counts are included with low-flow sample counts to compute a geometric mean for each site, classification changes from fully supporting to not fully supporting contact recreation on the basis of the geometric mean contact recreation criterion. All water temperature measurements at sampling sites in the lower West Fork Trinity River watershed were less than the maximum criterion for water temperature for the lower West Fork Trinity segment. Of the measurements at sampling sites in the Elm Fork Trinity River watershed, 95 percent were less than the maximum criterion for water temperature for the Elm Fork Trinity River segment. All dissolved oxygen concentrations were greater than the minimum criterion for stream segments classified as exceptional aquatic life use. Nearly all pH measurements were within the pH criterion range for the classified segments in both watersheds, except for those at one airport site. For sampling sites in the lower West Fork Trinity River watershed, all annual average dissolved solids concentrations were less than the maximum criterion for the lower West Fork Trinity segment. For sampling sites in the Elm Fork Trinity River, nine of the 13 sites (six airport, three non-airport) had annual averages that exceeded the maximum criterion for that segment. For ammonia, 23 samples from 12 different sites had concentrations that exceeded the screening level for ammonia. Of these 12 sites, only one non-airport site had more than the required number of exceedances to indicate a screening level concern. Stormflow total suspended solids concentrations were significantly higher than low-flow concentrations at the two integrator sites. For sampling sites in the lower West Fork Trinity River watershed, all annual average chloride concentrations were less than the maximum annual average chloride concentration criterion for that segment. For the 13 sampling sites in the Elm Fork Trinity River watershed, one non-airport site had an annual average concentration that exceeded the maximum annual average chloride concentration criterion for that segment.

Do Postoperative Biomechanical Changes Induce Heterotopic Ossification After Cervical Arthroplasty?: A 5-Year Follow-up Study.

PubMed

Kim, Keun Su; Heo, Dong Hwa

2016-07-01

Prospective clinical study. To evaluate the factors that would predispose a patient to heterotopic ossification (HO) formation after cervical arthroplasty. HO after arthroplasty is one of the complications of cervical total disk replacement (TDR). However, the predisposing factors and pathophysiology of HO have not been precisely described. We prospectively enrolled and followed up 23 patients, who received single-level arthroplasty with ProDisc-C, for 5 years after the operation. The patients who developed grade 3 or 4 HO were classified into the "high-grade HO group," whereas the patients with grade 0, 1, or 2 HO were classified into the "low-grade HO group." We compared the postoperative changes in the range of motion (ROM) and height of the functional segmental unit (FSU) of the implantation segments between the 2 groups. The mean differences in height and ROM of the FSU were 2.59±1.42 mm and 6.7±3.2 degrees in the high-grade HO group, and 0.87±0.72 mm and 3.1±2.8 degrees in the low-grade HO group. The mean differences in height and ROM of the FSU were significantly higher in the high-grade HO group than in the low-grade HO group (P<0.05). After cervical arthroplasty, the height of the FSU and ROM of the implantation segments were significantly increased in the high-grade HO group compared with the low-grade HO group. Overcorrection of the height of the FSU and increase in the ROM of the implantation segment may influence the formation of HOs after cervical arthroplasty.

Acoustic-noise-optimized diffusion-weighted imaging.

PubMed

Ott, Martin; Blaimer, Martin; Grodzki, David M; Breuer, Felix A; Roesch, Julie; Dörfler, Arnd; Heismann, Björn; Jakob, Peter M

2015-12-01

This work was aimed at reducing acoustic noise in diffusion-weighted MR imaging (DWI) that might reach acoustic noise levels of over 100 dB(A) in clinical practice. A diffusion-weighted readout-segmented echo-planar imaging (EPI) sequence was optimized for acoustic noise by utilizing small readout segment widths to obtain low gradient slew rates and amplitudes instead of faster k-space coverage. In addition, all other gradients were optimized for low slew rates. Volunteer and patient imaging experiments were conducted to demonstrate the feasibility of the method. Acoustic noise measurements were performed and analyzed for four different DWI measurement protocols at 1.5T and 3T. An acoustic noise reduction of up to 20 dB(A) was achieved, which corresponds to a fourfold reduction in acoustic perception. The image quality was preserved at the level of a standard single-shot (ss)-EPI sequence, with a 27-54% increase in scan time. The diffusion-weighted imaging technique proposed in this study allowed a substantial reduction in the level of acoustic noise compared to standard single-shot diffusion-weighted EPI. This is expected to afford considerably more patient comfort, but a larger study would be necessary to fully characterize the subjective changes in patient experience.

Development of High Resolution Mirrors and Cd-Zn-Te Detectors for Hard X-ray Astronomy

NASA Technical Reports Server (NTRS)

Ramsey, Brian D.; Speegle, Chet O.; Gaskin, Jessica; Sharma, Dharma; Engelhaupt, Darell; Six, N. Frank (Technical Monitor)

2002-01-01

We describe the fabrication and implementation of a high-resolution conical, grazing- incidence, hard X-ray (20-70 keV) telescope. When flown aboard stratospheric balloons, these mirrors are used to image cosmic sources such as supernovae, neutron stars, and quasars. The fabrication process involves generating super-polished mandrels, mirror shell electroforming, and mirror testing. The cylindrical mandrels consist of two conical segments; each segment is approximately 305 mm long. These mandrels are first, precision ground to within approx. 1.0 micron straightness along each conical segment and then lapped and polished to less than 0.5 micron straightness. Each mandrel segment is the super-polished to an average surface roughness of approx. 3.25 angstrom rms. By mirror shell replication, this combination of good figure and low surface roughness has enabled us to achieve 15 arcsec, confirmed by X-ray measurements in the Marshall Space Flight Center 102 meter test facility. To image the focused X-rays requires a focal plane detector with appropriate spatial resolution. For 15 arcsec optics of 6 meter focal length, this resolution must be around 200 microns. In addition, the detector must have a high efficiency, relatively high energy resolution, and low background. We are currently developing Cadmium-Zinc-Telluride fine-pixel detectors for this purpose. The detectors under study consist of a 16x16 pixel array with a pixel pitch of 300 microns and are 1 mm and 2 mm thick. At 60 keV, the measured energy resolution is around 2%.

Optimizing Scoring and Sampling Methods for Assessing Built Neighborhood Environment Quality in Residential Areas

PubMed Central

Adu-Brimpong, Joel; Coffey, Nathan; Ayers, Colby; Berrigan, David; Yingling, Leah R.; Thomas, Samantha; Mitchell, Valerie; Ahuja, Chaarushi; Rivers, Joshua; Hartz, Jacob; Powell-Wiley, Tiffany M.

2017-01-01

Optimization of existing measurement tools is necessary to explore links between aspects of the neighborhood built environment and health behaviors or outcomes. We evaluate a scoring method for virtual neighborhood audits utilizing the Active Neighborhood Checklist (the Checklist), a neighborhood audit measure, and assess street segment representativeness in low-income neighborhoods. Eighty-two home neighborhoods of Washington, D.C. Cardiovascular Health/Needs Assessment (NCT01927783) participants were audited using Google Street View imagery and the Checklist (five sections with 89 total questions). Twelve street segments per home address were assessed for (1) Land-Use Type; (2) Public Transportation Availability; (3) Street Characteristics; (4) Environment Quality and (5) Sidewalks/Walking/Biking features. Checklist items were scored 0–2 points/question. A combinations algorithm was developed to assess street segments’ representativeness. Spearman correlations were calculated between built environment quality scores and Walk Score®, a validated neighborhood walkability measure. Street segment quality scores ranged 10–47 (Mean = 29.4 ± 6.9) and overall neighborhood quality scores, 172–475 (Mean = 352.3 ± 63.6). Walk scores® ranged 0–91 (Mean = 46.7 ± 26.3). Street segment combinations’ correlation coefficients ranged 0.75–1.0. Significant positive correlations were found between overall neighborhood quality scores, four of the five Checklist subsection scores, and Walk Scores® (r = 0.62, p < 0.001). This scoring method adequately captures neighborhood features in low-income, residential areas and may aid in delineating impact of specific built environment features on health behaviors and outcomes. PMID:28282878

Predictors of Segmented School Day Physical Activity and Sedentary Time in Children from a Northwest England Low-Income Community

PubMed Central

Taylor, Sarah L.; Curry, Whitney B.; Knowles, Zoe R.; Noonan, Robert J.; McGrane, Bronagh; Fairclough, Stuart J.

2017-01-01

Background: Schools have been identified as important settings for health promotion through physical activity participation, particularly as children are insufficiently active for health. The aim of this study was to investigate the child and school-level influences on children′s physical activity levels and sedentary time during school hours in a sample of children from a low-income community; Methods: One hundred and eighty-six children (110 boys) aged 9–10 years wore accelerometers for 7 days, with 169 meeting the inclusion criteria of 16 h∙day−1 for a minimum of three week days. Multilevel prediction models were constructed to identify significant predictors of sedentary time, light, and moderate to vigorous physical activity during school hour segments. Child-level predictors (sex, weight status, maturity offset, cardiorespiratory fitness, physical activity self-efficacy, physical activity enjoyment) and school-level predictors (number on roll, playground area, provision score) were entered into the models; Results: Maturity offset, fitness, weight status, waist circumference-to-height ratio, sedentary time, moderate to vigorous physical activity, number of children on roll and playground area significantly predicted physical activity and sedentary time; Conclusions: Research should move towards considering context-specific physical activity and its correlates to better inform intervention strategies. PMID:28509887

A level set method for multiple sclerosis lesion segmentation.

PubMed

Zhao, Yue; Guo, Shuxu; Luo, Min; Shi, Xue; Bilello, Michel; Zhang, Shaoxiang; Li, Chunming

2018-06-01

In this paper, we present a level set method for multiple sclerosis (MS) lesion segmentation from FLAIR images in the presence of intensity inhomogeneities. We use a three-phase level set formulation of segmentation and bias field estimation to segment MS lesions and normal tissue region (including GM and WM) and CSF and the background from FLAIR images. To save computational load, we derive a two-phase formulation from the original multi-phase level set formulation to segment the MS lesions and normal tissue regions. The derived method inherits the desirable ability to precisely locate object boundaries of the original level set method, which simultaneously performs segmentation and estimation of the bias field to deal with intensity inhomogeneity. Experimental results demonstrate the advantages of our method over other state-of-the-art methods in terms of segmentation accuracy. Copyright © 2017 Elsevier Inc. All rights reserved.

Lordosis restoration after anterior longitudinal ligament release and placement of lateral hyperlordotic interbody cages during the minimally invasive lateral transpsoas approach: a radiographic study in cadavers.

PubMed

Uribe, Juan S; Smith, Donald A; Dakwar, Elias; Baaj, Ali A; Mundis, Gregory M; Turner, Alexander W L; Cornwall, G Bryan; Akbarnia, Behrooz A

2012-11-01

In the surgical treatment of spinal deformities, the importance of restoring lumbar lordosis is well recognized. Smith-Petersen osteotomies (SPOs) yield approximately 10° of lordosis per level, whereas pedicle subtraction osteotomies result in as much as 30° increased lumbar lordosis. Recently, selective release of the anterior longitudinal ligament (ALL) and placement of lordotic interbody grafts using the minimally invasive lateral retroperitoneal transpsoas approach (XLIF) has been performed as an attempt to increase lumbar lordosis while avoiding the morbidity of osteotomy. The objective of the present study was to measure the effect of the selective release of the ALL and varying degrees of lordotic implants placed using the XLIF approach on segmental lumbar lordosis in cadaveric specimens between L-1 and L-5. Nine adult fresh-frozen cadaveric specimens were placed in the lateral decubitus position. Lateral radiographs were obtained at baseline and after 4 interventions at each level as follows: 1) placement of a standard 10° lordotic cage, 2) ALL release and placement of a 10° lordotic cage, 3) ALL release and placement of a 20° lordotic cage, and 4) ALL release and placement of a 30° lordotic cage. All four cages were implanted sequentially at each interbody level between L-1 and L-5. Before and after each intervention, segmental lumbar lordosis was measured in all specimens at each interbody level between L-1 and L-5 using the Cobb method on lateral radiography. The mean baseline segmental lordotic angles at L1-2, L2-3, L3-4, and L4-5 were -3.8°, 3.8°, 7.8°, and 22.6°, respectively. The mean lumbar lordosis was 29.4°. Compared with baseline, the mean postimplantation increase in segmental lordosis in all levels combined was 0.9° in Intervention 1 (10° cage without ALL release); 4.1° in Intervention 2 (ALL release with 10° cage); 9.5° in Intervention 3 (ALL release with 20° cage); and 11.6° in Intervention 4 (ALL release with 30° cage). Foraminal height in the same sequence of conditions increased by 6.3%, 4.6%, 8.8% and 10.4%, respectively, while central disc height increased by 16.1%, 22.3%, 52.0% and 66.7%, respectively. Following ALL release and placement of lordotic cages at all 4 lumbar levels, the average global lumbar lordosis increase from preoperative lordosis was 3.2° using 10° cages, 12.0° using 20° cages, and 20.3° using 30° cages. Global lumbar lordosis with the cages at 4 levels exhibited a negative correlation with preoperative global lordosis (10°, R = -0.756; 20°, -0.730; and 30°, R = -0.437). Combined ALL release and placement of increasingly lordotic lateral interbody cages leads to progressive gains in segmental lordosis in the lumbar spine. Mean global lumbar lordosis similarly increased with increasingly lordotic cages, although the effect with a single cage could not be evaluated. Greater global lordosis was achieved with smaller preoperative lordosis. The mean maximum increase in segmental lordosis of 11.6° followed ALL release and placement of the 30° cage.

Segmented-memory recurrent neural networks.

PubMed

Chen, Jinmiao; Chaudhari, Narendra S

2009-08-01

Conventional recurrent neural networks (RNNs) have difficulties in learning long-term dependencies. To tackle this problem, we propose an architecture called segmented-memory recurrent neural network (SMRNN). A symbolic sequence is broken into segments and then presented as inputs to the SMRNN one symbol per cycle. The SMRNN uses separate internal states to store symbol-level context, as well as segment-level context. The symbol-level context is updated for each symbol presented for input. The segment-level context is updated after each segment. The SMRNN is trained using an extended real-time recurrent learning algorithm. We test the performance of SMRNN on the information latching problem, the "two-sequence problem" and the problem of protein secondary structure (PSS) prediction. Our implementation results indicate that SMRNN performs better on long-term dependency problems than conventional RNNs. Besides, we also theoretically analyze how the segmented memory of SMRNN helps learning long-term temporal dependencies and study the impact of the segment length.

Globally optimal tumor segmentation in PET-CT images: a graph-based co-segmentation method.

PubMed

Han, Dongfeng; Bayouth, John; Song, Qi; Taurani, Aakant; Sonka, Milan; Buatti, John; Wu, Xiaodong

2011-01-01

Tumor segmentation in PET and CT images is notoriously challenging due to the low spatial resolution in PET and low contrast in CT images. In this paper, we have proposed a general framework to use both PET and CT images simultaneously for tumor segmentation. Our method utilizes the strength of each imaging modality: the superior contrast of PET and the superior spatial resolution of CT. We formulate this problem as a Markov Random Field (MRF) based segmentation of the image pair with a regularized term that penalizes the segmentation difference between PET and CT. Our method simulates the clinical practice of delineating tumor simultaneously using both PET and CT, and is able to concurrently segment tumor from both modalities, achieving globally optimal solutions in low-order polynomial time by a single maximum flow computation. The method was evaluated on clinically relevant tumor segmentation problems. The results showed that our method can effectively make use of both PET and CT image information, yielding segmentation accuracy of 0.85 in Dice similarity coefficient and the average median hausdorff distance (HD) of 6.4 mm, which is 10% (resp., 16%) improvement compared to the graph cuts method solely using the PET (resp., CT) images.

Multi person detection and tracking based on hierarchical level-set method

NASA Astrophysics Data System (ADS)

Khraief, Chadia; Benzarti, Faouzi; Amiri, Hamid

2018-04-01

In this paper, we propose an efficient unsupervised method for mutli-person tracking based on hierarchical level-set approach. The proposed method uses both edge and region information in order to effectively detect objects. The persons are tracked on each frame of the sequence by minimizing an energy functional that combines color, texture and shape information. These features are enrolled in covariance matrix as region descriptor. The present method is fully automated without the need to manually specify the initial contour of Level-set. It is based on combined person detection and background subtraction methods. The edge-based is employed to maintain a stable evolution, guide the segmentation towards apparent boundaries and inhibit regions fusion. The computational cost of level-set is reduced by using narrow band technique. Many experimental results are performed on challenging video sequences and show the effectiveness of the proposed method.

Transcriptomic insights into citrus segment membrane's cell wall components relating to fruit sensory texture.

PubMed

Wang, Xun; Lin, Lijin; Tang, Yi; Xia, Hui; Zhang, Xiancong; Yue, Maolan; Qiu, Xia; Xu, Ke; Wang, Zhihui

2018-04-23

During fresh fruit consumption, sensory texture is one factor that affects the organoleptic qualities. Chemical components of plant cell walls, including pectin, cellulose, hemicellulose and lignin, play central roles in determining the textural qualities. To explore the genes and regulatory pathways involved in fresh citrus' perceived sensory texture, we performed mRNA-seq analyses of the segment membranes of two citrus cultivars, Shiranui and Kiyomi, with different organoleptic textures. Segment membranes were sampled at two developmental stages of citrus fruit, the beginning and end of the expansion period. More than 3000 differentially expressed genes were identified. The gene ontology analysis revealed that more categories were significantly enriched in 'Shiranui' than in 'Kiyomi' at both developmental stages. In total, 108 significantly enriched pathways were obtained, with most belonging to metabolism. A detailed transcriptomic analysis revealed potential critical genes involved in the metabolism of cell wall structures, for example, GAUT4 in pectin synthesis, CESA1, 3 and 6, and SUS4 in cellulose synthesis, CSLC5, XXT1 and XXT2 in hemicellulose synthesis, and CSE in lignin synthesis. Low levels, or no expression, of genes involved in cellulose and hemicellulose, such as CESA4, CESA7, CESA8, IRX9 and IRX14, confirmed that secondary cell walls were negligible or absent in citrus segment membranes. A chemical component analysis of the segment membranes from mature fruit revealed that the pectin, cellulose and lignin contents, and the segment membrane's weight (% of segment) were greater in 'Kiyomi'. Organoleptic quality of citrus is easily overlooked. It is mainly determined by sensory texture perceived in citrus segment membrane properties. We performed mRNA-seq analyses of citrus segment membranes to explore the genes and regulatory pathways involved in fresh citrus' perceived sensory texture. Transcriptomic data showed high repeatability between two independent biological replicates. The expression levels of genes involved in cell wall structure metabolism, including pectin, cellulose, hemicellulose and lignin, were investigated. Meanwhile, chemical component contents of the segment membranes from mature fruit were analyzed. This study provided detailed transcriptional regulatory profiles of different organoleptic citrus qualities and integrated insights into the mechanisms affecting citrus' sensory texture.

Segmenting patients and physicians using preferences from discrete choice experiments.

PubMed

Deal, Ken

2014-01-01

People often form groups or segments that have similar interests and needs and seek similar benefits from health providers. Health organizations need to understand whether the same health treatments, prevention programs, services, and products should be applied to everyone in the relevant population or whether different treatments need to be provided to each of several segments that are relatively homogeneous internally but heterogeneous among segments. Our objective was to explain the purposes, benefits, and methods of segmentation for health organizations, and to illustrate the process of segmenting health populations based on preference coefficients from a discrete choice conjoint experiment (DCE) using an example study of prevention of cyberbullying among university students. We followed a two-level procedure for investigating segmentation incorporating several methods for forming segments in Level 1 using DCE preference coefficients and testing their quality, reproducibility, and usability by health decision makers. Covariates (demographic, behavioral, lifestyle, and health state variables) were included in Level 2 to further evaluate quality and to support the scoring of large databases and developing typing tools for assigning those in the relevant population, but not in the sample, to the segments. Several segmentation solution candidates were found during the Level 1 analysis, and the relationship of the preference coefficients to the segments was investigated using predictive methods. Those segmentations were tested for their quality and reproducibility and three were found to be very close in quality. While one seemed better than others in the Level 1 analysis, another was very similar in quality and proved ultimately better in predicting segment membership using covariates in Level 2. The two segments in the final solution were profiled for attributes that would support the development and acceptance of cyberbullying prevention programs among university students. Those segments were very different-where one wanted substantial penalties against cyberbullies and were willing to devote time to a prevention program, while the other felt no need to be involved in prevention and wanted only minor penalties. Segmentation recognizes key differences in why patients and physicians prefer different health programs and treatments. A viable segmentation solution may lead to adapting prevention programs and treatments for each targeted segment and/or to educating and communicating to better inform those in each segment of the program/treatment benefits. Segment members' revealed preferences showing behavioral changes provide the ultimate basis for evaluating the segmentation benefits to the health organization.

Stress in Lumbar Intervertebral Discs during Distraction

PubMed Central

Gay, Ralph E.; Ilharreborde, Brice; Zhao, Kristin D.; Berglund, Lawrence J.; Bronfort, Gert; An, Kai-Nan

2008-01-01

BACKGROUND CONTEXT The intervertebral disc is a common source of low back pain. Prospective studies suggest that treatments that intermittently distract the disc might be beneficial for chronic low back pain. Although the potential exists for distraction therapies to affect the disc biomechanically their effect on intradiscal stress is debated. PURPOSE To determine if distraction alone, distraction combined with flexion or distraction combined with extension can reduce nucleus pulposus pressure and posterior anulus compressive stress in cadaveric lumbar discs compared to simulated standing or lying. STUDY DESIGN Laboratory study using single cadaveric motion segments. OUTCOME MEASURES Strain gauge measures of nucleus pulposus pressure and compressive stress in the anterior and posterior annulus fibrosus METHODS Intradiscal stress profilometry was performed on 15 motion segments during 5 simulated conditions: standing, lying, and 3 distracted conditions. Disc degeneration was graded by inspection from 1 (normal) to 4 (severe degeneration). RESULTS All distraction conditions markedly reduced nucleus pressure compared to either simulated standing or lying. There was no difference between distraction with flexion and distraction with extension in regard to posterior annulus compressive stress. Discs with little or no degeneration appeared to distributed compressive stress differently than those with moderate or severe degeneration. CONCLUSIONS Distraction appears to predictably reduce nucleus pulposus pressure. The effect of distraction therapy on the distribution of compressive stress may be dependent in part on the health of the disc. PMID:17981092

Topology and Dynamics of the Zebrafish Segmentation Clock Core Circuit

PubMed Central

Schröter, Christian; Isakova, Alina; Hens, Korneel; Soroldoni, Daniele; Gajewski, Martin; Jülicher, Frank; Maerkl, Sebastian J.; Deplancke, Bart; Oates, Andrew C.

2012-01-01

During vertebrate embryogenesis, the rhythmic and sequential segmentation of the body axis is regulated by an oscillating genetic network termed the segmentation clock. We describe a new dynamic model for the core pace-making circuit of the zebrafish segmentation clock based on a systematic biochemical investigation of the network's topology and precise measurements of somitogenesis dynamics in novel genetic mutants. We show that the core pace-making circuit consists of two distinct negative feedback loops, one with Her1 homodimers and the other with Her7:Hes6 heterodimers, operating in parallel. To explain the observed single and double mutant phenotypes of her1, her7, and hes6 mutant embryos in our dynamic model, we postulate that the availability and effective stability of the dimers with DNA binding activity is controlled in a “dimer cloud” that contains all possible dimeric combinations between the three factors. This feature of our model predicts that Hes6 protein levels should oscillate despite constant hes6 mRNA production, which we confirm experimentally using novel Hes6 antibodies. The control of the circuit's dynamics by a population of dimers with and without DNA binding activity is a new principle for the segmentation clock and may be relevant to other biological clocks and transcriptional regulatory networks. PMID:22911291

Two methods of Haustral fold detection from computed tomographic virtual colonoscopy images

NASA Astrophysics Data System (ADS)

Chowdhury, Ananda S.; Tan, Sovira; Yao, Jianhua; Linguraru, Marius G.; Summers, Ronald M.

2009-02-01

Virtual colonoscopy (VC) has gained popularity as a new colon diagnostic method over the last decade. VC is a new, less invasive alternative to the usually practiced optical colonoscopy for colorectal polyp and cancer screening, the second major cause of cancer related deaths in industrial nations. Haustral (colonic) folds serve as important landmarks for virtual endoscopic navigation in the existing computer-aided-diagnosis (CAD) system. In this paper, we propose and compare two different methods of haustral fold detection from volumetric computed tomographic virtual colonoscopy images. The colon lumen is segmented from the input using modified region growing and fuzzy connectedness. The first method for fold detection uses a level set that evolves on a mesh representation of the colon surface. The colon surface is obtained from the segmented colon lumen using the Marching Cubes algorithm. The second method for fold detection, based on a combination of heat diffusion and fuzzy c-means algorithm, is employed on the segmented colon volume. Folds obtained on the colon volume using this method are then transferred to the corresponding colon surface. After experimentation with different datasets, results are found to be promising. The results also demonstrate that the first method has a tendency of slight under-segmentation while the second method tends to slightly over-segment the folds.

Exploring multiple feature combination strategies with a recurrent neural network architecture for off-line handwriting recognition

NASA Astrophysics Data System (ADS)

Mioulet, L.; Bideault, G.; Chatelain, C.; Paquet, T.; Brunessaux, S.

2015-01-01

The BLSTM-CTC is a novel recurrent neural network architecture that has outperformed previous state of the art algorithms in tasks such as speech recognition or handwriting recognition. It has the ability to process long term dependencies in temporal signals in order to label unsegmented data. This paper describes different ways of combining features using a BLSTM-CTC architecture. Not only do we explore the low level combination (feature space combination) but we also explore high level combination (decoding combination) and mid-level (internal system representation combination). The results are compared on the RIMES word database. Our results show that the low level combination works best, thanks to the powerful data modeling of the LSTM neurons.

Consensus embedding: theory, algorithms and application to segmentation and classification of biomedical data

PubMed Central

2012-01-01

Background Dimensionality reduction (DR) enables the construction of a lower dimensional space (embedding) from a higher dimensional feature space while preserving object-class discriminability. However several popular DR approaches suffer from sensitivity to choice of parameters and/or presence of noise in the data. In this paper, we present a novel DR technique known as consensus embedding that aims to overcome these problems by generating and combining multiple low-dimensional embeddings, hence exploiting the variance among them in a manner similar to ensemble classifier schemes such as Bagging. We demonstrate theoretical properties of consensus embedding which show that it will result in a single stable embedding solution that preserves information more accurately as compared to any individual embedding (generated via DR schemes such as Principal Component Analysis, Graph Embedding, or Locally Linear Embedding). Intelligent sub-sampling (via mean-shift) and code parallelization are utilized to provide for an efficient implementation of the scheme. Results Applications of consensus embedding are shown in the context of classification and clustering as applied to: (1) image partitioning of white matter and gray matter on 10 different synthetic brain MRI images corrupted with 18 different combinations of noise and bias field inhomogeneity, (2) classification of 4 high-dimensional gene-expression datasets, (3) cancer detection (at a pixel-level) on 16 image slices obtained from 2 different high-resolution prostate MRI datasets. In over 200 different experiments concerning classification and segmentation of biomedical data, consensus embedding was found to consistently outperform both linear and non-linear DR methods within all applications considered. Conclusions We have presented a novel framework termed consensus embedding which leverages ensemble classification theory within dimensionality reduction, allowing for application to a wide range of high-dimensional biomedical data classification and segmentation problems. Our generalizable framework allows for improved representation and classification in the context of both imaging and non-imaging data. The algorithm offers a promising solution to problems that currently plague DR methods, and may allow for extension to other areas of biomedical data analysis. PMID:22316103

Effects of Water Sprinklers on the Performance of Low Level AFFF Aircraft Hangar Fire Suppression Systems

DTIC Science & Technology

2000-05-22

AFFF fire suppression system. The combined overhead water-only sprinkler and low level AFFF system is being considered as a new protection scheme for...performance of a low level system during AFFF discharge (4.0 Lpm/sq m (0.1 gpm/sq ft)). Based on the results of these tests, the design criteria for...Navy hangar protection may be revised to incorporate AFFF application from only the low level system, combined with overhead closed-head guide response water sprinklers.

Segmental transport of Ca²⁺ and Mg²⁺ along the gastrointestinal tract.

PubMed

Lameris, Anke L; Nevalainen, Pasi I; Reijnen, Daphne; Simons, Ellen; Eygensteyn, Jelle; Monnens, Leo; Bindels, René J M; Hoenderop, Joost G J

2015-02-01

Calcium (Ca(2+)) and magnesium (Mg(2+)) ions are involved in many vital physiological functions. Since dietary intake is the only source of minerals for the body, intestinal absorption is essential for normal homeostatic levels. The aim of this study was to characterize the absorption of Ca(2+) as well as Mg(2+) along the gastrointestinal tract at a molecular and functional level. In both humans and mice the Ca(2+) channel transient receptor potential vanilloid subtype 6 (TRPV6) is expressed in the proximal intestinal segments, whereas Mg(2+) channel transient receptor potential melastatin subtype 6 (TRPM6) is expressed in the distal parts of the intestine. A method was established to measure the rate of Mg(2+) absorption from the intestine in a time-dependent manner by use of (25)Mg(2+). In addition, local absorption of Ca(2+) and Mg(2+) in different segments of the intestine of mice was determined by using surgically implanted intestinal cannulas. By these methods, it was demonstrated that intestinal absorption of Mg(2+) is regulated by dietary needs in a vitamin D-independent manner. Also, it was shown that at low luminal concentrations, favoring transcellular absorption, Ca(2+) transport mainly takes place in the proximal segments of the intestine, whereas Mg(2+) absorption predominantly occurs in the distal part of the gastrointestinal tract. Vitamin D treatment of mice increased serum Mg(2+) levels and 24-h urinary Mg(2+) excretion, but not intestinal absorption of (25)Mg(2+). Segmental cannulation of the intestine and time-dependent absorption studies using (25)Mg(2+) provide new ways to study intestinal Mg(2+) absorption. Copyright © 2015 the American Physiological Society.

An Efficient Pipeline for Abdomen Segmentation in CT Images.

PubMed

Koyuncu, Hasan; Ceylan, Rahime; Sivri, Mesut; Erdogan, Hasan

2018-04-01

Computed tomography (CT) scans usually include some disadvantages due to the nature of the imaging procedure, and these handicaps prevent accurate abdomen segmentation. Discontinuous abdomen edges, bed section of CT, patient information, closeness between the edges of the abdomen and CT, poor contrast, and a narrow histogram can be regarded as the most important handicaps that occur in abdominal CT scans. Currently, one or more handicaps can arise and prevent technicians obtaining abdomen images through simple segmentation techniques. In other words, CT scans can include the bed section of CT, a patient's diagnostic information, low-quality abdomen edges, low-level contrast, and narrow histogram, all in one scan. These phenomena constitute a challenge, and an efficient pipeline that is unaffected by handicaps is required. In addition, analysis such as segmentation, feature selection, and classification has meaning for a real-time diagnosis system in cases where the abdomen section is directly used with a specific size. A statistical pipeline is designed in this study that is unaffected by the handicaps mentioned above. Intensity-based approaches, morphological processes, and histogram-based procedures are utilized to design an efficient structure. Performance evaluation is realized in experiments on 58 CT images (16 training, 16 test, and 26 validation) that include the abdomen and one or more disadvantage(s). The first part of the data (16 training images) is used to detect the pipeline's optimum parameters, while the second and third parts are utilized to evaluate and to confirm the segmentation performance. The segmentation results are presented as the means of six performance metrics. Thus, the proposed method achieves remarkable average rates for training/test/validation of 98.95/99.36/99.57% (jaccard), 99.47/99.67/99.79% (dice), 100/99.91/99.91% (sensitivity), 98.47/99.23/99.85% (specificity), 99.38/99.63/99.87% (classification accuracy), and 98.98/99.45/99.66% (precision). In summary, a statistical pipeline performing the task of abdomen segmentation is achieved that is not affected by the disadvantages, and the most detailed abdomen segmentation study is performed for the use before organ and tumor segmentation, feature extraction, and classification.

Exploration of immunoglobulin transcriptomes from mice immunized with three-finger toxins and phospholipases A2 from the Central American coral snake, Micrurus nigrocinctus.

PubMed

Laustsen, Andreas H; Engmark, Mikael; Clouser, Christopher; Timberlake, Sonia; Vigneault, Francois; Gutiérrez, José María; Lomonte, Bruno

2017-01-01

Snakebite envenomings represent a neglected public health issue in many parts of the rural tropical world. Animal-derived antivenoms have existed for more than a hundred years and are effective in neutralizing snake venom toxins when timely administered. However, the low immunogenicity of many small but potent snake venom toxins represents a challenge for obtaining a balanced immune response against the medically relevant components of the venom. Here, we employ high-throughput sequencing of the immunoglobulin (Ig) transcriptome of mice immunized with a three-finger toxin and a phospholipase A 2 from the venom of the Central American coral snake, Micrurus nigrocinctus. Although exploratory in nature, our indicate results showed that only low frequencies of mRNA encoding IgG isotypes, the most relevant isotype for therapeutic purposes, were present in splenocytes of five mice immunized with 6 doses of the two types of toxins over 90 days. Furthermore, analysis of Ig heavy chain transcripts showed that no particular combination of variable (V) and joining (J) gene segments had been selected in the immunization process, as would be expected after a strong humoral immune response to a single antigen. Combined with the titration of toxin-specific antibodies in the sera of immunized mice, these data support the low immunogenicity of three-finger toxins and phospholipases A 2 found in M. nigrocinctus venoms, and highlight the need for future studies analyzing the complexity of antibody responses to toxins at the molecular level.

Balancing the Role of Priors in Multi-Observer Segmentation Evaluation

PubMed Central

Huang, Xiaolei; Wang, Wei; Lopresti, Daniel; Long, Rodney; Antani, Sameer; Xue, Zhiyun; Thoma, George

2009-01-01

Comparison of a group of multiple observer segmentations is known to be a challenging problem. A good segmentation evaluation method would allow different segmentations not only to be compared, but to be combined to generate a “true” segmentation with higher consensus. Numerous multi-observer segmentation evaluation approaches have been proposed in the literature, and STAPLE in particular probabilistically estimates the true segmentation by optimal combination of observed segmentations and a prior model of the truth. An Expectation–Maximization (EM) algorithm, STAPLE’S convergence to the desired local minima depends on good initializations for the truth prior and the observer-performance prior. However, accurate modeling of the initial truth prior is nontrivial. Moreover, among the two priors, the truth prior always dominates so that in certain scenarios when meaningful observer-performance priors are available, STAPLE can not take advantage of that information. In this paper, we propose a Bayesian decision formulation of the problem that permits the two types of prior knowledge to be integrated in a complementary manner in four cases with differing application purposes: (1) with known truth prior; (2) with observer prior; (3) with neither truth prior nor observer prior; and (4) with both truth prior and observer prior. The third and fourth cases are not discussed (or effectively ignored) by STAPLE, and in our research we propose a new method to combine multiple-observer segmentations based on the maximum a posterior (MAP) principle, which respects the observer prior regardless of the availability of the truth prior. Based on the four scenarios, we have developed a web-based software application that implements the flexible segmentation evaluation framework for digitized uterine cervix images. Experiment results show that our framework has flexibility in effectively integrating different priors for multi-observer segmentation evaluation and it also generates results comparing favorably to those by the STAPLE algorithm and the Majority Vote Rule. PMID:20523759

Smoke regions extraction based on two steps segmentation and motion detection in early fire

NASA Astrophysics Data System (ADS)

Jian, Wenlin; Wu, Kaizhi; Yu, Zirong; Chen, Lijuan

2018-03-01

Aiming at the early problems of video-based smoke detection in fire video, this paper proposes a method to extract smoke suspected regions by combining two steps segmentation and motion characteristics. Early smoldering smoke can be seen as gray or gray-white regions. In the first stage, regions of interests (ROIs) with smoke are obtained by using two step segmentation methods. Then, suspected smoke regions are detected by combining the two step segmentation and motion detection. Finally, morphological processing is used for smoke regions extracting. The Otsu algorithm is used as segmentation method and the ViBe algorithm is used to detect the motion of smoke. The proposed method was tested on 6 test videos with smoke. The experimental results show the effectiveness of our proposed method over visual observation.

Multi-level deep supervised networks for retinal vessel segmentation.

PubMed

Mo, Juan; Zhang, Lei

2017-12-01

Changes in the appearance of retinal blood vessels are an important indicator for various ophthalmologic and cardiovascular diseases, including diabetes, hypertension, arteriosclerosis, and choroidal neovascularization. Vessel segmentation from retinal images is very challenging because of low blood vessel contrast, intricate vessel topology, and the presence of pathologies such as microaneurysms and hemorrhages. To overcome these challenges, we propose a neural network-based method for vessel segmentation. A deep supervised fully convolutional network is developed by leveraging multi-level hierarchical features of the deep networks. To improve the discriminative capability of features in lower layers of the deep network and guide the gradient back propagation to overcome gradient vanishing, deep supervision with auxiliary classifiers is incorporated in some intermediate layers of the network. Moreover, the transferred knowledge learned from other domains is used to alleviate the issue of insufficient medical training data. The proposed approach does not rely on hand-crafted features and needs no problem-specific preprocessing or postprocessing, which reduces the impact of subjective factors. We evaluate the proposed method on three publicly available databases, the DRIVE, STARE, and CHASE_DB1 databases. Extensive experiments demonstrate that our approach achieves better or comparable performance to state-of-the-art methods with a much faster processing speed, making it suitable for real-world clinical applications. The results of cross-training experiments demonstrate its robustness with respect to the training set. The proposed approach segments retinal vessels accurately with a much faster processing speed and can be easily applied to other biomedical segmentation tasks.

Improvements in Topical Ocular Drug Delivery Systems: Hydrogels and Contact Lenses.

PubMed

Ribeiro, Andreza Maria; Figueiras, Ana; Veiga, Francisco

2015-01-01

Conventional ophthalmic systems present very low corneal systemic bioavailability due to the nasolacrimal drainage and the difficulty to deliver the drug in the posterior segment of ocular tissue. For these reasons, recent advances have focused on the development of new ophthalmic drug delivery systems. This review provides an insight into the various constraints associated with ocular drug delivery, summarizes recent findings in soft contact lenses (SCL) and the applications of novel pharmaceutical systems for ocular drug delivery. Among the new therapeutic approaches in ophthalmology, SCL are novel continuous-delivery systems, providing high and sustained levels of drugs to the cornea. The tendency of research in ophthalmic drug delivery systems development are directed towards a combination of several technologies (bio-inspired and molecular imprinting techniques) and materials (cyclodextrins, surfactants, specific monomers). There is a tendency to develop systems which not only prolong the contact time of the vehicle at the ocular surface, but also at the same time slow down the clearance of the drug. Different materials can be applied during the development of contact lenses and can be combined with natural inspired strategies of drug immobilization and release, providing successful tools for ocular drug delivery systems.

LINKS: learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images.

PubMed

Wang, Li; Gao, Yaozong; Shi, Feng; Li, Gang; Gilmore, John H; Lin, Weili; Shen, Dinggang

2015-03-01

Segmentation of infant brain MR images is challenging due to insufficient image quality, severe partial volume effect, and ongoing maturation and myelination processes. In the first year of life, the image contrast between white and gray matters of the infant brain undergoes dramatic changes. In particular, the image contrast is inverted around 6-8months of age, and the white and gray matter tissues are isointense in both T1- and T2-weighted MR images and thus exhibit the extremely low tissue contrast, which poses significant challenges for automated segmentation. Most previous studies used multi-atlas label fusion strategy, which has the limitation of equally treating the different available image modalities and is often computationally expensive. To cope with these limitations, in this paper, we propose a novel learning-based multi-source integration framework for segmentation of infant brain images. Specifically, we employ the random forest technique to effectively integrate features from multi-source images together for tissue segmentation. Here, the multi-source images include initially only the multi-modality (T1, T2 and FA) images and later also the iteratively estimated and refined tissue probability maps of gray matter, white matter, and cerebrospinal fluid. Experimental results on 119 infants show that the proposed method achieves better performance than other state-of-the-art automated segmentation methods. Further validation was performed on the MICCAI grand challenge and the proposed method was ranked top among all competing methods. Moreover, to alleviate the possible anatomical errors, our method can also be combined with an anatomically-constrained multi-atlas labeling approach for further improving the segmentation accuracy. Copyright © 2014 Elsevier Inc. All rights reserved.

LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images

PubMed Central

Wang, Li; Gao, Yaozong; Shi, Feng; Li, Gang; Gilmore, John H.; Lin, Weili; Shen, Dinggang

2014-01-01

Segmentation of infant brain MR images is challenging due to insufficient image quality, severe partial volume effect, and ongoing maturation and myelination processes. In the first year of life, the image contrast between white and gray matters of the infant brain undergoes dramatic changes. In particular, the image contrast is inverted around 6-8 months of age, and the white and gray matter tissues are isointense in both T1- and T2-weighted MR images and thus exhibit the extremely low tissue contrast, which poses significant challenges for automated segmentation. Most previous studies used multi-atlas label fusion strategy, which has the limitation of equally treating the different available image modalities and is often computationally expensive. To cope with these limitations, in this paper, we propose a novel learning-based multi-source integration framework for segmentation of infant brain images. Specifically, we employ the random forest technique to effectively integrate features from multi-source images together for tissue segmentation. Here, the multi-source images include initially only the multi-modality (T1, T2 and FA) images and later also the iteratively estimated and refined tissue probability maps of gray matter, white matter, and cerebrospinal fluid. Experimental results on 119 infants show that the proposed method achieves better performance than other state-of-the-art automated segmentation methods. Further validation was performed on the MICCAI grand challenge and the proposed method was ranked top among all competing methods. Moreover, to alleviate the possible anatomical errors, our method can also be combined with an anatomically-constrained multi-atlas labeling approach for further improving the segmentation accuracy. PMID:25541188

Retinal degeneration in cats fed casein. IV. The early receptor potential.

PubMed

Berson, E L; Watson, G; Grasse, K L; Szamier, R B

1981-08-01

Electroretinographic studies of casein-fed cats with retinal taurine deficiency revealed that the early receptor potential (ERP) was initially normal in amplitude at a time when the a-wave and b-wave of the electroretinogram were substantially reduced or even nondetectable. The preserved ERP's in these taurine-deficient cats could be correlated with the histologic finding that their outer segments were relatively intact over 90% of the retinal area subtended by the test flash. The sequence of electroretinographic changes in these taurine-deficient cats was also consistent with previous biochemical studies on the normal cat retina that have shown a relatively low concentration of taurine at the level of the outer segments and a higher concentration at the level of the inner segments. The responses in early stages from taurine-deficient cats differed from the responses obtained from vitamin A--deficient cats but resembled those from cats that received an intravitreal injection of ouabain. Similarities and a difference between the responses of taurine-deficient cats and those of patients with early retinitis pigmentosa are considered.

Efficient segmentation of 3D fluoroscopic datasets from mobile C-arm

NASA Astrophysics Data System (ADS)

Styner, Martin A.; Talib, Haydar; Singh, Digvijay; Nolte, Lutz-Peter

2004-05-01

The emerging mobile fluoroscopic 3D technology linked with a navigation system combines the advantages of CT-based and C-arm-based navigation. The intra-operative, automatic segmentation of 3D fluoroscopy datasets enables the combined visualization of surgical instruments and anatomical structures for enhanced planning, surgical eye-navigation and landmark digitization. We performed a thorough evaluation of several segmentation algorithms using a large set of data from different anatomical regions and man-made phantom objects. The analyzed segmentation methods include automatic thresholding, morphological operations, an adapted region growing method and an implicit 3D geodesic snake method. In regard to computational efficiency, all methods performed within acceptable limits on a standard Desktop PC (30sec-5min). In general, the best results were obtained with datasets from long bones, followed by extremities. The segmentations of spine, pelvis and shoulder datasets were generally of poorer quality. As expected, the threshold-based methods produced the worst results. The combined thresholding and morphological operations methods were considered appropriate for a smaller set of clean images. The region growing method performed generally much better in regard to computational efficiency and segmentation correctness, especially for datasets of joints, and lumbar and cervical spine regions. The less efficient implicit snake method was able to additionally remove wrongly segmented skin tissue regions. This study presents a step towards efficient intra-operative segmentation of 3D fluoroscopy datasets, but there is room for improvement. Next, we plan to study model-based approaches for datasets from the knee and hip joint region, which would be thenceforth applied to all anatomical regions in our continuing development of an ideal segmentation procedure for 3D fluoroscopic images.

Parameter-space metric of semicoherent searches for continuous gravitational waves

NASA Astrophysics Data System (ADS)

Pletsch, Holger J.

2010-08-01

Continuous gravitational-wave (CW) signals such as emitted by spinning neutron stars are an important target class for current detectors. However, the enormous computational demand prohibits fully coherent broadband all-sky searches for prior unknown CW sources over wide ranges of parameter space and for yearlong observation times. More efficient hierarchical “semicoherent” search strategies divide the data into segments much shorter than one year, which are analyzed coherently; then detection statistics from different segments are combined incoherently. To optimally perform the incoherent combination, understanding of the underlying parameter-space structure is requisite. This problem is addressed here by using new coordinates on the parameter space, which yield the first analytical parameter-space metric for the incoherent combination step. This semicoherent metric applies to broadband all-sky surveys (also embedding directed searches at fixed sky position) for isolated CW sources. Furthermore, the additional metric resolution attained through the combination of segments is studied. From the search parameters (sky position, frequency, and frequency derivatives), solely the metric resolution in the frequency derivatives is found to significantly increase with the number of segments.

Optimized micromirror arrays for adaptive optics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Michalicek, M. Adrian

This paper describes the design, layout, fabrication, and surface characterization of highly optimized surface micromachined micromirror devices. Design considerations and fabrication capabilities are presented. These devices are fabricated in the state-of-the-art, four-level, planarized, ultra-low-stress polysilicon process available at Sandia National Laboratories known as the Sandia Ultra-planar Multi-level MEMS Technology (SUMMiT). This enabling process permits the development of micromirror devices with near-ideal characteristics that have previously been unrealizable in standard three-layer polysilicon processes. The reduced 1 {mu}m minimum feature sizes and 0.1 {mu}m mask resolution make it possible to produce dense wiring patterns and irregularly shaped flexures. Likewise, mirror surfaces canmore » be uniquely distributed and segmented in advanced patterns and often irregular shapes in order to minimize wavefront error across the pupil. The ultra-low-stress polysilicon and planarized upper layer allow designers to make larger and more complex micromirrors of varying shape and surface area within an array while maintaining uniform performance of optical surfaces. Powerful layout functions of the AutoCAD editor simplify the design of advanced micromirror arrays and make it possible to optimize devices according to the capabilities of the fabrication process. Micromirrors fabricated in this process have demonstrated a surface variance across the array from only 2{endash}3 nm to a worst case of roughly 25 nm while boasting active surface areas of 98{percent} or better. Combining the process planarization with a {open_quotes}planarized-by-design{close_quotes} approach will produce micromirror array surfaces that are limited in flatness only by the surface deposition roughness of the structural material. Ultimately, the combination of advanced process and layout capabilities have permitted the fabrication of highly optimized micromirror arrays for adaptive optics. {copyright} {ital 1999 American Institute of Physics.}« less

A Novel Segmentation Approach Combining Region- and Edge-Based Information for Ultrasound Images

PubMed Central

Luo, Yaozhong; Liu, Longzhong; Li, Xuelong

2017-01-01

Ultrasound imaging has become one of the most popular medical imaging modalities with numerous diagnostic applications. However, ultrasound (US) image segmentation, which is the essential process for further analysis, is a challenging task due to the poor image quality. In this paper, we propose a new segmentation scheme to combine both region- and edge-based information into the robust graph-based (RGB) segmentation method. The only interaction required is to select two diagonal points to determine a region of interest (ROI) on the original image. The ROI image is smoothed by a bilateral filter and then contrast-enhanced by histogram equalization. Then, the enhanced image is filtered by pyramid mean shift to improve homogeneity. With the optimization of particle swarm optimization (PSO) algorithm, the RGB segmentation method is performed to segment the filtered image. The segmentation results of our method have been compared with the corresponding results obtained by three existing approaches, and four metrics have been used to measure the segmentation performance. The experimental results show that the method achieves the best overall performance and gets the lowest ARE (10.77%), the second highest TPVF (85.34%), and the second lowest FPVF (4.48%). PMID:28536703

Maximum-likelihood techniques for joint segmentation-classification of multispectral chromosome images.

PubMed

Schwartzkopf, Wade C; Bovik, Alan C; Evans, Brian L

2005-12-01

Traditional chromosome imaging has been limited to grayscale images, but recently a 5-fluorophore combinatorial labeling technique (M-FISH) was developed wherein each class of chromosomes binds with a different combination of fluorophores. This results in a multispectral image, where each class of chromosomes has distinct spectral components. In this paper, we develop new methods for automatic chromosome identification by exploiting the multispectral information in M-FISH chromosome images and by jointly performing chromosome segmentation and classification. We (1) develop a maximum-likelihood hypothesis test that uses multispectral information, together with conventional criteria, to select the best segmentation possibility; (2) use this likelihood function to combine chromosome segmentation and classification into a robust chromosome identification system; and (3) show that the proposed likelihood function can also be used as a reliable indicator of errors in segmentation, errors in classification, and chromosome anomalies, which can be indicators of radiation damage, cancer, and a wide variety of inherited diseases. We show that the proposed multispectral joint segmentation-classification method outperforms past grayscale segmentation methods when decomposing touching chromosomes. We also show that it outperforms past M-FISH classification techniques that do not use segmentation information.

Method 365.5 Determination of Orthophosphate in Estuarine and Coastal Waters by Automated Colorimetric Analysis

EPA Science Inventory

This method provides a procedure for the determination of low-level orthophosphate concentrations normally found in estuarine and/or coastal waters. It is based upon the method of Murphy and Riley1 adapted for automated segmented flow analysis2 in which the two reagent solutions ...

49 CFR 236.1005 - Requirements for Positive Train Control systems.

Code of Federal Regulations, 2012 CFR

2012-10-01

... be determined and reported as follows: (i) The traffic density threshold of 5 million gross tons... in rail traffic such as reductions in total traffic volume to a level below 5 million gross tons... requirement to install PTC on a low density track segment where a PTC system is otherwise required by this...

49 CFR 236.1005 - Requirements for Positive Train Control systems.

Code of Federal Regulations, 2014 CFR

2014-10-01

... be determined and reported as follows: (i) The traffic density threshold of 5 million gross tons... in rail traffic such as reductions in total traffic volume to a level below 5 million gross tons... requirement to install PTC on a low density track segment where a PTC system is otherwise required by this...

49 CFR 236.1005 - Requirements for Positive Train Control systems.

Code of Federal Regulations, 2013 CFR

2013-10-01

... be determined and reported as follows: (i) The traffic density threshold of 5 million gross tons... in rail traffic such as reductions in total traffic volume to a level below 5 million gross tons... requirement to install PTC on a low density track segment where a PTC system is otherwise required by this...

Dehydrin expression as a potential diagnostic tool for cold stress in white clover.

PubMed

Vaseva, Irina Ivanova; Anders, Iwona; Yuperlieva-Mateeva, Bistra; Nenkova, Rosa; Kostadinova, Anelia; Feller, Urs

2014-05-01

Cold acclimation is important for crop survival in environments undergoing seasonal low temperatures. It involves the induction of defensive mechanisms including the accumulation of different cryoprotective molecules among which are dehydrins (DHN). Recently several sequences coding for dehydrins were identified in white clover (Trifolium repens). This work aimed to select the most responsive to cold stress DHN analogues in search for cold stress diagnostic markers. The assessment of dehydrin transcript accumulation via RT-PCR and immunodetection performed with three antibodies against the conserved K-, Y-, and S-segment allowed to outline different dehydrin types presented in the tested samples. Both analyses confirmed that YnKn dehydrins were underrepresented in the controls but exposure to low temperature specifically induced their accumulation. Strong immunosignals corresponding to 37-40 kDa with antibodies against Y- and K-segment were revealed in cold-stressed leaves. Another 'cold-specific' band at position 52-55 kDa was documented on membranes probed with antibodies against K-segment. Real time RT-qPCR confirmed that low temperatures induced the accumulation of SKn and YnSKn transcripts in leaves and reduced their expression in roots. Results suggest that a YnKn dehydrin transcript with GenBank ID: KC247805 and the immunosignal at 37-40 kDa, obtained with antibodies against Y- and K-segment are reliable markers for cold stress in white clover. The assessment of SKn (GenBank ID: EU846208) and YnSKn (GenBank ID: KC247804) transcript levels in leaves could serve as additional diagnostic tools. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

Jansen-MIDAS: A multi-level photomicrograph segmentation software based on isotropic undecimated wavelets.

PubMed

de Siqueira, Alexandre Fioravante; Cabrera, Flávio Camargo; Nakasuga, Wagner Massayuki; Pagamisse, Aylton; Job, Aldo Eloizo

2018-01-01

Image segmentation, the process of separating the elements within a picture, is frequently used for obtaining information from photomicrographs. Segmentation methods should be used with reservations, since incorrect results can mislead when interpreting regions of interest (ROI). This decreases the success rate of extra procedures. Multi-Level Starlet Segmentation (MLSS) and Multi-Level Starlet Optimal Segmentation (MLSOS) were developed to be an alternative for general segmentation tools. These methods gave rise to Jansen-MIDAS, an open-source software. A scientist can use it to obtain several segmentations of hers/his photomicrographs. It is a reliable alternative to process different types of photomicrographs: previous versions of Jansen-MIDAS were used to segment ROI in photomicrographs of two different materials, with an accuracy superior to 89%. © 2017 Wiley Periodicals, Inc.

Ensemble Semi-supervised Frame-work for Brain Magnetic Resonance Imaging Tissue Segmentation

PubMed Central

Azmi, Reza; Pishgoo, Boshra; Norozi, Narges; Yeganeh, Samira

2013-01-01

Brain magnetic resonance images (MRIs) tissue segmentation is one of the most important parts of the clinical diagnostic tools. Pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. Supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow to obtain. Moreover, they cannot use unlabeled data to train classifiers. On the other hand, unsupervised segmentation methods have no prior knowledge and lead to low level of performance. However, semi-supervised learning which uses a few labeled data together with a large amount of unlabeled data causes higher accuracy with less trouble. In this paper, we propose an ensemble semi-supervised frame-work for segmenting of brain magnetic resonance imaging (MRI) tissues that it has been used results of several semi-supervised classifiers simultaneously. Selecting appropriate classifiers has a significant role in the performance of this frame-work. Hence, in this paper, we present two semi-supervised algorithms expectation filtering maximization and MCo_Training that are improved versions of semi-supervised methods expectation maximization and Co_Training and increase segmentation accuracy. Afterward, we use these improved classifiers together with graph-based semi-supervised classifier as components of the ensemble frame-work. Experimental results show that performance of segmentation in this approach is higher than both supervised methods and the individual semi-supervised classifiers. PMID:24098863

The role of facial canal diameter in the pathogenesis and grade of Bell's palsy: a study by high resolution computed tomography.

PubMed

Celik, Onur; Eskiizmir, Gorkem; Pabuscu, Yuksel; Ulkumen, Burak; Toker, Gokce Tanyeri

The exact etiology of Bell's palsy still remains obscure. The only authenticated finding is inflammation and edema of the facial nerve leading to entrapment inside the facial canal. To identify if there is any relationship between the grade of Bell's palsy and diameter of the facial canal, and also to study any possible anatomic predisposition of facial canal for Bell's palsy including parts which have not been studied before. Medical records and temporal computed tomography scans of 34 patients with Bell's palsy were utilized in this retrospective clinical study. Diameters of both facial canals (affected and unaffected) of each patient were measured at labyrinthine segment, geniculate ganglion, tympanic segment, second genu, mastoid segment and stylomastoid foramen. The House-Brackmann (HB) scale of each patient at presentation and 3 months after the treatment was evaluated from their medical records. The paired samples t-test and Wilcoxon signed-rank test were used for comparison of width between the affected side and unaffected side. The Wilcoxon signed-rank test was also used for evaluation of relationship between the diameter of facial canal and the grade of the Bell's palsy. Significant differences were established at a level of p=0.05 (IBM SPSS Statistics for Windows, Version 21.0.; Armonk, NY, IBM Corp). Thirty-four patients - 16 females, 18 males; mean age±Standard Deviation, 40.3±21.3 - with Bell's palsy were included in the study. According to the HB facial nerve grading system; 8 patients were grade V, 6 were grade IV, 11 were grade III, 8 were grade II and 1 patient was grade I. The mean width at the labyrinthine segment of the facial canal in the affected temporal bone was significantly smaller than the equivalent in the unaffected temporal bone (p=0.00). There was no significant difference between the affected and unaffected temporal bones at the geniculate ganglion (p=0.87), tympanic segment (p=0.66), second genu (p=0.62), mastoid segment (p=0.67) and stylomastoid foramen (p=0.16). We did not find any relationship between the HB grade and the facial canal diameter at the level of labyrinthine segment (p=0.41), tympanic segment (p=0.12), mastoid segment (p=0.14), geniculate ganglion (p=0.13) and stylomastoid foramen (p=0.44), while we found significant relationship at the level of second genu (p=0.02). We found the diameter of labyrinthine segment of facial canal as an anatomic risk factor for Bell's palsy. We also found significant relationship between the HB grade and FC diameter at the level of second genu. Future studies (MRI-CT combined or 3D modeling) are needed to promote this possible relevance especially at second genu. Thus, in the future it may be possible to selectively decompress particular segments in high grade BP patients. Copyright © 2016 Associação Brasileira de Otorrinolaringologia e Cirurgia Cérvico-Facial. Published by Elsevier Editora Ltda. All rights reserved.

Resonant Raman scattering of controlled molecular weight polyacetylene

NASA Astrophysics Data System (ADS)

Schen, M. A.; Chien, J. C. W.; Perrin, E.; Lefrant, S.; Mulazzi, E.

1988-12-01

Polyacetylene, (CH)x, films of 500, 5300, 10 500, and 100 000 Daltons number average molecular weights (Mn ) were synthesized using the titanium tetra-n-butoxide/triethyl aluminum-catalyst/cocatalyst system and examined using resonant Raman scattering techniques. Before isomerization, trans segments are found to exist mainly as short, isolated sequences independent of Mn. After thermal isomerization, theoretical analysis of the RRS spectra using the Brivio, Mulazzi model indicate the ratio of long trans conjugated segments (N≥30) to short trans conjugated segments (N≤30) is significantly larger for 100 000 Dalton polymer in comparison to polymer of 10 500 Mn and below. For samples below 10 500 Daltons, no clear relationship between actual polymer molecular weight and G is observed. Optimization of the isomerization conditions for 100 000 Dalton polymer results in trans-(CH)x with a G=0.80. These results suggest that not until very long molecular chains are obtained can samples composed principally of long conjugated segments be obtained. It is proposed that defects which arise during and after the polymerization limit the content of long segments. Ambient, short term oxidation of 100 000 Mn polymer shows a decrease in G from 0.80 to 0.70. Low level chain oxidation or doping is shown to preferentially occur within long conjugated segments.

Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters

PubMed Central

Zhu, Hongchun; Cai, Lijie; Liu, Haiying; Huang, Wei

2016-01-01

Multi-scale image segmentation and the selection of optimal segmentation parameters are the key processes in the object-oriented information extraction of high-resolution remote sensing images. The accuracy of remote sensing special subject information depends on this extraction. On the basis of WorldView-2 high-resolution data, the optimal segmentation parameters methodof object-oriented image segmentation and high-resolution image information extraction, the following processes were conducted in this study. Firstly, the best combination of the bands and weights was determined for the information extraction of high-resolution remote sensing image. An improved weighted mean-variance method was proposed andused to calculatethe optimal segmentation scale. Thereafter, the best shape factor parameter and compact factor parameters were computed with the use of the control variables and the combination of the heterogeneity and homogeneity indexes. Different types of image segmentation parameters were obtained according to the surface features. The high-resolution remote sensing images were multi-scale segmented with the optimal segmentation parameters. Ahierarchical network structure was established by setting the information extraction rules to achieve object-oriented information extraction. This study presents an effective and practical method that can explain expert input judgment by reproducible quantitative measurements. Furthermore the results of this procedure may be incorporated into a classification scheme. PMID:27362762

A Multi-Objective Decision Making Approach for Solving the Image Segmentation Fusion Problem.

PubMed

Khelifi, Lazhar; Mignotte, Max

2017-08-01

Image segmentation fusion is defined as the set of methods which aim at merging several image segmentations, in a manner that takes full advantage of the complementarity of each one. Previous relevant researches in this field have been impeded by the difficulty in identifying an appropriate single segmentation fusion criterion, providing the best possible, i.e., the more informative, result of fusion. In this paper, we propose a new model of image segmentation fusion based on multi-objective optimization which can mitigate this problem, to obtain a final improved result of segmentation. Our fusion framework incorporates the dominance concept in order to efficiently combine and optimize two complementary segmentation criteria, namely, the global consistency error and the F-measure (precision-recall) criterion. To this end, we present a hierarchical and efficient way to optimize the multi-objective consensus energy function related to this fusion model, which exploits a simple and deterministic iterative relaxation strategy combining the different image segments. This step is followed by a decision making task based on the so-called "technique for order performance by similarity to ideal solution". Results obtained on two publicly available databases with manual ground truth segmentations clearly show that our multi-objective energy-based model gives better results than the classical mono-objective one.

Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters.

PubMed

Zhu, Hongchun; Cai, Lijie; Liu, Haiying; Huang, Wei

2016-01-01

Multi-scale image segmentation and the selection of optimal segmentation parameters are the key processes in the object-oriented information extraction of high-resolution remote sensing images. The accuracy of remote sensing special subject information depends on this extraction. On the basis of WorldView-2 high-resolution data, the optimal segmentation parameters methodof object-oriented image segmentation and high-resolution image information extraction, the following processes were conducted in this study. Firstly, the best combination of the bands and weights was determined for the information extraction of high-resolution remote sensing image. An improved weighted mean-variance method was proposed andused to calculatethe optimal segmentation scale. Thereafter, the best shape factor parameter and compact factor parameters were computed with the use of the control variables and the combination of the heterogeneity and homogeneity indexes. Different types of image segmentation parameters were obtained according to the surface features. The high-resolution remote sensing images were multi-scale segmented with the optimal segmentation parameters. Ahierarchical network structure was established by setting the information extraction rules to achieve object-oriented information extraction. This study presents an effective and practical method that can explain expert input judgment by reproducible quantitative measurements. Furthermore the results of this procedure may be incorporated into a classification scheme.

Effects of the addition of functional electrical stimulation to ground level gait training with body weight support after chronic stroke.

PubMed

Prado-Medeiros, Christiane L; Sousa, Catarina O; Souza, Andréa S; Soares, Márcio R; Barela, Ana M F; Salvini, Tania F

2011-01-01

The addition of functional electrical stimulation (FES) to treadmill gait training with partial body weight support (BWS) has been proposed as a strategy to facilitate gait training in people with hemiparesis. However, there is a lack of studies that evaluate the effectiveness of FES addition on ground level gait training with BWS, which is the most common locomotion surface. To investigate the additional effects of commum peroneal nerve FES combined with gait training and BWS on ground level, on spatial-temporal gait parameters, segmental angles, and motor function. Twelve people with chronic hemiparesis participated in the study. An A1-B-A2 design was applied. A1 and A2 corresponded to ground level gait training using BWS, and B corresponded to the same training with the addition of FES. The assessments were performed using the Modified Ashworth Scale (MAS), Functional Ambulation Category (FAC), Rivermead Motor Assessment (RMA), and filming. The kinematics analyzed variables were mean walking speed of locomotion; step length; stride length, speed and duration; initial and final double support duration; single-limb support duration; swing period; range of motion (ROM), maximum and minimum angles of foot, leg, thigh, and trunk segments. There were not changes between phases for the functional assessment of RMA, for the spatial-temporal gait variables and segmental angles, no changes were observed after the addition of FES. The use of FES on ground level gait training with BWS did not provide additional benefits for all assessed parameters.

Using maximum entropy to predict suitable habitat for the endangered dwarf wedgemussel in the Maryland Coastal Plain

USGS Publications Warehouse

Campbell, Cara; Hilderbrand, Robert H.

2017-01-01

Species distribution modelling can be useful for the conservation of rare and endangered species. Freshwater mussel declines have thinned species ranges producing spatially fragmented distributions across large areas. Spatial fragmentation in combination with a complex life history and heterogeneous environment makes predictive modelling difficult.A machine learning approach (maximum entropy) was used to model occurrences and suitable habitat for the federally endangered dwarf wedgemussel, Alasmidonta heterodon, in Maryland's Coastal Plain catchments. Landscape-scale predictors (e.g. land cover, land use, soil characteristics, geology, flow characteristics, and climate) were used to predict the suitability of individual stream segments for A. heterodon.The best model contained variables at three scales: minimum elevation (segment scale), percentage Tertiary deposits, low intensity development, and woody wetlands (sub-catchment), and percentage low intensity development, pasture/hay agriculture, and average depth to the water table (catchment). Despite a very small sample size owing to the rarity of A. heterodon, cross-validated prediction accuracy was 91%.Most predicted suitable segments occur in catchments not known to contain A. heterodon, which provides opportunities for new discoveries or population restoration. These model predictions can guide surveys toward the streams with the best chance of containing the species or, alternatively, away from those streams with little chance of containing A. heterodon.Developed reaches had low predicted suitability for A. heterodon in the Coastal Plain. Urban and exurban sprawl continues to modify stream ecosystems in the region, underscoring the need to preserve existing populations and to discover and protect new populations.

Analysis of thermionic bare tether operation regimes in passive mode

NASA Astrophysics Data System (ADS)

Sanmartín, J. R.; Chen, Xin; Sánchez-Arriaga, G.

2017-01-01

A thermionic bare tether (TBT) is a long conductor coated with a low work-function material. In drag mode, a tether segment extending from anodic end A to a zero-bias point B, with the standard Orbital-motion-limited current collection, is followed by a complex cathodic segment. In general, as bias becomes more negative in moving from B to cathodic end C, one first finds space-charge-limited (SCL) emission covering up to some intermediate point B*, then full Richardson-Dushman (RD) emission reaching from B* to end C. An approximate analytical study, which combines the current and voltage profile equations with results from asymptotic studies of the Vlasov-Poisson system for emissive probes, is carried out to determine the parameter domain covering two limit regimes, which are effectively controlled by just two dimensionless parameters involving ambient plasma and TBT material properties. In one such limit regime, no point B* is reached and thus no full RD emission develops. In an opposite regime, SCL segment BB* is too short to contribute significantly to the current balance.

IMRT sequencing for a six-bank multi-leaf system.

PubMed

Topolnjak, R; van der Heide, U A; Lagendijk, J J W

2005-05-07

In this study, we present a sequencer for delivering step-and-shoot IMRT using a six-bank multi-leaf system. Such a system was proposed earlier and combines a high-resolution field-shaping ability with a large field size. It consists of three layers of two opposing leaf banks with 1 cm leaves. The layers are rotated relative to each other at 60 degrees . A low-resolution mode of sequencing is achieved by using one layer of leaves as primary MLC, while the other two are used to improve back-up collimation. For high-resolution sequencing, an algorithm is presented that creates segments shaped by all six banks. Compared to a hypothetical mini-MLC with 0.4 cm leaves, a similar performance can be achieved, but a trade-off has to be made between accuracy and the number of segments.

A label field fusion bayesian model and its penalized maximum rand estimator for image segmentation.

PubMed

Mignotte, Max

2010-06-01

This paper presents a novel segmentation approach based on a Markov random field (MRF) fusion model which aims at combining several segmentation results associated with simpler clustering models in order to achieve a more reliable and accurate segmentation result. The proposed fusion model is derived from the recently introduced probabilistic Rand measure for comparing one segmentation result to one or more manual segmentations of the same image. This non-parametric measure allows us to easily derive an appealing fusion model of label fields, easily expressed as a Gibbs distribution, or as a nonstationary MRF model defined on a complete graph. Concretely, this Gibbs energy model encodes the set of binary constraints, in terms of pairs of pixel labels, provided by each segmentation results to be fused. Combined with a prior distribution, this energy-based Gibbs model also allows for definition of an interesting penalized maximum probabilistic rand estimator with which the fusion of simple, quickly estimated, segmentation results appears as an interesting alternative to complex segmentation models existing in the literature. This fusion framework has been successfully applied on the Berkeley image database. The experiments reported in this paper demonstrate that the proposed method is efficient in terms of visual evaluation and quantitative performance measures and performs well compared to the best existing state-of-the-art segmentation methods recently proposed in the literature.

Molecular Analysis of Non-Small Cell Lung Cancer (NSCLC) Identifies Subsets with Different Sensitivity to Insulin like Growth Factor I Receptor (IGF-IR) Inhibition

PubMed Central

Gualberto, Antonio; Dolled-Filhart, Marisa; Gustavson, Mark; Christiansen, Jason; Wang, Yu-Fen; Hixon, Mary L.; Reynolds, Jennifer; McDonald, Sandra; Ang, Agnes; Rimm, David L.; Langer, Corey J.; Blakely, Johnetta; Garland, Linda; Paz-Ares, Luis G.; Karp, Daniel D.; Lee, Adrian V.

2010-01-01

Purpose Identify molecular determinants of sensitivity of NSCLC to anti-insulin like growth factor receptor (IGF-IR) therapy. Experimental Design 216 tumor samples were investigated. 165 consisted of retrospective analyses of banked tissue and an additional 51 were from patients enrolled in a phase 2 study of figitumumab (F), a monoclonal antibody against the IGF-IR, in stage IIIb/IV NSCLC. Biomarkers assessed included IGF-IR, EGFR, IGF-2, IGF-2R, IRS-1, IRS-2, vimentin and E-cadherin. Sub-cellular localization of IRS-1 and phosphorylation levels of MAPK and Akt1 were also analyzed. Results IGF-IR was differentially expressed across histological subtypes (P=0.04), with highest levels observed in squamous cell tumors. Elevated IGF-IR expression was also observed in a small number of squamous cell tumors responding to chemotherapy combined with F (p=0.008). Since no other biomarker/response interaction was observed using classical histological sub-typing, a molecular approach was undertaken to segment NSCLC into mechanism-based subpopulations. Principal component analysis and unsupervised Bayesian clustering identified 3 NSCLC subsets that resembled the steps of the epithelial-to-mesenchymal transition: E-cadherin high/IRS-1 low (Epithelial-like), E-cadherin intermediate/IRS-1 high (Transitional) and E-cadherin low/IRS-1 low (Mesenchymal-like). Several markers of the IGF-IR pathway were over-expressed in the Transitional subset. Furthermore, a higher response rate to the combination of chemotherapy and F was observed in Transitional tumors (71%) compared to those in the Mesenchymal-like subset (32%, p=0.03). Only one Epithelial-like tumor was identified in the phase 2 study, suggesting that advanced NSCLC has undergone significant de-differentiation at diagnosis. Conclusion NSCLC comprises molecular subsets with differential sensitivity to IGF-IR inhibition. PMID:20670944

Image segmentation and dynamic lineage analysis in single-cell fluorescence microscopy.

PubMed

Wang, Quanli; Niemi, Jarad; Tan, Chee-Meng; You, Lingchong; West, Mike

2010-01-01

An increasingly common component of studies in synthetic and systems biology is analysis of dynamics of gene expression at the single-cell level, a context that is heavily dependent on the use of time-lapse movies. Extracting quantitative data on the single-cell temporal dynamics from such movies remains a major challenge. Here, we describe novel methods for automating key steps in the analysis of single-cell, fluorescent images-segmentation and lineage reconstruction-to recognize and track individual cells over time. The automated analysis iteratively combines a set of extended morphological methods for segmentation, and uses a neighborhood-based scoring method for frame-to-frame lineage linking. Our studies with bacteria, budding yeast and human cells, demonstrate the portability and usability of these methods, whether using phase, bright field or fluorescent images. These examples also demonstrate the utility of our integrated approach in facilitating analyses of engineered and natural cellular networks in diverse settings. The automated methods are implemented in freely available, open-source software.

Dynamic multi-segmental postural control in patients with chronic non-specific low back pain compared to pain-free controls: A cross-sectional study.

PubMed

McCaskey, Michael A; Wirth, Brigitte; Schuster-Amft, Corina; de Bruin, Eling D

2018-01-01

Reduced postural control is thought to contribute to the development and persistence of chronic non-specific low back pain (CNLBP). It is therefore frequently assessed in affected patients and commonly reported as the average amount of postural sway while standing upright under a variety of sensory conditions. These averaged linear outcomes, such as mean centre of pressure (CP) displacement or mean CP surface areas, may not reflect the true postural status. Adding nonlinear outcomes and multi-segmental kinematic analysis has been reported to better reflect the complexity of postural control and may detect subtler postural differences. In this cross-sectional study, a combination of linear and nonlinear postural parameters were assessed in patients with CNLBP (n = 24, 24-75 years, 9 females) and compared to symptom-free controls (CG, n = 34, 22-67 years, 11 females). Primary outcome was postural control measured by variance of joint configurations (uncontrolled manifold index, UI), confidence ellipse surface areas (CEA) and approximate entropy (ApEn) of CP dispersion during the response phase of a perturbed postural control task on a swaying platform. Secondary outcomes were segment excursions and clinical outcome correlates for pain and function. Non-parametric tests for group comparison with P-adjustment for multiple comparisons were conducted. Principal component analysis was applied to identify patterns of segmental contribution in both groups. CNLBP and CG performed similarly with respect to the primary outcomes. Comparison of joint kinematics revealed significant differences of hip (P < .001) and neck (P < .025) angular excursion, representing medium to large group effects (r's = .36 - .51). Significant (P's < .05), but moderate correlations of ApEn (r = -.42) and UI (r = -.46) with the health-related outcomes were observed. These findings lend further support to the notion that averaged linear outcomes do not suffice to describe subtle postural differences in CNLBP patients with low to moderate pain status.

Impact of PET and MRI threshold-based tumor volume segmentation on patient-specific targeted radionuclide therapy dosimetry using CLR1404.

PubMed

Besemer, Abigail E; Titz, Benjamin; Grudzinski, Joseph J; Weichert, Jamey P; Kuo, John S; Robins, H Ian; Hall, Lance T; Bednarz, Bryan P

2017-07-06

Variations in tumor volume segmentation methods in targeted radionuclide therapy (TRT) may lead to dosimetric uncertainties. This work investigates the impact of PET and MRI threshold-based tumor segmentation on TRT dosimetry in patients with primary and metastatic brain tumors. In this study, PET/CT images of five brain cancer patients were acquired at 6, 24, and 48 h post-injection of 124 I-CLR1404. The tumor volume was segmented using two standardized uptake value (SUV) threshold levels, two tumor-to-background ratio (TBR) threshold levels, and a T1 Gadolinium-enhanced MRI threshold. The dice similarity coefficient (DSC), jaccard similarity coefficient (JSC), and overlap volume (OV) metrics were calculated to compare differences in the MRI and PET contours. The therapeutic 131 I-CLR1404 voxel-level dose distribution was calculated from the 124 I-CLR1404 activity distribution using RAPID, a Geant4 Monte Carlo internal dosimetry platform. The TBR, SUV, and MRI tumor volumes ranged from 2.3-63.9 cc, 0.1-34.7 cc, and 0.4-11.8 cc, respectively. The average  ±  standard deviation (range) was 0.19  ±  0.13 (0.01-0.51), 0.30  ±  0.17 (0.03-0.67), and 0.75  ±  0.29 (0.05-1.00) for the JSC, DSC, and OV, respectively. The DSC and JSC values were small and the OV values were large for both the MRI-SUV and MRI-TBR combinations because the regions of PET uptake were generally larger than the MRI enhancement. Notable differences in the tumor dose volume histograms were observed for each patient. The mean (standard deviation) 131 I-CLR1404 tumor doses ranged from 0.28-1.75 Gy GBq -1 (0.07-0.37 Gy GBq -1 ). The ratio of maximum-to-minimum mean doses for each patient ranged from 1.4-2.0. The tumor volume and the interpretation of the tumor dose is highly sensitive to the imaging modality, PET enhancement metric, and threshold level used for tumor volume segmentation. The large variations in tumor doses clearly demonstrate the need for standard protocols for multimodality tumor segmentation in TRT dosimetry.

Impact of PET and MRI threshold-based tumor volume segmentation on patient-specific targeted radionuclide therapy dosimetry using CLR1404

NASA Astrophysics Data System (ADS)

Besemer, Abigail E.; Titz, Benjamin; Grudzinski, Joseph J.; Weichert, Jamey P.; Kuo, John S.; Robins, H. Ian; Hall, Lance T.; Bednarz, Bryan P.

2017-08-01

Variations in tumor volume segmentation methods in targeted radionuclide therapy (TRT) may lead to dosimetric uncertainties. This work investigates the impact of PET and MRI threshold-based tumor segmentation on TRT dosimetry in patients with primary and metastatic brain tumors. In this study, PET/CT images of five brain cancer patients were acquired at 6, 24, and 48 h post-injection of 124I-CLR1404. The tumor volume was segmented using two standardized uptake value (SUV) threshold levels, two tumor-to-background ratio (TBR) threshold levels, and a T1 Gadolinium-enhanced MRI threshold. The dice similarity coefficient (DSC), jaccard similarity coefficient (JSC), and overlap volume (OV) metrics were calculated to compare differences in the MRI and PET contours. The therapeutic 131I-CLR1404 voxel-level dose distribution was calculated from the 124I-CLR1404 activity distribution using RAPID, a Geant4 Monte Carlo internal dosimetry platform. The TBR, SUV, and MRI tumor volumes ranged from 2.3-63.9 cc, 0.1-34.7 cc, and 0.4-11.8 cc, respectively. The average  ±  standard deviation (range) was 0.19  ±  0.13 (0.01-0.51), 0.30  ±  0.17 (0.03-0.67), and 0.75  ±  0.29 (0.05-1.00) for the JSC, DSC, and OV, respectively. The DSC and JSC values were small and the OV values were large for both the MRI-SUV and MRI-TBR combinations because the regions of PET uptake were generally larger than the MRI enhancement. Notable differences in the tumor dose volume histograms were observed for each patient. The mean (standard deviation) 131I-CLR1404 tumor doses ranged from 0.28-1.75 Gy GBq-1 (0.07-0.37 Gy GBq-1). The ratio of maximum-to-minimum mean doses for each patient ranged from 1.4-2.0. The tumor volume and the interpretation of the tumor dose is highly sensitive to the imaging modality, PET enhancement metric, and threshold level used for tumor volume segmentation. The large variations in tumor doses clearly demonstrate the need for standard protocols for multimodality tumor segmentation in TRT dosimetry.

A machine learning approach to multi-level ECG signal quality classification.

PubMed

Li, Qiao; Rajagopalan, Cadathur; Clifford, Gari D

2014-12-01

Current electrocardiogram (ECG) signal quality assessment studies have aimed to provide a two-level classification: clean or noisy. However, clinical usage demands more specific noise level classification for varying applications. This work outlines a five-level ECG signal quality classification algorithm. A total of 13 signal quality metrics were derived from segments of ECG waveforms, which were labeled by experts. A support vector machine (SVM) was trained to perform the classification and tested on a simulated dataset and was validated using data from the MIT-BIH arrhythmia database (MITDB). The simulated training and test datasets were created by selecting clean segments of the ECG in the 2011 PhysioNet/Computing in Cardiology Challenge database, and adding three types of real ECG noise at different signal-to-noise ratio (SNR) levels from the MIT-BIH Noise Stress Test Database (NSTDB). The MITDB was re-annotated for five levels of signal quality. Different combinations of the 13 metrics were trained and tested on the simulated datasets and the best combination that produced the highest classification accuracy was selected and validated on the MITDB. Performance was assessed using classification accuracy (Ac), and a single class overlap accuracy (OAc), which assumes that an individual type classified into an adjacent class is acceptable. An Ac of 80.26% and an OAc of 98.60% on the test set were obtained by selecting 10 metrics while 57.26% (Ac) and 94.23% (OAc) were the numbers for the unseen MITDB validation data without retraining. By performing the fivefold cross validation, an Ac of 88.07±0.32% and OAc of 99.34±0.07% were gained on the validation fold of MITDB. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Localized-atlas-based segmentation of breast MRI in a decision-making framework.

PubMed

Fooladivanda, Aida; Shokouhi, Shahriar B; Ahmadinejad, Nasrin

2017-03-01

Breast-region segmentation is an important step for density estimation and Computer-Aided Diagnosis (CAD) systems in Magnetic Resonance Imaging (MRI). Detection of breast-chest wall boundary is often a difficult task due to similarity between gray-level values of fibroglandular tissue and pectoral muscle. This paper proposes a robust breast-region segmentation method which is applicable for both complex cases with fibroglandular tissue connected to the pectoral muscle, and simple cases with high contrast boundaries. We present a decision-making framework based on geometric features and support vector machine (SVM) to classify breasts in two main groups, complex and simple. For complex cases, breast segmentation is done using a combination of intensity-based and atlas-based techniques; however, only intensity-based operation is employed for simple cases. A novel atlas-based method, that is called localized-atlas, accomplishes the processes of atlas construction and registration based on the region of interest (ROI). Atlas-based segmentation is performed by relying on the chest wall template. Our approach is validated using a dataset of 210 cases. Based on similarity between automatic and manual segmentation results, the proposed method achieves Dice similarity coefficient, Jaccard coefficient, total overlap, false negative, and false positive values of 96.3, 92.9, 97.4, 2.61 and 4.77%, respectively. The localization error of the breast-chest wall boundary is 1.97 mm, in terms of averaged deviation distance. The achieved results prove that the suggested framework performs the breast segmentation with negligible errors and efficient computational time for different breasts from the viewpoints of size, shape, and density pattern.

Psychographic Segments of College Females and Males in Relation to Substance Use Behaviors

PubMed Central

Suragh, Tiffany Ashley; Berg, Carla J.; Nehl, Eric J.

2014-01-01

Objectives A common commercial marketing segmentation technique is to divide a population into groups based on psychographic characteristics (i.e., attitudes and interests). We used this approach to define segments of female and male college students and examine substance use differences. Method We administered an online survey to 24,055 students at six colleges in the Southeastern United States (response rate 20.1%, n = 4,840), obtaining complete data from 3,469 participants. We assessed sociodemographics, psychographic factors such as those used by the tobacco industry to define market segments, and substance use (cigarettes, other tobacco products, alcohol, and marijuana). Cluster analysis was conducted among females and males using 15 psychographic measures (sensation seeking, Big Five personality traits, and nine measures adapted from tobacco industry documents), identifying three segments per sex. Results Safe responsibles were characterized by high levels of agreeableness, conscientiousness, emotional stability, academic achievement, and religious service attendance. Stoic individualists were characterized by low extraversion, sensation seeking, and openness. Thrill-seeking socializers were characterized by high levels of sensation seeking and extraversion. Among females, thrill-seeking socializers were significantly more likely than safe responsibles to have used any substance in the prior 30 days (odds ratio [OR] = 2.04, 95% confidence interval [CI] [1.65, 2.52]; Nagelkerke R2 = .084). Among males, stoic individualists (OR = 1.50, CI [1.08, 2.08]) and thrill-seeking socializers (OR = 1.53, CI [1.09, 2.13]) were more likely than safe responsibles to have used substances in the past 30 days (Nagelkerke R2: .109). Conclusion Psychographic segmentation can identify young adult subgroups with differing psychographic and substance use profiles and inform health campaigns and messaging targeting youth. PMID:24729744

Psychographic Segments of College Females and Males in Relation to Substance Use Behaviors.

PubMed

Suragh, Tiffany Ashley; Berg, Carla J; Nehl, Eric J

2013-09-01

A common commercial marketing segmentation technique is to divide a population into groups based on psychographic characteristics (i.e., attitudes and interests). We used this approach to define segments of female and male college students and examine substance use differences. We administered an online survey to 24,055 students at six colleges in the Southeastern United States (response rate 20.1%, n = 4,840), obtaining complete data from 3,469 participants. We assessed sociodemographics, psychographic factors such as those used by the tobacco industry to define market segments, and substance use (cigarettes, other tobacco products, alcohol, and marijuana). Cluster analysis was conducted among females and males using 15 psychographic measures (sensation seeking, Big Five personality traits, and nine measures adapted from tobacco industry documents), identifying three segments per sex. Safe responsibles were characterized by high levels of agreeableness, conscientiousness, emotional stability, academic achievement, and religious service attendance. Stoic individualists were characterized by low extraversion, sensation seeking, and openness. Thrill-seeking socializers were characterized by high levels of sensation seeking and extraversion. Among females, thrill-seeking socializers were significantly more likely than safe responsibles to have used any substance in the prior 30 days (odds ratio [OR] = 2.04, 95% confidence interval [CI] [1.65, 2.52]; Nagelkerke R 2 = .084). Among males, stoic individualists (OR = 1.50, CI [1.08, 2.08]) and thrill-seeking socializers (OR = 1.53, CI [1.09, 2.13]) were more likely than safe responsibles to have used substances in the past 30 days (Nagelkerke R 2 : .109). Psychographic segmentation can identify young adult subgroups with differing psychographic and substance use profiles and inform health campaigns and messaging targeting youth.

Advances on Propulsion Technology for High-Speed Aircraft. Volume 1

DTIC Science & Technology

2007-03-01

sprayed Cu -3% Ag alloys , ITSC 2001 - Singapour - 6dit6e par C.C. Berndt - K.A. Khor et E.F. Lugscheider - ASM-TSS - Materials park - OH-USA, p.633... spraying of CuCrNb powder and a more advanced approach which combines the advantages of a high temperature, low density and porous carbon-fibre...physical vapour deposition (EB-PVD), vacuum plasma spraying (VPS) and solution plasma spraying (SPS) [38-41]. A segmented sub-scale model combustor with

ACM-based automatic liver segmentation from 3-D CT images by combining multiple atlases and improved mean-shift techniques.

PubMed

Ji, Hongwei; He, Jiangping; Yang, Xin; Deklerck, Rudi; Cornelis, Jan

2013-05-01

In this paper, we present an autocontext model(ACM)-based automatic liver segmentation algorithm, which combines ACM, multiatlases, and mean-shift techniques to segment liver from 3-D CT images. Our algorithm is a learning-based method and can be divided into two stages. At the first stage, i.e., the training stage, ACM is performed to learn a sequence of classifiers in each atlas space (based on each atlas and other aligned atlases). With the use of multiple atlases, multiple sequences of ACM-based classifiers are obtained. At the second stage, i.e., the segmentation stage, the test image will be segmented in each atlas space by applying each sequence of ACM-based classifiers. The final segmentation result will be obtained by fusing segmentation results from all atlas spaces via a multiclassifier fusion technique. Specially, in order to speed up segmentation, given a test image, we first use an improved mean-shift algorithm to perform over-segmentation and then implement the region-based image labeling instead of the original inefficient pixel-based image labeling. The proposed method is evaluated on the datasets of MICCAI 2007 liver segmentation challenge. The experimental results show that the average volume overlap error and the average surface distance achieved by our method are 8.3% and 1.5 m, respectively, which are comparable to the results reported in the existing state-of-the-art work on liver segmentation.

Five-Segment Reusable Solid Rocket Booster Upgrade

NASA Technical Reports Server (NTRS)

Sauvageau, Don

1999-01-01

The Five Segment Reusable Solid Rocket Booster (RSRB) feasibility status is presented in viewgraph form. The Five Segment Booster (FSB) objective is to provide a low cost, low risk approach to increase reliability and safety of the Shuttle system. Topics include: booster upgrade requirements; design summary; reliability issues; booster trajectories; launch site assessment; and enhanced abort modes.

Computer-aided detection of bladder mass within non-contrast-enhanced region of CT Urography (CTU)

NASA Astrophysics Data System (ADS)

Cha, Kenny H.; Hadjiiski, Lubomir M.; Chan, Heang-Ping; Caoili, Elaine M.; Cohan, Richard H.; Weizer, Alon; Zhou, Chuan

2016-03-01

We are developing a computer-aided detection system for bladder cancer in CT urography (CTU). We have previously developed methods for detection of bladder masses within the contrast-enhanced region of the bladder. In this study, we investigated methods for detection of bladder masses within the non-contrast enhanced region. The bladder was first segmented using a newly developed deep-learning convolutional neural network in combination with level sets. The non-contrast-enhanced region was separated from the contrast-enhanced region with a maximum-intensityprojection- based method. The non-contrast region was smoothed and a gray level threshold was employed to segment the bladder wall and potential masses. The bladder wall was transformed into a straightened thickness profile, which was analyzed to identify lesion candidates as a prescreening step. The lesion candidates were segmented using our autoinitialized cascaded level set (AI-CALS) segmentation method, and 27 morphological features were extracted for each candidate. Stepwise feature selection with simplex optimization and leave-one-case-out resampling were used for training and validation of a false positive (FP) classifier. In each leave-one-case-out cycle, features were selected from the training cases and a linear discriminant analysis (LDA) classifier was designed to merge the selected features into a single score for classification of the left-out test case. A data set of 33 cases with 42 biopsy-proven lesions in the noncontrast enhanced region was collected. During prescreening, the system obtained 83.3% sensitivity at an average of 2.4 FPs/case. After feature extraction and FP reduction by LDA, the system achieved 81.0% sensitivity at 2.0 FPs/case, and 73.8% sensitivity at 1.5 FPs/case.

Research on a pulmonary nodule segmentation method combining fast self-adaptive FCM and classification.

PubMed

Liu, Hui; Zhang, Cai-Ming; Su, Zhi-Yuan; Wang, Kai; Deng, Kai

2015-01-01

The key problem of computer-aided diagnosis (CAD) of lung cancer is to segment pathologically changed tissues fast and accurately. As pulmonary nodules are potential manifestation of lung cancer, we propose a fast and self-adaptive pulmonary nodules segmentation method based on a combination of FCM clustering and classification learning. The enhanced spatial function considers contributions to fuzzy membership from both the grayscale similarity between central pixels and single neighboring pixels and the spatial similarity between central pixels and neighborhood and improves effectively the convergence rate and self-adaptivity of the algorithm. Experimental results show that the proposed method can achieve more accurate segmentation of vascular adhesion, pleural adhesion, and ground glass opacity (GGO) pulmonary nodules than other typical algorithms.

Multi-font printed Mongolian document recognition system

NASA Astrophysics Data System (ADS)

Peng, Liangrui; Liu, Changsong; Ding, Xiaoqing; Wang, Hua; Jin, Jianming

2009-01-01

Mongolian is one of the major ethnic languages in China. Large amount of Mongolian printed documents need to be digitized in digital library and various applications. Traditional Mongolian script has unique writing style and multi-font-type variations, which bring challenges to Mongolian OCR research. As traditional Mongolian script has some characteristics, for example, one character may be part of another character, we define the character set for recognition according to the segmented components, and the components are combined into characters by rule-based post-processing module. For character recognition, a method based on visual directional feature and multi-level classifiers is presented. For character segmentation, a scheme is used to find the segmentation point by analyzing the properties of projection and connected components. As Mongolian has different font-types which are categorized into two major groups, the parameter of segmentation is adjusted for each group. A font-type classification method for the two font-type group is introduced. For recognition of Mongolian text mixed with Chinese and English, language identification and relevant character recognition kernels are integrated. Experiments show that the presented methods are effective. The text recognition rate is 96.9% on the test samples from practical documents with multi-font-types and mixed scripts.

Automatic FDG-PET-based tumor and metastatic lymph node segmentation in cervical cancer

NASA Astrophysics Data System (ADS)

Arbonès, Dídac R.; Jensen, Henrik G.; Loft, Annika; Munck af Rosenschöld, Per; Hansen, Anders Elias; Igel, Christian; Darkner, Sune

2014-03-01

Treatment of cervical cancer, one of the three most commonly diagnosed cancers worldwide, often relies on delineations of the tumour and metastases based on PET imaging using the contrast agent 18F-Fluorodeoxyglucose (FDG). We present a robust automatic algorithm for segmenting the gross tumour volume (GTV) and metastatic lymph nodes in such images. As the cervix is located next to the bladder and FDG is washed out through the urine, the PET-positive GTV and the bladder cannot be easily separated. Our processing pipeline starts with a histogram-based region of interest detection followed by level set segmentation. After that, morphological image operations combined with clustering, region growing, and nearest neighbour labelling allow to remove the bladder and to identify the tumour and metastatic lymph nodes. The proposed method was applied to 125 patients and no failure could be detected by visual inspection. We compared our segmentations with results from manual delineations of corresponding MR and CT images, showing that the detected GTV lays at least 97.5% within the MR/CT delineations. We conclude that the algorithm has a very high potential for substituting the tedious manual delineation of PET positive areas.

Reconstruction of incomplete cell paths through a 3D-2D level set segmentation

NASA Astrophysics Data System (ADS)

Hariri, Maia; Wan, Justin W. L.

2012-02-01

Segmentation of fluorescent cell images has been a popular technique for tracking live cells. One challenge of segmenting cells from fluorescence microscopy is that cells in fluorescent images frequently disappear. When the images are stacked together to form a 3D image volume, the disappearance of the cells leads to broken cell paths. In this paper, we present a segmentation method that can reconstruct incomplete cell paths. The key idea of this model is to perform 2D segmentation in a 3D framework. The 2D segmentation captures the cells that appear in the image slices while the 3D segmentation connects the broken cell paths. The formulation is similar to the Chan-Vese level set segmentation which detects edges by comparing the intensity value at each voxel with the mean intensity values inside and outside of the level set surface. Our model, however, performs the comparison on each 2D slice with the means calculated by the 2D projected contour. The resulting effect is to segment the cells on each image slice. Unlike segmentation on each image frame individually, these 2D contours together form the 3D level set function. By enforcing minimum mean curvature on the level set surface, our segmentation model is able to extend the cell contours right before (and after) the cell disappears (and reappears) into the gaps, eventually connecting the broken paths. We will present segmentation results of C2C12 cells in fluorescent images to illustrate the effectiveness of our model qualitatively and quantitatively by different numerical examples.

Oriented active shape models.

PubMed

Liu, Jiamin; Udupa, Jayaram K

2009-04-01

Active shape models (ASM) are widely employed for recognizing anatomic structures and for delineating them in medical images. In this paper, a novel strategy called oriented active shape models (OASM) is presented in an attempt to overcome the following five limitations of ASM: 1) lower delineation accuracy, 2) the requirement of a large number of landmarks, 3) sensitivity to search range, 4) sensitivity to initialization, and 5) inability to fully exploit the specific information present in the given image to be segmented. OASM effectively combines the rich statistical shape information embodied in ASM with the boundary orientedness property and the globally optimal delineation capability of the live wire methodology of boundary segmentation. The latter characteristics allow live wire to effectively separate an object boundary from other nonobject boundaries with similar properties especially when they come very close in the image domain. The approach leads to a two-level dynamic programming method, wherein the first level corresponds to boundary recognition and the second level corresponds to boundary delineation, and to an effective automatic initialization method. The method outputs a globally optimal boundary that agrees with the shape model if the recognition step is successful in bringing the model close to the boundary in the image. Extensive evaluation experiments have been conducted by utilizing 40 image (magnetic resonance and computed tomography) data sets in each of five different application areas for segmenting breast, liver, bones of the foot, and cervical vertebrae of the spine. Comparisons are made between OASM and ASM based on precision, accuracy, and efficiency of segmentation. Accuracy is assessed using both region-based false positive and false negative measures and boundary-based distance measures. The results indicate the following: 1) The accuracy of segmentation via OASM is considerably better than that of ASM; 2) The number of landmarks can be reduced by a factor of 3 in OASM over that in ASM; 3) OASM becomes largely independent of search range and initialization becomes automatic. All three benefits of OASM ensue mainly from the severe constraints brought in by the boundary-orientedness property of live wire and the globally optimal solution found by the 2-level dynamic programming algorithm.

A discriminative model-constrained graph cuts approach to fully automated pediatric brain tumor segmentation in 3-D MRI.

PubMed

Wels, Michael; Carneiro, Gustavo; Aplas, Alexander; Huber, Martin; Hornegger, Joachim; Comaniciu, Dorin

2008-01-01

In this paper we present a fully automated approach to the segmentation of pediatric brain tumors in multi-spectral 3-D magnetic resonance images. It is a top-down segmentation approach based on a Markov random field (MRF) model that combines probabilistic boosting trees (PBT) and lower-level segmentation via graph cuts. The PBT algorithm provides a strong discriminative observation model that classifies tumor appearance while a spatial prior takes into account the pair-wise homogeneity in terms of classification labels and multi-spectral voxel intensities. The discriminative model relies not only on observed local intensities but also on surrounding context for detecting candidate regions for pathology. A mathematically sound formulation for integrating the two approaches into a unified statistical framework is given. The proposed method is applied to the challenging task of detection and delineation of pediatric brain tumors. This segmentation task is characterized by a high non-uniformity of both the pathology and the surrounding non-pathologic brain tissue. A quantitative evaluation illustrates the robustness of the proposed method. Despite dealing with more complicated cases of pediatric brain tumors the results obtained are mostly better than those reported for current state-of-the-art approaches to 3-D MR brain tumor segmentation in adult patients. The entire processing of one multi-spectral data set does not require any user interaction, and takes less time than previously proposed methods.

A semiautomatic segmentation method for prostate in CT images using local texture classification and statistical shape modeling.

PubMed

Shahedi, Maysam; Halicek, Martin; Guo, Rongrong; Zhang, Guoyi; Schuster, David M; Fei, Baowei

2018-06-01

Prostate segmentation in computed tomography (CT) images is useful for treatment planning and procedure guidance such as external beam radiotherapy and brachytherapy. However, because of the low, soft tissue contrast of CT images, manual segmentation of the prostate is a time-consuming task with high interobserver variation. In this study, we proposed a semiautomated, three-dimensional (3D) segmentation for prostate CT images using shape and texture analysis and we evaluated the method against manual reference segmentations. The prostate gland usually has a globular shape with a smoothly curved surface, and its shape could be accurately modeled or reconstructed having a limited number of well-distributed surface points. In a training dataset, using the prostate gland centroid point as the origin of a coordination system, we defined an intersubject correspondence between the prostate surface points based on the spherical coordinates. We applied this correspondence to generate a point distribution model for prostate shape using principal component analysis and to study the local texture difference between prostate and nonprostate tissue close to the different prostate surface subregions. We used the learned shape and texture characteristics of the prostate in CT images and then combined them with user inputs to segment a new image. We trained our segmentation algorithm using 23 CT images and tested the algorithm on two sets of 10 nonbrachytherapy and 37 postlow dose rate brachytherapy CT images. We used a set of error metrics to evaluate the segmentation results using two experts' manual reference segmentations. For both nonbrachytherapy and post-brachytherapy image sets, the average measured Dice similarity coefficient (DSC) was 88% and the average mean absolute distance (MAD) was 1.9 mm. The average measured differences between the two experts on both datasets were 92% (DSC) and 1.1 mm (MAD). The proposed, semiautomatic segmentation algorithm showed a fast, robust, and accurate performance for 3D prostate segmentation of CT images, specifically when no previous, intrapatient information, that is, previously segmented images, was available. The accuracy of the algorithm is comparable to the best performance results reported in the literature and approaches the interexpert variability observed in manual segmentation. © 2018 American Association of Physicists in Medicine.

Short Segment Spinal Instrumentation With Index Vertebra Pedicle Screw Placement for Pathologies Involving the Anterior and Middle Vertebral Column Is as Effective as Long Segment Stabilization With Cage Reconstruction: A Biomechanical Study.

PubMed

Bartanusz, Viktor; Harris, Jonathan; Moldavsky, Mark; Cai, Yiwei; Bucklen, Brandon

2015-11-01

An in vitro, cadaveric biomechanical study. The aim of the present study was to compare single-segment posterior instrumentation and fracture-level screws with single/multilevel posterior fixation and corpectomy in a simulated, unstable burst fracture model. The optimal extent of instrumentation for surgical cases of non-neoplastic vertebral body pathologies remains uncertain. Although several clinical studies demonstrate advantages of short segment instrumentation with index-level screws over more extensive corpectomy and anterior-posterior techniques, a comprehensive biomechanical comparison of these techniques is currently lacking. Six bovine spines (T11-L5) were tested in flexion, extension, lateral bending (LB), and axial rotation (AR) following simulated burst fracture at L2. Posterior instrumentation included 1 level above/below (1LF) and 2 levels above/below fracture level (2LF), intermediate or index screws at fracture level (FF), and cross-connectors above/below fracture level (CC). Anterior corpectomy devices included expandable corpectomy spacers with/without integrated screws, ACDi and ACD, respectively FORTIFY-Integrated/FORTIFY; Globus Medical, Inc., PA. Constructs were tested in the following order: (1) Intact; (2) 1LF; (3) 1LF and CC; (4) 1LF and FF; (5) 1LF, CC, and FF; (6) 2LF; (7) 2LF and CC; (8) 2LF and FF; (9) 2LF, CC, and FF; (10) 2LF and ACD; (11) 2LF, ACD, and CC; (12) 1LF and ACDi; (13) 1LF, ACDi, and CC. During flexion, all constructs except 1LF reduced motion relative to intact (P ≤ 0.05). Anterior support was most stable, but no differences were found between constructs (P ≥ 0.05). Every construct reduced motion in extension, though no differences were found between constructs and intact (P ≥ 0.05). During LB, all constructs reduced motion relative to intact (P ≤ 0.05); 2LF constructs further reduced motion (P ≤ 0.05). No construct returned AR motion to intact, with significant increases in 1LF and ACDi, 2LF and ACD, and 2LF, ACD, and CC (P ≤ 0.05). Cross-connectors and fracture screws reinforced each other in posterior-only constructs, providing maximum stability (P ≥ 0.05). This biomechanical comparison study found no significant superiority of combined anterior-posterior constructs over short segment fracture screw fixation, only multilevel posterior instrumentation with and without anterior support, providing increased stability in LB. Biomechanical equivalency suggests that short segment fracture screw intervention may provide appropriate stabilization for non-neoplastic pathologies involving the anterior and middle vertebral columns. 2.

White blood cell segmentation by color-space-based k-means clustering.

PubMed

Zhang, Congcong; Xiao, Xiaoyan; Li, Xiaomei; Chen, Ying-Jie; Zhen, Wu; Chang, Jun; Zheng, Chengyun; Liu, Zhi

2014-09-01

White blood cell (WBC) segmentation, which is important for cytometry, is a challenging issue because of the morphological diversity of WBCs and the complex and uncertain background of blood smear images. This paper proposes a novel method for the nucleus and cytoplasm segmentation of WBCs for cytometry. A color adjustment step was also introduced before segmentation. Color space decomposition and k-means clustering were combined for segmentation. A database including 300 microscopic blood smear images were used to evaluate the performance of our method. The proposed segmentation method achieves 95.7% and 91.3% overall accuracy for nucleus segmentation and cytoplasm segmentation, respectively. Experimental results demonstrate that the proposed method can segment WBCs effectively with high accuracy.

Curriculum and Methods (EDS 300).

ERIC Educational Resources Information Center

McNaughton, Robert H.; And Others

This course is a team taught, 12 quarter-hour block course, which combines a general teaching, competency segment and a specialized subject area methods segment. It is required of all students seeking secondary certification and is supported out of the regular secondary department budget. The teaching competency segment has the following three…

WCPP-THE WOLF PLOTTING AND CONTOURING PACKAGE

NASA Technical Reports Server (NTRS)

Masaki, G. T.

1994-01-01

The WOLF Contouring and Plotting Package provides the user with a complete general purpose plotting and contouring capability. This package is a complete system for producing line printer, SC4020, Gerber, Calcomp, and SD4060 plots. The package has been designed to be highly flexible and easy to use. Any plot from a quick simple plot (which requires only one call to the package) to highly sophisticated plots (including motion picture plots) can be easily generated with only a basic knowledge of FORTRAN and the plot commands. Anyone designing a software system that requires plotted output will find that this package offers many advantages over the standard hardware support packages available. The WCPP package is divided into a plot segment and a contour segment. The plot segment can produce output for any combination of line printer, SC4020, Gerber, Calcomp, and SD4060 plots. The line printer plots allow the user to have plots available immediately after a job is run at a low cost. Although the resolution of line printer plots is low, the quick results allows the user to judge if a high resolution plot of a particular run is desirable. The SC4020 and SD4060 provide high speed high resolution cathode ray plots with film and hard copy output available. The Gerber and Calcomp plotters provide very high quality (of publishable quality) plots of good resolution. Being bed or drum type plotters, the Gerber and Calcomp plotters are usually slow and not suited for large volume plotting. All output for any or all of the plotters can be produced simultaneously. The types of plots supported are: linear, semi-log, log-log, polar, tabular data using the FORTRAN WRITE statement, 3-D perspective linear, and affine transformations. The labeling facility provides for horizontal labels, vertical labels, diagonal labels, vector characters of a requested size (special character fonts are easily implemented), and rotated letters. The gridding routines label the grid lines according to user specification. Special line features include multiple lines, dashed lines, and tic marks. The contour segment of this package is a collection of subroutines which can be used to produce contour plots and perform related functions. The package can contour any data which can be placed on a grid or data which is regularly spaced, including any general affine or polar grid data. The package includes routines which will grid random data. Contour levels can be specified at any values desired. Input data can be smoothed with undefined points being acceptable where data is unreliable or unknown. Plots which are extremely large or detailed can be automatically output in parts to improve resolution or overcome plotter size limitations. The contouring segment uses the plot segment for actual plotting, thus all the features described for the plotting segment are available to the user of the contouring segment. Included with this package are two data bases for producing world map plots in Mercator projection. One data base provides just continent outlines and another provides continent outlines and national borders in great detail. This package is written in FORTRAN IV and IBM OS ASSEMBLER and has been implemented on an IBM 360 with a central memory requirement of approximately 140K of 8 bit bytes. The ASSEMBLER routines are basic plotter interface routines. The WCPP package was developed in 1972.

The effect of rising vs. falling glucose level on amperometric glucose sensor lag and accuracy in Type 1 diabetes.

PubMed

Ward, W K; Engle, J M; Branigan, D; El Youssef, J; Massoud, R G; Castle, J R

2012-08-01

Because declining glucose levels should be detected quickly in persons with Type 1 diabetes, a lag between blood glucose and subcutaneous sensor glucose can be problematic. It is unclear whether the magnitude of sensor lag is lower during falling glucose than during rising glucose. Initially, we analysed 95 data segments during which glucose changed and during which very frequent reference blood glucose monitoring was performed. However, to minimize confounding effects of noise and calibration error, we excluded data segments in which there was substantial sensor error. After these exclusions, and combination of data from duplicate sensors, there were 72 analysable data segments (36 for rising glucose, 36 for falling). We measured lag in two ways: (1) the time delay at the vertical mid-point of the glucose change (regression delay); and (2) determination of the optimal time shift required to minimize the difference between glucose sensor signals and blood glucose values drawn concurrently. Using the regression delay method, the mean sensor lag for rising vs. falling glucose segments was 8.9 min (95%CI 6.1-11.6) vs. 1.5 min (95%CI -2.6 to 5.5, P<0.005). Using the time shift optimization method, results were similar, with a lag that was higher for rising than for falling segments [8.3 (95%CI 5.8-10.7) vs. 1.5 min (95% CI -2.2 to 5.2), P<0.001]. Commensurate with the lag results, sensor accuracy was greater during falling than during rising glucose segments. In Type 1 diabetes, when noise and calibration error are minimized to reduce effects that confound delay measurement, subcutaneous glucose sensors demonstrate a shorter lag duration and greater accuracy when glucose is falling than when rising. © 2011 The Authors. Diabetic Medicine © 2011 Diabetes UK.

Active learning based segmentation of Crohns disease from abdominal MRI.

PubMed

Mahapatra, Dwarikanath; Vos, Franciscus M; Buhmann, Joachim M

2016-05-01

This paper proposes a novel active learning (AL) framework, and combines it with semi supervised learning (SSL) for segmenting Crohns disease (CD) tissues from abdominal magnetic resonance (MR) images. Robust fully supervised learning (FSL) based classifiers require lots of labeled data of different disease severities. Obtaining such data is time consuming and requires considerable expertise. SSL methods use a few labeled samples, and leverage the information from many unlabeled samples to train an accurate classifier. AL queries labels of most informative samples and maximizes gain from the labeling effort. Our primary contribution is in designing a query strategy that combines novel context information with classification uncertainty and feature similarity. Combining SSL and AL gives a robust segmentation method that: (1) optimally uses few labeled samples and many unlabeled samples; and (2) requires lower training time. Experimental results show our method achieves higher segmentation accuracy than FSL methods with fewer samples and reduced training effort. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Optic disc segmentation for glaucoma screening system using fundus images.

PubMed

Almazroa, Ahmed; Sun, Weiwei; Alodhayb, Sami; Raahemifar, Kaamran; Lakshminarayanan, Vasudevan

2017-01-01

Segmenting the optic disc (OD) is an important and essential step in creating a frame of reference for diagnosing optic nerve head pathologies such as glaucoma. Therefore, a reliable OD segmentation technique is necessary for automatic screening of optic nerve head abnormalities. The main contribution of this paper is in presenting a novel OD segmentation algorithm based on applying a level set method on a localized OD image. To prevent the blood vessels from interfering with the level set process, an inpainting technique was applied. As well an important contribution was to involve the variations in opinions among the ophthalmologists in detecting the disc boundaries and diagnosing the glaucoma. Most of the previous studies were trained and tested based on only one opinion, which can be assumed to be biased for the ophthalmologist. In addition, the accuracy was calculated based on the number of images that coincided with the ophthalmologists' agreed-upon images, and not only on the overlapping images as in previous studies. The ultimate goal of this project is to develop an automated image processing system for glaucoma screening. The disc algorithm is evaluated using a new retinal fundus image dataset called RIGA (retinal images for glaucoma analysis). In the case of low-quality images, a double level set was applied, in which the first level set was considered to be localization for the OD. Five hundred and fifty images are used to test the algorithm accuracy as well as the agreement among the manual markings of six ophthalmologists. The accuracy of the algorithm in marking the optic disc area and centroid was 83.9%, and the best agreement was observed between the results of the algorithm and manual markings in 379 images.

A low-cost three-dimensional laser surface scanning approach for defining body segment parameters.

PubMed

Pandis, Petros; Bull, Anthony Mj

2017-11-01

Body segment parameters are used in many different applications in ergonomics as well as in dynamic modelling of the musculoskeletal system. Body segment parameters can be defined using different methods, including techniques that involve time-consuming manual measurements of the human body, used in conjunction with models or equations. In this study, a scanning technique for measuring subject-specific body segment parameters in an easy, fast, accurate and low-cost way was developed and validated. The scanner can obtain the body segment parameters in a single scanning operation, which takes between 8 and 10 s. The results obtained with the system show a standard deviation of 2.5% in volumetric measurements of the upper limb of a mannequin and 3.1% difference between scanning volume and actual volume. Finally, the maximum mean error for the moment of inertia by scanning a standard-sized homogeneous object was 2.2%. This study shows that a low-cost system can provide quick and accurate subject-specific body segment parameter estimates.

Machine learning based brain tumour segmentation on limited data using local texture and abnormality.

PubMed

Bonte, Stijn; Goethals, Ingeborg; Van Holen, Roel

2018-05-07

Brain tumour segmentation in medical images is a very challenging task due to the large variety in tumour shape, position, appearance, scanning modalities and scanning parameters. Most existing segmentation algorithms use information from four different MRI-sequences, but since this is often not available, there is need for a method able to delineate the different tumour tissues based on a minimal amount of data. We present a novel approach using a Random Forests model combining voxelwise texture and abnormality features on a contrast-enhanced T1 and FLAIR MRI. We transform the two scans into 275 feature maps. A random forest model next calculates the probability to belong to 4 tumour classes or 5 normal classes. Afterwards, a dedicated voxel clustering algorithm provides the final tumour segmentation. We trained our method on the BraTS 2013 database and validated it on the larger BraTS 2017 dataset. We achieve median Dice scores of 40.9% (low-grade glioma) and 75.0% (high-grade glioma) to delineate the active tumour, and 68.4%/80.1% for the total abnormal region including edema. Our fully automated brain tumour segmentation algorithm is able to delineate contrast enhancing tissue and oedema with high accuracy based only on post-contrast T1-weighted and FLAIR MRI, whereas for non-enhancing tumour tissue and necrosis only moderate results are obtained. This makes the method especially suitable for high-grade glioma. Copyright © 2018 Elsevier Ltd. All rights reserved.

Robust QRS detection for HRV estimation from compressively sensed ECG measurements for remote health-monitoring systems.

PubMed

Pant, Jeevan K; Krishnan, Sridhar

2018-03-15

To present a new compressive sensing (CS)-based method for the acquisition of ECG signals and for robust estimation of heart-rate variability (HRV) parameters from compressively sensed measurements with high compression ratio. CS is used in the biosensor to compress the ECG signal. Estimation of the locations of QRS segments is carried out by applying two algorithms on the compressed measurements. The first algorithm reconstructs the ECG signal by enforcing a block-sparse structure on the first-order difference of the signal, so the transient QRS segments are significantly emphasized on the first-order difference of the signal. Multiple block-divisions of the signals are carried out with various block lengths, and multiple reconstructed signals are combined to enhance the robustness of the localization of the QRS segments. The second algorithm removes errors in the locations of QRS segments by applying low-pass filtering and morphological operations. The proposed CS-based method is found to be effective for the reconstruction of ECG signals by enforcing transient QRS structures on the first-order difference of the signal. It is demonstrated to be robust not only to high compression ratio but also to various artefacts present in ECG signals acquired by using on-body wireless sensors. HRV parameters computed by using the QRS locations estimated from the signals reconstructed with a compression ratio as high as 90% are comparable with that computed by using QRS locations estimated by using the Pan-Tompkins algorithm. The proposed method is useful for the realization of long-term HRV monitoring systems by using CS-based low-power wireless on-body biosensors.

A Hybrid Method for Pancreas Extraction from CT Image Based on Level Set Methods

PubMed Central

Tan, Hanqing; Fujita, Hiroshi

2013-01-01

This paper proposes a novel semiautomatic method to extract the pancreas from abdominal CT images. Traditional level set and region growing methods that request locating initial contour near the final boundary of object have problem of leakage to nearby tissues of pancreas region. The proposed method consists of a customized fast-marching level set method which generates an optimal initial pancreas region to solve the problem that the level set method is sensitive to the initial contour location and a modified distance regularized level set method which extracts accurate pancreas. The novelty in our method is the proper selection and combination of level set methods, furthermore an energy-decrement algorithm and an energy-tune algorithm are proposed to reduce the negative impact of bonding force caused by connected tissue whose intensity is similar with pancreas. As a result, our method overcomes the shortages of oversegmentation at weak boundary and can accurately extract pancreas from CT images. The proposed method is compared to other five state-of-the-art medical image segmentation methods based on a CT image dataset which contains abdominal images from 10 patients. The evaluated results demonstrate that our method outperforms other methods by achieving higher accuracy and making less false segmentation in pancreas extraction. PMID:24066016

Early Cretaceous MORB-type basalt and A-type rhyolite in northern Tibet: Evidence for ridge subduction in the Bangong-Nujiang Tethyan Ocean

NASA Astrophysics Data System (ADS)

Fan, Jian-Jun; Li, Cai; Sun, Zhen-Ming; Xu, Wei; Wang, Ming; Xie, Chao-Ming

2018-04-01

New zircon U-Pb ages, major- and trace-element data, and Hf isotopic compositions are presented for bimodal volcanic rocks of the Zhaga Formation (ZF) in the western-middle segment of the Bangong-Nujiang suture zone (BNSZ), northern Tibet. The genesis of these rocks is described, and implications for late-stage evolution of the Bangong-Nujiang Tethyan Ocean (BNTO) are considered. Detailed studies show that the ZF bimodal rocks, which occur as layers within a typical bathyal to abyssal flysch deposit, comprise MORB-type basalt that formed at a mid-ocean ridge, and low-K calc-alkaline A-type rhyolite derived from juvenile crust. The combination of MORB-type basalt, calc-alkaline A-type rhyolite, and bathyal to abyssal flysch deposits in the ZF leads us to propose that they formed as a result of ridge subduction. The A-type ZF rhyolites yield LA-ICP-MS zircon U-Pb ages of 118-112 Ma, indicating formation during the Early Cretaceous. Data from the present study, combined with regional geological data, indicate that the BNTO underwent conversion from ocean opening to ocean closure during the Late Jurassic-Early Cretaceous. The eastern segment of the BNTO closed during this period, while the western and western-middle segments were still at least partially open and active during the Early Cretaceous, accompanied by ridge subduction within the Bangong-Nujiang Tethyan Ocean.