Unsupervised object segmentation with a hybrid graph model (HGM).

PubMed

Liu, Guangcan; Lin, Zhouchen; Yu, Yong; Tang, Xiaoou

2010-05-01

In this work, we address the problem of performing class-specific unsupervised object segmentation, i.e., automatic segmentation without annotated training images. Object segmentation can be regarded as a special data clustering problem where both class-specific information and local texture/color similarities have to be considered. To this end, we propose a hybrid graph model (HGM) that can make effective use of both symmetric and asymmetric relationship among samples. The vertices of a hybrid graph represent the samples and are connected by directed edges and/or undirected ones, which represent the asymmetric and/or symmetric relationship between them, respectively. When applied to object segmentation, vertices are superpixels, the asymmetric relationship is the conditional dependence of occurrence, and the symmetric relationship is the color/texture similarity. By combining the Markov chain formed by the directed subgraph and the minimal cut of the undirected subgraph, the object boundaries can be determined for each image. Using the HGM, we can conveniently achieve simultaneous segmentation and recognition by integrating both top-down and bottom-up information into a unified process. Experiments on 42 object classes (9,415 images in total) show promising results.

Learning of perceptual grouping for object segmentation on RGB-D data☆

PubMed Central

Richtsfeld, Andreas; Mörwald, Thomas; Prankl, Johann; Zillich, Michael; Vincze, Markus

2014-01-01

Object segmentation of unknown objects with arbitrary shape in cluttered scenes is an ambitious goal in computer vision and became a great impulse with the introduction of cheap and powerful RGB-D sensors. We introduce a framework for segmenting RGB-D images where data is processed in a hierarchical fashion. After pre-clustering on pixel level parametric surface patches are estimated. Different relations between patch-pairs are calculated, which we derive from perceptual grouping principles, and support vector machine classification is employed to learn Perceptual Grouping. Finally, we show that object hypotheses generation with Graph-Cut finds a globally optimal solution and prevents wrong grouping. Our framework is able to segment objects, even if they are stacked or jumbled in cluttered scenes. We also tackle the problem of segmenting objects when they are partially occluded. The work is evaluated on publicly available object segmentation databases and also compared with state-of-the-art work of object segmentation. PMID:24478571

Fast Appearance Modeling for Automatic Primary Video Object Segmentation.

PubMed

Yang, Jiong; Price, Brian; Shen, Xiaohui; Lin, Zhe; Yuan, Junsong

2016-02-01

Automatic segmentation of the primary object in a video clip is a challenging problem as there is no prior knowledge of the primary object. Most existing techniques thus adapt an iterative approach for foreground and background appearance modeling, i.e., fix the appearance model while optimizing the segmentation and fix the segmentation while optimizing the appearance model. However, these approaches may rely on good initialization and can be easily trapped in local optimal. In addition, they are usually time consuming for analyzing videos. To address these limitations, we propose a novel and efficient appearance modeling technique for automatic primary video object segmentation in the Markov random field (MRF) framework. It embeds the appearance constraint as auxiliary nodes and edges in the MRF structure, and can optimize both the segmentation and appearance model parameters simultaneously in one graph cut. The extensive experimental evaluations validate the superiority of the proposed approach over the state-of-the-art methods, in both efficiency and effectiveness.

Multi-atlas-based segmentation of the parotid glands of MR images in patients following head-and-neck cancer radiotherapy

NASA Astrophysics Data System (ADS)

Cheng, Guanghui; Yang, Xiaofeng; Wu, Ning; Xu, Zhijian; Zhao, Hongfu; Wang, Yuefeng; Liu, Tian

2013-02-01

Xerostomia (dry mouth), resulting from radiation damage to the parotid glands, is one of the most common and distressing side effects of head-and-neck cancer radiotherapy. Recent MRI studies have demonstrated that the volume reduction of parotid glands is an important indicator for radiation damage and xerostomia. In the clinic, parotid-volume evaluation is exclusively based on physicians' manual contours. However, manual contouring is time-consuming and prone to inter-observer and intra-observer variability. Here, we report a fully automated multi-atlas-based registration method for parotid-gland delineation in 3D head-and-neck MR images. The multi-atlas segmentation utilizes a hybrid deformable image registration to map the target subject to multiple patients' images, applies the transformation to the corresponding segmented parotid glands, and subsequently uses the multiple patient-specific pairs (head-and-neck MR image and transformed parotid-gland mask) to train support vector machine (SVM) to reach consensus to segment the parotid gland of the target subject. This segmentation algorithm was tested with head-and-neck MRIs of 5 patients following radiotherapy for the nasopharyngeal cancer. The average parotid-gland volume overlapped 85% between the automatic segmentations and the physicians' manual contours. In conclusion, we have demonstrated the feasibility of an automatic multi-atlas based segmentation algorithm to segment parotid glands in head-and-neck MR images.

Travelling and splitting of a wave of hedgehog expression involved in spider-head segmentation.

PubMed

Kanayama, Masaki; Akiyama-Oda, Yasuko; Nishimura, Osamu; Tarui, Hiroshi; Agata, Kiyokazu; Oda, Hiroki

2011-10-11

During development segmentation is a process that generates a spatial periodic pattern. Peak splitting of waves of gene expression is a mathematically predicted, simple strategy accounting for this type of process, but it has not been well characterized biologically. Here we show temporally repeated splitting of gene expression into stripes that is associated with head axis growth in the spider Achaearanea embryo. Preceding segmentation, a wave of hedgehog homologue gene expression is observed to travel posteriorly during development stage 6. This stripe, co-expressing an orthodenticle homologue, undergoes two cycles of splitting and shifting accompanied by convergent extension, serving as a generative zone for the head segments. The two orthodenticle and odd-paired homologues are identified as targets of Hedgehog signalling, and evidence suggests that their activities mediate feedback to maintain the head generative zone and to promote stripe splitting in this zone. We propose that the 'stripe-splitting' strategy employs genetic components shared with Drosophila blastoderm subdivision, which are required for participation in an autoregulatory signalling network.

Multi-object segmentation using coupled nonparametric shape and relative pose priors

NASA Astrophysics Data System (ADS)

Uzunbas, Mustafa Gökhan; Soldea, Octavian; Çetin, Müjdat; Ünal, Gözde; Erçil, Aytül; Unay, Devrim; Ekin, Ahmet; Firat, Zeynep

2009-02-01

We present a new method for multi-object segmentation in a maximum a posteriori estimation framework. Our method is motivated by the observation that neighboring or coupling objects in images generate configurations and co-dependencies which could potentially aid in segmentation if properly exploited. Our approach employs coupled shape and inter-shape pose priors that are computed using training images in a nonparametric multi-variate kernel density estimation framework. The coupled shape prior is obtained by estimating the joint shape distribution of multiple objects and the inter-shape pose priors are modeled via standard moments. Based on such statistical models, we formulate an optimization problem for segmentation, which we solve by an algorithm based on active contours. Our technique provides significant improvements in the segmentation of weakly contrasted objects in a number of applications. In particular for medical image analysis, we use our method to extract brain Basal Ganglia structures, which are members of a complex multi-object system posing a challenging segmentation problem. We also apply our technique to the problem of handwritten character segmentation. Finally, we use our method to segment cars in urban scenes.

SU-E-J-224: Multimodality Segmentation of Head and Neck Tumors

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

Aristophanous, M; Yang, J; Beadle, B

2014-06-01

Purpose: Develop an algorithm that is able to automatically segment tumor volume in Head and Neck cancer by integrating information from CT, PET and MR imaging simultaneously. Methods: Twenty three patients that were recruited under an adaptive radiotherapy protocol had MR, CT and PET/CT scans within 2 months prior to start of radiotherapy. The patients had unresectable disease and were treated either with chemoradiotherapy or radiation therapy alone. Using the Velocity software, the PET/CT and MR (T1 weighted+contrast) scans were registered to the planning CT using deformable and rigid registration respectively. The PET and MR images were then resampled accordingmore » to the registration to match the planning CT. The resampled images, together with the planning CT, were fed into a multi-channel segmentation algorithm, which is based on Gaussian mixture models and solved with the expectation-maximization algorithm and Markov random fields. A rectangular region of interest (ROI) was manually placed to identify the tumor area and facilitate the segmentation process. The auto-segmented tumor contours were compared with the gross tumor volume (GTV) manually defined by the physician. The volume difference and Dice similarity coefficient (DSC) between the manual and autosegmented GTV contours were calculated as the quantitative evaluation metrics. Results: The multimodality segmentation algorithm was applied to all 23 patients. The volumes of the auto-segmented GTV ranged from 18.4cc to 32.8cc. The average (range) volume difference between the manual and auto-segmented GTV was −42% (−32.8%–63.8%). The average DSC value was 0.62, ranging from 0.39 to 0.78. Conclusion: An algorithm for the automated definition of tumor volume using multiple imaging modalities simultaneously was successfully developed and implemented for Head and Neck cancer. This development along with more accurate registration algorithms can aid physicians in the efforts to interpret the

TH-CD-206-05: Machine-Learning Based Segmentation of Organs at Risks for Head and Neck Radiotherapy Planning

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

Ibragimov, B; Pernus, F; Strojan, P

Purpose: Accurate and efficient delineation of tumor target and organs-at-risks is essential for the success of radiotherapy. In reality, despite of decades of intense research efforts, auto-segmentation has not yet become clinical practice. In this study, we present, for the first time, a deep learning-based classification algorithm for autonomous segmentation in head and neck (HaN) treatment planning. Methods: Fifteen HN datasets of CT, MR and PET images with manual annotation of organs-at-risk (OARs) including spinal cord, brainstem, optic nerves, chiasm, eyes, mandible, tongue, parotid glands were collected and saved in a library of plans. We also have ten super-resolution MRmore » images of the tongue area, where the genioglossus and inferior longitudinalis tongue muscles are defined as organs of interest. We applied the concepts of random forest- and deep learning-based object classification for automated image annotation with the aim of using machine learning to facilitate head and neck radiotherapy planning process. In this new paradigm of segmentation, random forests were used for landmark-assisted segmentation of super-resolution MR images. Alternatively to auto-segmentation with random forest-based landmark detection, deep convolutional neural networks were developed for voxel-wise segmentation of OARs in single and multi-modal images. The network consisted of three pairs of convolution and pooing layer, one RuLU layer and a softmax layer. Results: We present a comprehensive study on using machine learning concepts for auto-segmentation of OARs and tongue muscles for the HaN radiotherapy planning. An accuracy of 81.8% in terms of Dice coefficient was achieved for segmentation of genioglossus and inferior longitudinalis tongue muscles. Preliminary results of OARs regimentation also indicate that deep-learning afforded an unprecedented opportunities to improve the accuracy and robustness of radiotherapy planning. Conclusion: A novel machine learning

Segmentation of Object Outlines into Parts: A Large-Scale Integrative Study

ERIC Educational Resources Information Center

De Winter, Joeri; Wagemans, Johan

2006-01-01

In this study, a large number of observers (N=201) were asked to segment a collection of outlines derived from line drawings of everyday objects (N=88). This data set was then used as a benchmark to evaluate current models of object segmentation. All of the previously proposed rules of segmentation were found supported in our results. For example,…

Clinical evaluation of multi-atlas based segmentation of lymph node regions in head and neck and prostate cancer patients.

PubMed

Sjöberg, Carl; Lundmark, Martin; Granberg, Christoffer; Johansson, Silvia; Ahnesjö, Anders; Montelius, Anders

2013-10-03

Semi-automated segmentation using deformable registration of selected atlas cases consisting of expert segmented patient images has been proposed to facilitate the delineation of lymph node regions for three-dimensional conformal and intensity-modulated radiotherapy planning of head and neck and prostate tumours. Our aim is to investigate if fusion of multiple atlases will lead to clinical workload reductions and more accurate segmentation proposals compared to the use of a single atlas segmentation, due to a more complete representation of the anatomical variations. Atlases for lymph node regions were constructed using 11 head and neck patients and 15 prostate patients based on published recommendations for segmentations. A commercial registration software (Velocity AI) was used to create individual segmentations through deformable registration. Ten head and neck patients, and ten prostate patients, all different from the atlas patients, were randomly chosen for the study from retrospective data. Each patient was first delineated three times, (a) manually by a radiation oncologist, (b) automatically using a single atlas segmentation proposal from a chosen atlas and (c) automatically by fusing the atlas proposals from all cases in the database using the probabilistic weighting fusion algorithm. In a subsequent step a radiation oncologist corrected the segmentation proposals achieved from step (b) and (c) without using the result from method (a) as reference. The time spent for editing the segmentations was recorded separately for each method and for each individual structure. Finally, the Dice Similarity Coefficient and the volume of the structures were used to evaluate the similarity between the structures delineated with the different methods. For the single atlas method, the time reduction compared to manual segmentation was 29% and 23% for head and neck and pelvis lymph nodes, respectively, while editing the fused atlas proposal resulted in time reductions of

Fuzzy object models for newborn brain MR image segmentation

NASA Astrophysics Data System (ADS)

Kobashi, Syoji; Udupa, Jayaram K.

2013-03-01

Newborn brain MR image segmentation is a challenging problem because of variety of size, shape and MR signal although it is the fundamental study for quantitative radiology in brain MR images. Because of the large difference between the adult brain and the newborn brain, it is difficult to directly apply the conventional methods for the newborn brain. Inspired by the original fuzzy object model introduced by Udupa et al. at SPIE Medical Imaging 2011, called fuzzy shape object model (FSOM) here, this paper introduces fuzzy intensity object model (FIOM), and proposes a new image segmentation method which combines the FSOM and FIOM into fuzzy connected (FC) image segmentation. The fuzzy object models are built from training datasets in which the cerebral parenchyma is delineated by experts. After registering FSOM with the evaluating image, the proposed method roughly recognizes the cerebral parenchyma region based on a prior knowledge of location, shape, and the MR signal given by the registered FSOM and FIOM. Then, FC image segmentation delineates the cerebral parenchyma using the fuzzy object models. The proposed method has been evaluated using 9 newborn brain MR images using the leave-one-out strategy. The revised age was between -1 and 2 months. Quantitative evaluation using false positive volume fraction (FPVF) and false negative volume fraction (FNVF) has been conducted. Using the evaluation data, a FPVF of 0.75% and FNVF of 3.75% were achieved. More data collection and testing are underway.

Concealed object segmentation and three-dimensional localization with passive millimeter-wave imaging

NASA Astrophysics Data System (ADS)

Yeom, Seokwon

2013-05-01

Millimeter waves imaging draws increasing attention in security applications for weapon detection under clothing. In this paper, concealed object segmentation and three-dimensional localization schemes are reviewed. A concealed object is segmented by the k-means algorithm. A feature-based stereo-matching method estimates the longitudinal distance of the concealed object. The distance is estimated by the discrepancy between the corresponding centers of the segmented objects. Experimental results are provided with the analysis of the depth resolution.

Segmenting the Femoral Head and Acetabulum in the Hip Joint Automatically Using a Multi-Step Scheme

NASA Astrophysics Data System (ADS)

Wang, Ji; Cheng, Yuanzhi; Fu, Yili; Zhou, Shengjun; Tamura, Shinichi

We describe a multi-step approach for automatic segmentation of the femoral head and the acetabulum in the hip joint from three dimensional (3D) CT images. Our segmentation method consists of the following steps: 1) construction of the valley-emphasized image by subtracting valleys from the original images; 2) initial segmentation of the bone regions by using conventional techniques including the initial threshold and binary morphological operations from the valley-emphasized image; 3) further segmentation of the bone regions by using the iterative adaptive classification with the initial segmentation result; 4) detection of the rough bone boundaries based on the segmented bone regions; 5) 3D reconstruction of the bone surface using the rough bone boundaries obtained in step 4) by a network of triangles; 6) correction of all vertices of the 3D bone surface based on the normal direction of vertices; 7) adjustment of the bone surface based on the corrected vertices. We evaluated our approach on 35 CT patient data sets. Our experimental results show that our segmentation algorithm is more accurate and robust against noise than other conventional approaches for automatic segmentation of the femoral head and the acetabulum. Average root-mean-square (RMS) distance from manual reference segmentations created by experienced users was approximately 0.68mm (in-plane resolution of the CT data).

Atlas-based automatic segmentation of head and neck organs at risk and nodal target volumes: a clinical validation.

PubMed

Daisne, Jean-François; Blumhofer, Andreas

2013-06-26

Intensity modulated radiotherapy for head and neck cancer necessitates accurate definition of organs at risk (OAR) and clinical target volumes (CTV). This crucial step is time consuming and prone to inter- and intra-observer variations. Automatic segmentation by atlas deformable registration may help to reduce time and variations. We aim to test a new commercial atlas algorithm for automatic segmentation of OAR and CTV in both ideal and clinical conditions. The updated Brainlab automatic head and neck atlas segmentation was tested on 20 patients: 10 cN0-stages (ideal population) and 10 unselected N-stages (clinical population). Following manual delineation of OAR and CTV, automatic segmentation of the same set of structures was performed and afterwards manually corrected. Dice Similarity Coefficient (DSC), Average Surface Distance (ASD) and Maximal Surface Distance (MSD) were calculated for "manual to automatic" and "manual to corrected" volumes comparisons. In both groups, automatic segmentation saved about 40% of the corresponding manual segmentation time. This effect was more pronounced for OAR than for CTV. The edition of the automatically obtained contours significantly improved DSC, ASD and MSD. Large distortions of normal anatomy or lack of iodine contrast were the limiting factors. The updated Brainlab atlas-based automatic segmentation tool for head and neck Cancer patients is timesaving but still necessitates review and corrections by an expert.

Object Segmentation Methods for Online Model Acquisition to Guide Robotic Grasping

NASA Astrophysics Data System (ADS)

Ignakov, Dmitri

A vision system is an integral component of many autonomous robots. It enables the robot to perform essential tasks such as mapping, localization, or path planning. A vision system also assists with guiding the robot's grasping and manipulation tasks. As an increased demand is placed on service robots to operate in uncontrolled environments, advanced vision systems must be created that can function effectively in visually complex and cluttered settings. This thesis presents the development of segmentation algorithms to assist in online model acquisition for guiding robotic manipulation tasks. Specifically, the focus is placed on localizing door handles to assist in robotic door opening, and on acquiring partial object models to guide robotic grasping. First, a method for localizing a door handle of unknown geometry based on a proposed 3D segmentation method is presented. Following segmentation, localization is performed by fitting a simple box model to the segmented handle. The proposed method functions without requiring assumptions about the appearance of the handle or the door, and without a geometric model of the handle. Next, an object segmentation algorithm is developed, which combines multiple appearance (intensity and texture) and geometric (depth and curvature) cues. The algorithm is able to segment objects without utilizing any a priori appearance or geometric information in visually complex and cluttered environments. The segmentation method is based on the Conditional Random Fields (CRF) framework, and the graph cuts energy minimization technique. A simple and efficient method for initializing the proposed algorithm which overcomes graph cuts' reliance on user interaction is also developed. Finally, an improved segmentation algorithm is developed which incorporates a distance metric learning (DML) step as a means of weighing various appearance and geometric segmentation cues, allowing the method to better adapt to the available data. The improved method

Unsupervised Segmentation of Head Tissues from Multi-modal MR Images for EEG Source Localization.

PubMed

Mahmood, Qaiser; Chodorowski, Artur; Mehnert, Andrew; Gellermann, Johanna; Persson, Mikael

2015-08-01

In this paper, we present and evaluate an automatic unsupervised segmentation method, hierarchical segmentation approach (HSA)-Bayesian-based adaptive mean shift (BAMS), for use in the construction of a patient-specific head conductivity model for electroencephalography (EEG) source localization. It is based on a HSA and BAMS for segmenting the tissues from multi-modal magnetic resonance (MR) head images. The evaluation of the proposed method was done both directly in terms of segmentation accuracy and indirectly in terms of source localization accuracy. The direct evaluation was performed relative to a commonly used reference method brain extraction tool (BET)-FMRIB's automated segmentation tool (FAST) and four variants of the HSA using both synthetic data and real data from ten subjects. The synthetic data includes multiple realizations of four different noise levels and several realizations of typical noise with a 20% bias field level. The Dice index and Hausdorff distance were used to measure the segmentation accuracy. The indirect evaluation was performed relative to the reference method BET-FAST using synthetic two-dimensional (2D) multimodal magnetic resonance (MR) data with 3% noise and synthetic EEG (generated for a prescribed source). The source localization accuracy was determined in terms of localization error and relative error of potential. The experimental results demonstrate the efficacy of HSA-BAMS, its robustness to noise and the bias field, and that it provides better segmentation accuracy than the reference method and variants of the HSA. They also show that it leads to a more accurate localization accuracy than the commonly used reference method and suggest that it has potential as a surrogate for expert manual segmentation for the EEG source localization problem.

Interleaved 3D-CNNs for joint segmentation of small-volume structures in head and neck CT images.

PubMed

Ren, Xuhua; Xiang, Lei; Nie, Dong; Shao, Yeqin; Zhang, Huan; Shen, Dinggang; Wang, Qian

2018-05-01

Accurate 3D image segmentation is a crucial step in radiation therapy planning of head and neck tumors. These segmentation results are currently obtained by manual outlining of tissues, which is a tedious and time-consuming procedure. Automatic segmentation provides an alternative solution, which, however, is often difficult for small tissues (i.e., chiasm and optic nerves in head and neck CT images) because of their small volumes and highly diverse appearance/shape information. In this work, we propose to interleave multiple 3D Convolutional Neural Networks (3D-CNNs) to attain automatic segmentation of small tissues in head and neck CT images. A 3D-CNN was designed to segment each structure of interest. To make full use of the image appearance information, multiscale patches are extracted to describe the center voxel under consideration and then input to the CNN architecture. Next, as neighboring tissues are often highly related in the physiological and anatomical perspectives, we interleave the CNNs designated for the individual tissues. In this way, the tentative segmentation result of a specific tissue can contribute to refine the segmentations of other neighboring tissues. Finally, as more CNNs are interleaved and cascaded, a complex network of CNNs can be derived, such that all tissues can be jointly segmented and iteratively refined. Our method was validated on a set of 48 CT images, obtained from the Medical Image Computing and Computer Assisted Intervention (MICCAI) Challenge 2015. The Dice coefficient (DC) and the 95% Hausdorff Distance (95HD) are computed to measure the accuracy of the segmentation results. The proposed method achieves higher segmentation accuracy (with the average DC: 0.58 ± 0.17 for optic chiasm, and 0.71 ± 0.08 for optic nerve; 95HD: 2.81 ± 1.56 mm for optic chiasm, and 2.23 ± 0.90 mm for optic nerve) than the MICCAI challenge winner (with the average DC: 0.38 for optic chiasm, and 0.68 for optic nerve; 95HD: 3.48 for

A new user-assisted segmentation and tracking technique for an object-based video editing system

NASA Astrophysics Data System (ADS)

Yu, Hong Y.; Hong, Sung-Hoon; Lee, Mike M.; Choi, Jae-Gark

2004-03-01

This paper presents a semi-automatic segmentation method which can be used to generate video object plane (VOP) for object based coding scheme and multimedia authoring environment. Semi-automatic segmentation can be considered as a user-assisted segmentation technique. A user can initially mark objects of interest around the object boundaries and then the user-guided and selected objects are continuously separated from the unselected areas through time evolution in the image sequences. The proposed segmentation method consists of two processing steps: partially manual intra-frame segmentation and fully automatic inter-frame segmentation. The intra-frame segmentation incorporates user-assistance to define the meaningful complete visual object of interest to be segmentation and decides precise object boundary. The inter-frame segmentation involves boundary and region tracking to obtain temporal coherence of moving object based on the object boundary information of previous frame. The proposed method shows stable efficient results that could be suitable for many digital video applications such as multimedia contents authoring, content based coding and indexing. Based on these results, we have developed objects based video editing system with several convenient editing functions.

Graphical user interface to optimize image contrast parameters used in object segmentation - biomed 2009.

PubMed

Anderson, Jeffrey R; Barrett, Steven F

2009-01-01

Image segmentation is the process of isolating distinct objects within an image. Computer algorithms have been developed to aid in the process of object segmentation, but a completely autonomous segmentation algorithm has yet to be developed [1]. This is because computers do not have the capability to understand images and recognize complex objects within the image. However, computer segmentation methods [2], requiring user input, have been developed to quickly segment objects in serial sectioned images, such as magnetic resonance images (MRI) and confocal laser scanning microscope (CLSM) images. In these cases, the segmentation process becomes a powerful tool in visualizing the 3D nature of an object. The user input is an important part of improving the performance of many segmentation methods. A double threshold segmentation method has been investigated [3] to separate objects in gray scaled images, where the gray level of the object is among the gray levels of the background. In order to best determine the threshold values for this segmentation method the image must be manipulated for optimal contrast. The same is true of other segmentation and edge detection methods as well. Typically, the better the image contrast, the better the segmentation results. This paper describes a graphical user interface (GUI) that allows the user to easily change image contrast parameters that will optimize the performance of subsequent object segmentation. This approach makes use of the fact that the human brain is extremely effective in object recognition and understanding. The GUI provides the user with the ability to define the gray scale range of the object of interest. These lower and upper bounds of this range are used in a histogram stretching process to improve image contrast. Also, the user can interactively modify the gamma correction factor that provides a non-linear distribution of gray scale values, while observing the corresponding changes to the image. This

The temporal dynamics of heading perception in the presence of moving objects

PubMed Central

Fajen, Brett R.

2015-01-01

Many forms of locomotion rely on the ability to accurately perceive one's direction of locomotion (i.e., heading) based on optic flow. Although accurate in rigid environments, heading judgments may be biased when independently moving objects are present. The aim of this study was to systematically investigate the conditions in which moving objects influence heading perception, with a focus on the temporal dynamics and the mechanisms underlying this bias. Subjects viewed stimuli simulating linear self-motion in the presence of a moving object and judged their direction of heading. Experiments 1 and 2 revealed that heading perception is biased when the object crosses or almost crosses the observer's future path toward the end of the trial, but not when the object crosses earlier in the trial. Nonetheless, heading perception is not based entirely on the instantaneous optic flow toward the end of the trial. This was demonstrated in Experiment 3 by varying the portion of the earlier part of the trial leading up to the last frame that was presented to subjects. When the stimulus duration was long enough to include the part of the trial before the moving object crossed the observer's path, heading judgments were less biased. The findings suggest that heading perception is affected by the temporal evolution of optic flow. The time course of dorsal medial superior temporal area (MSTd) neuron responses may play a crucial role in perceiving heading in the presence of moving objects, a property not captured by many existing models. PMID:26510765

Objective measurements to evaluate glottal space segmentation from laryngeal images.

PubMed

Gutiérrez-Arriola, J M; Osma-Ruiz, V; Sáenz-Lechón, N; Godino-Llorente, J I; Fraile, R; Arias-Londoño, J D

2012-01-01

Objective evaluation of the results of medical image segmentation is a known problem. Applied to the task of automatically detecting the glottal area from laryngeal images, this paper proposes a new objective measurement to evaluate the quality of a segmentation algorithm by comparing with the results given by a human expert. The new figure of merit is called Area Index, and its effectiveness is compared with one of the most used figures of merit found in the literature: the Pratt Index. Results over 110 laryngeal images presented high correlations between both indexes, demonstrating that the proposed measure is comparable to the Pratt Index and it is a good indicator of the segmentation quality.

Real-time object detection and semantic segmentation for autonomous driving

NASA Astrophysics Data System (ADS)

Li, Baojun; Liu, Shun; Xu, Weichao; Qiu, Wei

2018-02-01

In this paper, we proposed a Highly Coupled Network (HCNet) for joint objection detection and semantic segmentation. It follows that our method is faster and performs better than the previous approaches whose decoder networks of different tasks are independent. Besides, we present multi-scale loss architecture to learn better representation for different scale objects, but without extra time in the inference phase. Experiment results show that our method achieves state-of-the-art results on the KITTI datasets. Moreover, it can run at 35 FPS on a GPU and thus is a practical solution to object detection and semantic segmentation for autonomous driving.

Biophysics of object segmentation in a collision-detecting neuron

PubMed Central

Dewell, Richard Burkett

2018-01-01

Collision avoidance is critical for survival, including in humans, and many species possess visual neurons exquisitely sensitive to objects approaching on a collision course. Here, we demonstrate that a collision-detecting neuron can detect the spatial coherence of a simulated impending object, thereby carrying out a computation akin to object segmentation critical for proper escape behavior. At the cellular level, object segmentation relies on a precise selection of the spatiotemporal pattern of synaptic inputs by dendritic membrane potential-activated channels. One channel type linked to dendritic computations in many neural systems, the hyperpolarization-activated cation channel, HCN, plays a central role in this computation. Pharmacological block of HCN channels abolishes the neuron's spatial selectivity and impairs the generation of visually guided escape behaviors, making it directly relevant to survival. Additionally, our results suggest that the interaction of HCN and inactivating K+ channels within active dendrites produces neuronal and behavioral object specificity by discriminating between complex spatiotemporal synaptic activation patterns. PMID:29667927

Semiautomated segmentation of head and neck cancers in 18F-FDG PET scans: A just-enough-interaction approach.

PubMed

Beichel, Reinhard R; Van Tol, Markus; Ulrich, Ethan J; Bauer, Christian; Chang, Tangel; Plichta, Kristin A; Smith, Brian J; Sunderland, John J; Graham, Michael M; Sonka, Milan; Buatti, John M

2016-06-01

The purpose of this work was to develop, validate, and compare a highly computer-aided method for the segmentation of hot lesions in head and neck 18F-FDG PET scans. A semiautomated segmentation method was developed, which transforms the segmentation problem into a graph-based optimization problem. For this purpose, a graph structure around a user-provided approximate lesion centerpoint is constructed and a suitable cost function is derived based on local image statistics. To handle frequently occurring situations that are ambiguous (e.g., lesions adjacent to each other versus lesion with inhomogeneous uptake), several segmentation modes are introduced that adapt the behavior of the base algorithm accordingly. In addition, the authors present approaches for the efficient interactive local and global refinement of initial segmentations that are based on the "just-enough-interaction" principle. For method validation, 60 PET/CT scans from 59 different subjects with 230 head and neck lesions were utilized. All patients had squamous cell carcinoma of the head and neck. A detailed comparison with the current clinically relevant standard manual segmentation approach was performed based on 2760 segmentations produced by three experts. Segmentation accuracy measured by the Dice coefficient of the proposed semiautomated and standard manual segmentation approach was 0.766 and 0.764, respectively. This difference was not statistically significant (p = 0.2145). However, the intra- and interoperator standard deviations were significantly lower for the semiautomated method. In addition, the proposed method was found to be significantly faster and resulted in significantly higher intra- and interoperator segmentation agreement when compared to the manual segmentation approach. Lack of consistency in tumor definition is a critical barrier for radiation treatment targeting as well as for response assessment in clinical trials and in clinical oncology decision-making. The properties

Semiautomated segmentation of head and neck cancers in 18F-FDG PET scans: A just-enough-interaction approach

PubMed Central

Beichel, Reinhard R.; Van Tol, Markus; Ulrich, Ethan J.; Bauer, Christian; Chang, Tangel; Plichta, Kristin A.; Smith, Brian J.; Sunderland, John J.; Graham, Michael M.; Sonka, Milan; Buatti, John M.

2016-01-01

Purpose: The purpose of this work was to develop, validate, and compare a highly computer-aided method for the segmentation of hot lesions in head and neck 18F-FDG PET scans. Methods: A semiautomated segmentation method was developed, which transforms the segmentation problem into a graph-based optimization problem. For this purpose, a graph structure around a user-provided approximate lesion centerpoint is constructed and a suitable cost function is derived based on local image statistics. To handle frequently occurring situations that are ambiguous (e.g., lesions adjacent to each other versus lesion with inhomogeneous uptake), several segmentation modes are introduced that adapt the behavior of the base algorithm accordingly. In addition, the authors present approaches for the efficient interactive local and global refinement of initial segmentations that are based on the “just-enough-interaction” principle. For method validation, 60 PET/CT scans from 59 different subjects with 230 head and neck lesions were utilized. All patients had squamous cell carcinoma of the head and neck. A detailed comparison with the current clinically relevant standard manual segmentation approach was performed based on 2760 segmentations produced by three experts. Results: Segmentation accuracy measured by the Dice coefficient of the proposed semiautomated and standard manual segmentation approach was 0.766 and 0.764, respectively. This difference was not statistically significant (p = 0.2145). However, the intra- and interoperator standard deviations were significantly lower for the semiautomated method. In addition, the proposed method was found to be significantly faster and resulted in significantly higher intra- and interoperator segmentation agreement when compared to the manual segmentation approach. Conclusions: Lack of consistency in tumor definition is a critical barrier for radiation treatment targeting as well as for response assessment in clinical trials and in

Semiautomated segmentation of head and neck cancers in 18F-FDG PET scans: A just-enough-interaction approach

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

Beichel, Reinhard R., E-mail: reinhard-beichel@uiowa.edu; Iowa Institute for Biomedical Imaging, University of Iowa, Iowa City, Iowa 52242; Department of Internal Medicine, University of Iowa, Iowa City, Iowa 52242

Purpose: The purpose of this work was to develop, validate, and compare a highly computer-aided method for the segmentation of hot lesions in head and neck 18F-FDG PET scans. Methods: A semiautomated segmentation method was developed, which transforms the segmentation problem into a graph-based optimization problem. For this purpose, a graph structure around a user-provided approximate lesion centerpoint is constructed and a suitable cost function is derived based on local image statistics. To handle frequently occurring situations that are ambiguous (e.g., lesions adjacent to each other versus lesion with inhomogeneous uptake), several segmentation modes are introduced that adapt the behaviormore » of the base algorithm accordingly. In addition, the authors present approaches for the efficient interactive local and global refinement of initial segmentations that are based on the “just-enough-interaction” principle. For method validation, 60 PET/CT scans from 59 different subjects with 230 head and neck lesions were utilized. All patients had squamous cell carcinoma of the head and neck. A detailed comparison with the current clinically relevant standard manual segmentation approach was performed based on 2760 segmentations produced by three experts. Results: Segmentation accuracy measured by the Dice coefficient of the proposed semiautomated and standard manual segmentation approach was 0.766 and 0.764, respectively. This difference was not statistically significant (p = 0.2145). However, the intra- and interoperator standard deviations were significantly lower for the semiautomated method. In addition, the proposed method was found to be significantly faster and resulted in significantly higher intra- and interoperator segmentation agreement when compared to the manual segmentation approach. Conclusions: Lack of consistency in tumor definition is a critical barrier for radiation treatment targeting as well as for response assessment in clinical trials and

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.

Structure preserving clustering-object tracking via subgroup motion pattern segmentation

NASA Astrophysics Data System (ADS)

Fan, Zheyi; Zhu, Yixuan; Jiang, Jiao; Weng, Shuqin; Liu, Zhiwen

2018-01-01

Tracking clustering objects with similar appearances simultaneously in collective scenes is a challenging task in the field of collective motion analysis. Recent work on clustering-object tracking often suffers from poor tracking accuracy and terrible real-time performance due to the neglect or the misjudgment of the motion differences among objects. To address this problem, we propose a subgroup motion pattern segmentation framework based on a multilayer clustering structure and establish spatial constraints only among objects in the same subgroup, which entails having consistent motion direction and close spatial position. In addition, the subgroup segmentation results are updated dynamically because crowd motion patterns are changeable and affected by objects' destinations and scene structures. The spatial structure information combined with the appearance similarity information is used in the structure preserving object tracking framework to track objects. Extensive experiments conducted on several datasets containing multiple real-world crowd scenes validate the accuracy and the robustness of the presented algorithm for tracking objects in collective scenes.

Multi-object segmentation framework using deformable models for medical imaging analysis.

PubMed

Namías, Rafael; D'Amato, Juan Pablo; Del Fresno, Mariana; Vénere, Marcelo; Pirró, Nicola; Bellemare, Marc-Emmanuel

2016-08-01

Segmenting structures of interest in medical images is an important step in different tasks such as visualization, quantitative analysis, simulation, and image-guided surgery, among several other clinical applications. Numerous segmentation methods have been developed in the past three decades for extraction of anatomical or functional structures on medical imaging. Deformable models, which include the active contour models or snakes, are among the most popular methods for image segmentation combining several desirable features such as inherent connectivity and smoothness. Even though different approaches have been proposed and significant work has been dedicated to the improvement of such algorithms, there are still challenging research directions as the simultaneous extraction of multiple objects and the integration of individual techniques. This paper presents a novel open-source framework called deformable model array (DMA) for the segmentation of multiple and complex structures of interest in different imaging modalities. While most active contour algorithms can extract one region at a time, DMA allows integrating several deformable models to deal with multiple segmentation scenarios. Moreover, it is possible to consider any existing explicit deformable model formulation and even to incorporate new active contour methods, allowing to select a suitable combination in different conditions. The framework also introduces a control module that coordinates the cooperative evolution of the snakes and is able to solve interaction issues toward the segmentation goal. Thus, DMA can implement complex object and multi-object segmentations in both 2D and 3D using the contextual information derived from the model interaction. These are important features for several medical image analysis tasks in which different but related objects need to be simultaneously extracted. Experimental results on both computed tomography and magnetic resonance imaging show that the proposed

Groups of adjacent contour segments for object detection.

PubMed

Ferrari, V; Fevrier, L; Jurie, F; Schmid, C

2008-01-01

We present a family of scale-invariant local shape features formed by chains of k connected, roughly straight contour segments (kAS), and their use for object class detection. kAS are able to cleanly encode pure fragments of an object boundary, without including nearby clutter. Moreover, they offer an attractive compromise between information content and repeatability, and encompass a wide variety of local shape structures. We also define a translation and scale invariant descriptor encoding the geometric configuration of the segments within a kAS, making kAS easy to reuse in other frameworks, for example as a replacement or addition to interest points. Software for detecting and describing kAS is released on lear.inrialpes.fr/software. We demonstrate the high performance of kAS within a simple but powerful sliding-window object detection scheme. Through extensive evaluations, involving eight diverse object classes and more than 1400 images, we 1) study the evolution of performance as the degree of feature complexity k varies and determine the best degree; 2) show that kAS substantially outperform interest points for detecting shape-based classes; 3) compare our object detector to the recent, state-of-the-art system by Dalal and Triggs [4].

A wavelet-based Bayesian framework for 3D object segmentation in microscopy

NASA Astrophysics Data System (ADS)

Pan, Kangyu; Corrigan, David; Hillebrand, Jens; Ramaswami, Mani; Kokaram, Anil

2012-03-01

In confocal microscopy, target objects are labeled with fluorescent markers in the living specimen, and usually appear with irregular brightness in the observed images. Also, due to the existence of out-of-focus objects in the image, the segmentation of 3-D objects in the stack of image slices captured at different depth levels of the specimen is still heavily relied on manual analysis. In this paper, a novel Bayesian model is proposed for segmenting 3-D synaptic objects from given image stack. In order to solve the irregular brightness and out-offocus problems, the segmentation model employs a likelihood using the luminance-invariant 'wavelet features' of image objects in the dual-tree complex wavelet domain as well as a likelihood based on the vertical intensity profile of the image stack in 3-D. Furthermore, a smoothness 'frame' prior based on the a priori knowledge of the connections of the synapses is introduced to the model for enhancing the connectivity of the synapses. As a result, our model can successfully segment the in-focus target synaptic object from a 3D image stack with irregular brightness.

A multimodality segmentation framework for automatic target delineation in head and neck radiotherapy

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

Yang, Jinzhong; Aristophanous, Michalis, E-mail: MAristophanous@mdanderson.org; Beadle, Beth M.

2015-09-15

Purpose: To develop an automatic segmentation algorithm integrating imaging information from computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) to delineate target volume in head and neck cancer radiotherapy. Methods: Eleven patients with unresectable disease at the tonsil or base of tongue who underwent MRI, CT, and PET/CT within two months before the start of radiotherapy or chemoradiotherapy were recruited for the study. For each patient, PET/CT and T1-weighted contrast MRI scans were first registered to the planning CT using deformable and rigid registration, respectively, to resample the PET and magnetic resonance (MR) images to themore » planning CT space. A binary mask was manually defined to identify the tumor area. The resampled PET and MR images, the planning CT image, and the binary mask were fed into the automatic segmentation algorithm for target delineation. The algorithm was based on a multichannel Gaussian mixture model and solved using an expectation–maximization algorithm with Markov random fields. To evaluate the algorithm, we compared the multichannel autosegmentation with an autosegmentation method using only PET images. The physician-defined gross tumor volume (GTV) was used as the “ground truth” for quantitative evaluation. Results: The median multichannel segmented GTV of the primary tumor was 15.7 cm{sup 3} (range, 6.6–44.3 cm{sup 3}), while the PET segmented GTV was 10.2 cm{sup 3} (range, 2.8–45.1 cm{sup 3}). The median physician-defined GTV was 22.1 cm{sup 3} (range, 4.2–38.4 cm{sup 3}). The median difference between the multichannel segmented and physician-defined GTVs was −10.7%, not showing a statistically significant difference (p-value = 0.43). However, the median difference between the PET segmented and physician-defined GTVs was −19.2%, showing a statistically significant difference (p-value =0.0037). The median Dice similarity coefficient between the multichannel

A multimodality segmentation framework for automatic target delineation in head and neck radiotherapy.

PubMed

Yang, Jinzhong; Beadle, Beth M; Garden, Adam S; Schwartz, David L; Aristophanous, Michalis

2015-09-01

To develop an automatic segmentation algorithm integrating imaging information from computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) to delineate target volume in head and neck cancer radiotherapy. Eleven patients with unresectable disease at the tonsil or base of tongue who underwent MRI, CT, and PET/CT within two months before the start of radiotherapy or chemoradiotherapy were recruited for the study. For each patient, PET/CT and T1-weighted contrast MRI scans were first registered to the planning CT using deformable and rigid registration, respectively, to resample the PET and magnetic resonance (MR) images to the planning CT space. A binary mask was manually defined to identify the tumor area. The resampled PET and MR images, the planning CT image, and the binary mask were fed into the automatic segmentation algorithm for target delineation. The algorithm was based on a multichannel Gaussian mixture model and solved using an expectation-maximization algorithm with Markov random fields. To evaluate the algorithm, we compared the multichannel autosegmentation with an autosegmentation method using only PET images. The physician-defined gross tumor volume (GTV) was used as the "ground truth" for quantitative evaluation. The median multichannel segmented GTV of the primary tumor was 15.7 cm(3) (range, 6.6-44.3 cm(3)), while the PET segmented GTV was 10.2 cm(3) (range, 2.8-45.1 cm(3)). The median physician-defined GTV was 22.1 cm(3) (range, 4.2-38.4 cm(3)). The median difference between the multichannel segmented and physician-defined GTVs was -10.7%, not showing a statistically significant difference (p-value = 0.43). However, the median difference between the PET segmented and physician-defined GTVs was -19.2%, showing a statistically significant difference (p-value =0.0037). The median Dice similarity coefficient between the multichannel segmented and physician-defined GTVs was 0.75 (range, 0.55-0.84), and the

Best Merge Region Growing Segmentation with Integrated Non-Adjacent Region Object Aggregation

NASA Technical Reports Server (NTRS)

Tilton, James C.; Tarabalka, Yuliya; Montesano, Paul M.; Gofman, Emanuel

2012-01-01

Best merge region growing normally produces segmentations with closed connected region objects. Recognizing that spectrally similar objects often appear in spatially separate locations, we present an approach for tightly integrating best merge region growing with non-adjacent region object aggregation, which we call Hierarchical Segmentation or HSeg. However, the original implementation of non-adjacent region object aggregation in HSeg required excessive computing time even for moderately sized images because of the required intercomparison of each region with all other regions. This problem was previously addressed by a recursive approximation of HSeg, called RHSeg. In this paper we introduce a refined implementation of non-adjacent region object aggregation in HSeg that reduces the computational requirements of HSeg without resorting to the recursive approximation. In this refinement, HSeg s region inter-comparisons among non-adjacent regions are limited to regions of a dynamically determined minimum size. We show that this refined version of HSeg can process moderately sized images in about the same amount of time as RHSeg incorporating the original HSeg. Nonetheless, RHSeg is still required for processing very large images due to its lower computer memory requirements and amenability to parallel processing. We then note a limitation of RHSeg with the original HSeg for high spatial resolution images, and show how incorporating the refined HSeg into RHSeg overcomes this limitation. The quality of the image segmentations produced by the refined HSeg is then compared with other available best merge segmentation approaches. Finally, we comment on the unique nature of the hierarchical segmentations produced by HSeg.

A link-segment model of upright human posture for analysis of head-trunk coordination

NASA Technical Reports Server (NTRS)

Nicholas, S. C.; Doxey-Gasway, D. D.; Paloski, W. H.

1998-01-01

Sensory-motor control of upright human posture may be organized in a top-down fashion such that certain head-trunk coordination strategies are employed to optimize visual and/or vestibular sensory inputs. Previous quantitative models of the biomechanics of human posture control have examined the simple case of ankle sway strategy, in which an inverted pendulum model is used, and the somewhat more complicated case of hip sway strategy, in which multisegment, articulated models are used. While these models can be used to quantify the gross dynamics of posture control, they are not sufficiently detailed to analyze head-trunk coordination strategies that may be crucial to understanding its underlying mechanisms. In this paper, we present a biomechanical model of upright human posture that extends an existing four mass, sagittal plane, link-segment model to a five mass model including an independent head link. The new model was developed to analyze segmental body movements during dynamic posturography experiments in order to study head-trunk coordination strategies and their influence on sensory inputs to balance control. It was designed specifically to analyze data collected on the EquiTest (NeuroCom International, Clackamas, OR) computerized dynamic posturography system, where the task of maintaining postural equilibrium may be challenged under conditions in which the visual surround, support surface, or both are in motion. The performance of the model was tested by comparing its estimated ground reaction forces to those measured directly by support surface force transducers. We conclude that this model will be a valuable analytical tool in the search for mechanisms of balance control.

Object-oriented approach to the automatic segmentation of bones from pediatric hand radiographs

NASA Astrophysics Data System (ADS)

Shim, Hyeonjoon; Liu, Brent J.; Taira, Ricky K.; Hall, Theodore R.

1997-04-01

The purpose of this paper is to develop a robust and accurate method that automatically segments phalangeal and epiphyseal bones from digital pediatric hand radiographs exhibiting various stages of growth. The development of this system draws principles from object-oriented design, model- guided analysis, and feedback control. A system architecture called 'the object segmentation machine' was implemented incorporating these design philosophies. The system is aided by a knowledge base where all model contours and other information such as age, race, and sex, are stored. These models include object structure models, shape models, 1-D wrist profiles, and gray level histogram models. Shape analysis is performed first by using an arc-length orientation transform to break down a given contour into elementary segments and curves. Then an interpretation tree is used as an inference engine to map known model contour segments to data contour segments obtained from the transform. Spatial and anatomical relationships among contour segments work as constraints from shape model. These constraints aid in generating a list of candidate matches. The candidate match with the highest confidence is chosen to be the current intermediate result. Verification of intermediate results are perform by a feedback control loop.

Object class segmentation of RGB-D video using recurrent convolutional neural networks.

PubMed

Pavel, Mircea Serban; Schulz, Hannes; Behnke, Sven

2017-04-01

Object class segmentation is a computer vision task which requires labeling each pixel of an image with the class of the object it belongs to. Deep convolutional neural networks (DNN) are able to learn and take advantage of local spatial correlations required for this task. They are, however, restricted by their small, fixed-sized filters, which limits their ability to learn long-range dependencies. Recurrent Neural Networks (RNN), on the other hand, do not suffer from this restriction. Their iterative interpretation allows them to model long-range dependencies by propagating activity. This property is especially useful when labeling video sequences, where both spatial and temporal long-range dependencies occur. In this work, a novel RNN architecture for object class segmentation is presented. We investigate several ways to train such a network. We evaluate our models on the challenging NYU Depth v2 dataset for object class segmentation and obtain competitive results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Learning-Based Object Identification and Segmentation Using Dual-Energy CT Images for Security.

PubMed

Martin, Limor; Tuysuzoglu, Ahmet; Karl, W Clem; Ishwar, Prakash

2015-11-01

In recent years, baggage screening at airports has included the use of dual-energy X-ray computed tomography (DECT), an advanced technology for nondestructive evaluation. The main challenge remains to reliably find and identify threat objects in the bag from DECT data. This task is particularly hard due to the wide variety of objects, the high clutter, and the presence of metal, which causes streaks and shading in the scanner images. Image noise and artifacts are generally much more severe than in medical CT and can lead to splitting of objects and inaccurate object labeling. The conventional approach performs object segmentation and material identification in two decoupled processes. Dual-energy information is typically not used for the segmentation, and object localization is not explicitly used to stabilize the material parameter estimates. We propose a novel learning-based framework for joint segmentation and identification of objects directly from volumetric DECT images, which is robust to streaks, noise and variability due to clutter. We focus on segmenting and identifying a small set of objects of interest with characteristics that are learned from training images, and consider everything else as background. We include data weighting to mitigate metal artifacts and incorporate an object boundary field to reduce object splitting. The overall formulation is posed as a multilabel discrete optimization problem and solved using an efficient graph-cut algorithm. We test the method on real data and show its potential for producing accurate labels of the objects of interest without splits in the presence of metal and clutter.

Translocation of Tektin 3 to the equatorial segment of heads in bull spermatozoa exposed to dibutyryl cAMP and calyculin A.

PubMed

Tsukamoto, Mariko; Hiyama, Erina; Hirotani, Karen; Gotoh, Takafumi; Inai, Tetsuichiro; Iida, Hiroshi

2017-01-01

Tektins (TEKTs) are filamentous proteins associated with microtubules in cilia, flagella, basal bodies, and centrioles. Five TEKTs (TEKT1, -2, -3, -4, and -5) have been identified as components of mammalian sperm flagella. We previously reported that TKET1 and -3 are also present in the heads of rodent spermatozoa. The present study clearly demonstrates that TEKT2 is present at the acrosome cap whereas TEKT3 resides just beneath the plasma membrane of the post-acrosomal region of sperm heads in unactivated bull spermatozoa, and builds on the distributional differences of TEKT1, -2, and -3 on sperm heads. We also discovered that hyperactivation of bull spermatozoa by cell-permeable cAMP and calyculin A, a protein phosphatase inhibitor, promoted translocation of TEKT3 from the post-acrosomal region to the equatorial segment in sperm heads, and that TEKT3 accumulated at the equatorial segment is lost upon acrosome reaction. Thus, translocation of TEKT3 to the equatorial segment may be a capacitation- or hyperactivation-associated phenomenon in bull spermatozoa. Mol. Reprod. Dev. 84: 30-43, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

[Object-oriented segmentation and classification of forest gap based on QuickBird remote sensing image.

PubMed

Mao, Xue Gang; Du, Zi Han; Liu, Jia Qian; Chen, Shu Xin; Hou, Ji Yu

2018-01-01

Traditional field investigation and artificial interpretation could not satisfy the need of forest gaps extraction at regional scale. High spatial resolution remote sensing image provides the possibility for regional forest gaps extraction. In this study, we used object-oriented classification method to segment and classify forest gaps based on QuickBird high resolution optical remote sensing image in Jiangle National Forestry Farm of Fujian Province. In the process of object-oriented classification, 10 scales (10-100, with a step length of 10) were adopted to segment QuickBird remote sensing image; and the intersection area of reference object (RA or ) and intersection area of segmented object (RA os ) were adopted to evaluate the segmentation result at each scale. For segmentation result at each scale, 16 spectral characteristics and support vector machine classifier (SVM) were further used to classify forest gaps, non-forest gaps and others. The results showed that the optimal segmentation scale was 40 when RA or was equal to RA os . The accuracy difference between the maximum and minimum at different segmentation scales was 22%. At optimal scale, the overall classification accuracy was 88% (Kappa=0.82) based on SVM classifier. Combining high resolution remote sensing image data with object-oriented classification method could replace the traditional field investigation and artificial interpretation method to identify and classify forest gaps at regional scale.

Image Segmentation Method Using Fuzzy C Mean Clustering Based on Multi-Objective Optimization

NASA Astrophysics Data System (ADS)

Chen, Jinlin; Yang, Chunzhi; Xu, Guangkui; Ning, Li

2018-04-01

Image segmentation is not only one of the hottest topics in digital image processing, but also an important part of computer vision applications. As one kind of image segmentation algorithms, fuzzy C-means clustering is an effective and concise segmentation algorithm. However, the drawback of FCM is that it is sensitive to image noise. To solve the problem, this paper designs a novel fuzzy C-mean clustering algorithm based on multi-objective optimization. We add a parameter λ to the fuzzy distance measurement formula to improve the multi-objective optimization. The parameter λ can adjust the weights of the pixel local information. In the algorithm, the local correlation of neighboring pixels is added to the improved multi-objective mathematical model to optimize the clustering cent. Two different experimental results show that the novel fuzzy C-means approach has an efficient performance and computational time while segmenting images by different type of noises.

Evaluation and comparison of current fetal ultrasound image segmentation methods for biometric measurements: a grand challenge.

PubMed

Rueda, Sylvia; Fathima, Sana; Knight, Caroline L; Yaqub, Mohammad; Papageorghiou, Aris T; Rahmatullah, Bahbibi; Foi, Alessandro; Maggioni, Matteo; Pepe, Antonietta; Tohka, Jussi; Stebbing, Richard V; McManigle, John E; Ciurte, Anca; Bresson, Xavier; Cuadra, Meritxell Bach; Sun, Changming; Ponomarev, Gennady V; Gelfand, Mikhail S; Kazanov, Marat D; Wang, Ching-Wei; Chen, Hsiang-Chou; Peng, Chun-Wei; Hung, Chu-Mei; Noble, J Alison

2014-04-01

This paper presents the evaluation results of the methods submitted to Challenge US: Biometric Measurements from Fetal Ultrasound Images, a segmentation challenge held at the IEEE International Symposium on Biomedical Imaging 2012. The challenge was set to compare and evaluate current fetal ultrasound image segmentation methods. It consisted of automatically segmenting fetal anatomical structures to measure standard obstetric biometric parameters, from 2D fetal ultrasound images taken on fetuses at different gestational ages (21 weeks, 28 weeks, and 33 weeks) and with varying image quality to reflect data encountered in real clinical environments. Four independent sub-challenges were proposed, according to the objects of interest measured in clinical practice: abdomen, head, femur, and whole fetus. Five teams participated in the head sub-challenge and two teams in the femur sub-challenge, including one team who tackled both. Nobody attempted the abdomen and whole fetus sub-challenges. The challenge goals were two-fold and the participants were asked to submit the segmentation results as well as the measurements derived from the segmented objects. Extensive quantitative (region-based, distance-based, and Bland-Altman measurements) and qualitative evaluation was performed to compare the results from a representative selection of current methods submitted to the challenge. Several experts (three for the head sub-challenge and two for the femur sub-challenge), with different degrees of expertise, manually delineated the objects of interest to define the ground truth used within the evaluation framework. For the head sub-challenge, several groups produced results that could be potentially used in clinical settings, with comparable performance to manual delineations. The femur sub-challenge had inferior performance to the head sub-challenge due to the fact that it is a harder segmentation problem and that the techniques presented relied more on the femur's appearance.

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

Unraveling Pancreatic Segmentation.

PubMed

Renard, Yohann; de Mestier, Louis; Perez, Manuela; Avisse, Claude; Lévy, Philippe; Kianmanesh, Reza

2018-04-01

Limited pancreatic resections are increasingly performed, but the rate of postoperative fistula is higher than after classical resections. Pancreatic segmentation, anatomically and radiologically identifiable, may theoretically help the surgeon removing selected anatomical portions with their own segmental pancreatic duct and thus might decrease the postoperative fistula rate. We aimed at systematically and comprehensively reviewing the previously proposed pancreatic segmentations and discuss their relevance and limitations. PubMed database was searched for articles investigating pancreatic segmentation, including human or animal anatomy, and cadaveric or surgical studies. Overall, 47/99 articles were selected and grouped into 4 main hypotheses of pancreatic segmentation methodology: anatomic, vascular, embryologic and lymphatic. The head, body and tail segments are gross description without distinct borders. The arterial territories defined vascular segments and isolate an isthmic paucivascular area. The embryological theory relied on the fusion plans of the embryological buds. The lymphatic drainage pathways defined the lymphatic segmentation. These theories had differences, but converged toward separating the head and body/tail parts, and the anterior from posterior and inferior parts of the pancreatic head. The rate of postoperative fistula was not decreased when surgical resection was performed following any of these segmentation theories; hence, none of them appeared relevant enough to guide pancreatic transections. Current pancreatic segmentation theories do not enable defining anatomical-surgical pancreatic segments. Other approaches should be explored, in particular focusing on pancreatic ducts, through pancreatic ducts reconstructions and embryologic 3D modelization.

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.

Fast automated segmentation of multiple objects via spatially weighted shape learning

NASA Astrophysics Data System (ADS)

Chandra, Shekhar S.; Dowling, Jason A.; Greer, Peter B.; Martin, Jarad; Wratten, Chris; Pichler, Peter; Fripp, Jurgen; Crozier, Stuart

2016-11-01

Active shape models (ASMs) have proved successful in automatic segmentation by using shape and appearance priors in a number of areas such as prostate segmentation, where accurate contouring is important in treatment planning for prostate cancer. The ASM approach however, is heavily reliant on a good initialisation for achieving high segmentation quality. This initialisation often requires algorithms with high computational complexity, such as three dimensional (3D) image registration. In this work, we present a fast, self-initialised ASM approach that simultaneously fits multiple objects hierarchically controlled by spatially weighted shape learning. Prominent objects are targeted initially and spatial weights are progressively adjusted so that the next (more difficult, less visible) object is simultaneously initialised using a series of weighted shape models. The scheme was validated and compared to a multi-atlas approach on 3D magnetic resonance (MR) images of 38 cancer patients and had the same (mean, median, inter-rater) Dice’s similarity coefficients of (0.79, 0.81, 0.85), while having no registration error and a computational time of 12-15 min, nearly an order of magnitude faster than the multi-atlas approach.

Fast automated segmentation of multiple objects via spatially weighted shape learning.

PubMed

Chandra, Shekhar S; Dowling, Jason A; Greer, Peter B; Martin, Jarad; Wratten, Chris; Pichler, Peter; Fripp, Jurgen; Crozier, Stuart

2016-11-21

Active shape models (ASMs) have proved successful in automatic segmentation by using shape and appearance priors in a number of areas such as prostate segmentation, where accurate contouring is important in treatment planning for prostate cancer. The ASM approach however, is heavily reliant on a good initialisation for achieving high segmentation quality. This initialisation often requires algorithms with high computational complexity, such as three dimensional (3D) image registration. In this work, we present a fast, self-initialised ASM approach that simultaneously fits multiple objects hierarchically controlled by spatially weighted shape learning. Prominent objects are targeted initially and spatial weights are progressively adjusted so that the next (more difficult, less visible) object is simultaneously initialised using a series of weighted shape models. The scheme was validated and compared to a multi-atlas approach on 3D magnetic resonance (MR) images of 38 cancer patients and had the same (mean, median, inter-rater) Dice's similarity coefficients of (0.79, 0.81, 0.85), while having no registration error and a computational time of 12-15 min, nearly an order of magnitude faster than the multi-atlas approach.

LOGISMOS—Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces: Cartilage Segmentation in the Knee Joint

PubMed Central

Zhang, Xiangmin; Williams, Rachel; Wu, Xiaodong; Anderson, Donald D.; Sonka, Milan

2011-01-01

A novel method for simultaneous segmentation of multiple interacting surfaces belonging to multiple interacting objects, called LOGISMOS (layered optimal graph image segmentation of multiple objects and surfaces), is reported. The approach is based on the algorithmic incorporation of multiple spatial inter-relationships in a single n-dimensional graph, followed by graph optimization that yields a globally optimal solution. The LOGISMOS method’s utility and performance are demonstrated on a bone and cartilage segmentation task in the human knee joint. Although trained on only a relatively small number of nine example images, this system achieved good performance. Judged by dice similarity coefficients (DSC) using a leave-one-out test, DSC values of 0.84 ± 0.04, 0.80 ± 0.04 and 0.80 ± 0.04 were obtained for the femoral, tibial, and patellar cartilage regions, respectively. These are excellent DSC values, considering the narrow-sheet character of the cartilage regions. Similarly, low signed mean cartilage thickness errors were obtained when compared to a manually-traced independent standard in 60 randomly selected 3-D MR image datasets from the Osteoarthritis Initiative database—0.11 ± 0.24, 0.05 ± 0.23, and 0.03 ± 0.17 mm for the femoral, tibial, and patellar cartilage thickness, respectively. The average signed surface positioning errors for the six detected surfaces ranged from 0.04 ± 0.12 mm to 0.16 ± 0.22 mm. The reported LOGISMOS framework provides robust and accurate segmentation of the knee joint bone and cartilage surfaces of the femur, tibia, and patella. As a general segmentation tool, the developed framework can be applied to a broad range of multiobject multisurface segmentation problems. PMID:20643602

Surface-region context in optimal multi-object graph-based segmentation: robust delineation of pulmonary tumors.

PubMed

Song, Qi; Chen, Mingqing; Bai, Junjie; Sonka, Milan; Wu, Xiaodong

2011-01-01

Multi-object segmentation with mutual interaction is a challenging task in medical image analysis. We report a novel solution to a segmentation problem, in which target objects of arbitrary shape mutually interact with terrain-like surfaces, which widely exists in the medical imaging field. The approach incorporates context information used during simultaneous segmentation of multiple objects. The object-surface interaction information is encoded by adding weighted inter-graph arcs to our graph model. A globally optimal solution is achieved by solving a single maximum flow problem in a low-order polynomial time. The performance of the method was evaluated in robust delineation of lung tumors in megavoltage cone-beam CT images in comparison with an expert-defined independent standard. The evaluation showed that our method generated highly accurate tumor segmentations. Compared with the conventional graph-cut method, our new approach provided significantly better results (p < 0.001). The Dice coefficient obtained by the conventional graph-cut approach (0.76 +/- 0.10) was improved to 0.84 +/- 0.05 when employing our new method for pulmonary tumor segmentation.

Multi-object model-based multi-atlas segmentation for rodent brains using dense discrete correspondences

NASA Astrophysics Data System (ADS)

Lee, Joohwi; Kim, Sun Hyung; Styner, Martin

2016-03-01

The delineation of rodent brain structures is challenging due to low-contrast multiple cortical and subcortical organs that are closely interfacing to each other. Atlas-based segmentation has been widely employed due to its ability to delineate multiple organs at the same time via image registration. The use of multiple atlases and subsequent label fusion techniques has further improved the robustness and accuracy of atlas-based segmentation. However, the accuracy of atlas-based segmentation is still prone to registration errors; for example, the segmentation of in vivo MR images can be less accurate and robust against image artifacts than the segmentation of post mortem images. In order to improve the accuracy and robustness of atlas-based segmentation, we propose a multi-object, model-based, multi-atlas segmentation method. We first establish spatial correspondences across atlases using a set of dense pseudo-landmark particles. We build a multi-object point distribution model using those particles in order to capture inter- and intra- subject variation among brain structures. The segmentation is obtained by fitting the model into a subject image, followed by label fusion process. Our result shows that the proposed method resulted in greater accuracy than comparable segmentation methods, including a widely used ANTs registration tool.

Causal Video Object Segmentation From Persistence of Occlusions

DTIC Science & Technology

2015-05-01

Precision, recall, and F-measure are reported on the ground truth anno - tations converted to binary masks. Note we cannot evaluate “number of...to lack of occlusions. References [1] P. Arbelaez, M. Maire, C. Fowlkes, and J . Malik. Con- tour detection and hierarchical image segmentation. TPAMI...X. Bai, J . Wang, D. Simons, and G. Sapiro. Video snapcut: robust video object cutout using localized classifiers. In ACM Transactions on Graphics

Object segmentation using graph cuts and active contours in a pyramidal framework

NASA Astrophysics Data System (ADS)

Subudhi, Priyambada; Mukhopadhyay, Susanta

2018-03-01

Graph cuts and active contours are two very popular interactive object segmentation techniques in the field of computer vision and image processing. However, both these approaches have their own well-known limitations. Graph cut methods perform efficiently giving global optimal segmentation result for smaller images. However, for larger images, huge graphs need to be constructed which not only takes an unacceptable amount of memory but also increases the time required for segmentation to a great extent. On the other hand, in case of active contours, initial contour selection plays an important role in the accuracy of the segmentation. So a proper selection of initial contour may improve the complexity as well as the accuracy of the result. In this paper, we have tried to combine these two approaches to overcome their above-mentioned drawbacks and develop a fast technique of object segmentation. Here, we have used a pyramidal framework and applied the mincut/maxflow algorithm on the lowest resolution image with the least number of seed points possible which will be very fast due to the smaller size of the image. Then, the obtained segmentation contour is super-sampled and and worked as the initial contour for the next higher resolution image. As the initial contour is very close to the actual contour, so fewer number of iterations will be required for the convergence of the contour. The process is repeated for all the high-resolution images and experimental results show that our approach is faster as well as memory efficient as compare to both graph cut or active contour segmentation alone.

Differential and relaxed image foresting transform for graph-cut segmentation of multiple 3D objects.

PubMed

Moya, Nikolas; Falcão, Alexandre X; Ciesielski, Krzysztof C; Udupa, Jayaram K

2014-01-01

Graph-cut algorithms have been extensively investigated for interactive binary segmentation, when the simultaneous delineation of multiple objects can save considerable user's time. We present an algorithm (named DRIFT) for 3D multiple object segmentation based on seed voxels and Differential Image Foresting Transforms (DIFTs) with relaxation. DRIFT stands behind efficient implementations of some state-of-the-art methods. The user can add/remove markers (seed voxels) along a sequence of executions of the DRIFT algorithm to improve segmentation. Its first execution takes linear time with the image's size, while the subsequent executions for corrections take sublinear time in practice. At each execution, DRIFT first runs the DIFT algorithm, then it applies diffusion filtering to smooth boundaries between objects (and background) and, finally, it corrects possible objects' disconnection occurrences with respect to their seeds. We evaluate DRIFT in 3D CT-images of the thorax for segmenting the arterial system, esophagus, left pleural cavity, right pleural cavity, trachea and bronchi, and the venous system.

Three-dimensional model-based object recognition and segmentation in cluttered scenes.

PubMed

Mian, Ajmal S; Bennamoun, Mohammed; Owens, Robyn

2006-10-01

Viewpoint independent recognition of free-form objects and their segmentation in the presence of clutter and occlusions is a challenging task. We present a novel 3D model-based algorithm which performs this task automatically and efficiently. A 3D model of an object is automatically constructed offline from its multiple unordered range images (views). These views are converted into multidimensional table representations (which we refer to as tensors). Correspondences are automatically established between these views by simultaneously matching the tensors of a view with those of the remaining views using a hash table-based voting scheme. This results in a graph of relative transformations used to register the views before they are integrated into a seamless 3D model. These models and their tensor representations constitute the model library. During online recognition, a tensor from the scene is simultaneously matched with those in the library by casting votes. Similarity measures are calculated for the model tensors which receive the most votes. The model with the highest similarity is transformed to the scene and, if it aligns accurately with an object in the scene, that object is declared as recognized and is segmented. This process is repeated until the scene is completely segmented. Experiments were performed on real and synthetic data comprised of 55 models and 610 scenes and an overall recognition rate of 95 percent was achieved. Comparison with the spin images revealed that our algorithm is superior in terms of recognition rate and efficiency.

Shared-hole graph search with adaptive constraints for 3D optic nerve head optical coherence tomography image segmentation

PubMed Central

Yu, Kai; Shi, Fei; Gao, Enting; Zhu, Weifang; Chen, Haoyu; Chen, Xinjian

2018-01-01

Optic nerve head (ONH) is a crucial region for glaucoma detection and tracking based on spectral domain optical coherence tomography (SD-OCT) images. In this region, the existence of a “hole” structure makes retinal layer segmentation and analysis very challenging. To improve retinal layer segmentation, we propose a 3D method for ONH centered SD-OCT image segmentation, which is based on a modified graph search algorithm with a shared-hole and locally adaptive constraints. With the proposed method, both the optic disc boundary and nine retinal surfaces can be accurately segmented in SD-OCT images. An overall mean unsigned border positioning error of 7.27 ± 5.40 µm was achieved for layer segmentation, and a mean Dice coefficient of 0.925 ± 0.03 was achieved for optic disc region detection. PMID:29541497

Object-based implicit learning in visual search: perceptual segmentation constrains contextual cueing.

PubMed

Conci, Markus; Müller, Hermann J; von Mühlenen, Adrian

2013-07-09

In visual search, detection of a target is faster when it is presented within a spatial layout of repeatedly encountered nontarget items, indicating that contextual invariances can guide selective attention (contextual cueing; Chun & Jiang, 1998). However, perceptual regularities may interfere with contextual learning; for instance, no contextual facilitation occurs when four nontargets form a square-shaped grouping, even though the square location predicts the target location (Conci & von Mühlenen, 2009). Here, we further investigated potential causes for this interference-effect: We show that contextual cueing can reliably occur for targets located within the region of a segmented object, but not for targets presented outside of the object's boundaries. Four experiments demonstrate an object-based facilitation in contextual cueing, with a modulation of context-based learning by relatively subtle grouping cues including closure, symmetry, and spatial regularity. Moreover, the lack of contextual cueing for targets located outside the segmented region was due to an absence of (latent) learning of contextual layouts, rather than due to an attentional bias towards the grouped region. Taken together, these results indicate that perceptual segmentation provides a basic structure within which contextual scene regularities are acquired. This in turn argues that contextual learning is constrained by object-based selection.

Segmentation, modeling and classification of the compact objects in a pile

NASA Technical Reports Server (NTRS)

Gupta, Alok; Funka-Lea, Gareth; Wohn, Kwangyoen

1990-01-01

The problem of interpreting dense range images obtained from the scene of a heap of man-made objects is discussed. A range image interpretation system consisting of segmentation, modeling, verification, and classification procedures is described. First, the range image is segmented into regions and reasoning is done about the physical support of these regions. Second, for each region several possible three-dimensional interpretations are made based on various scenarios of the objects physical support. Finally each interpretation is tested against the data for its consistency. The superquadric model is selected as the three-dimensional shape descriptor, plus tapering deformations along the major axis. Experimental results obtained from some complex range images of mail pieces are reported to demonstrate the soundness and the robustness of our approach.

Object Segmentation from Motion Discontinuities and Temporal Occlusions–A Biologically Inspired Model

PubMed Central

Beck, Cornelia; Ognibeni, Thilo; Neumann, Heiko

2008-01-01

Background Optic flow is an important cue for object detection. Humans are able to perceive objects in a scene using only kinetic boundaries, and can perform the task even when other shape cues are not provided. These kinetic boundaries are characterized by the presence of motion discontinuities in a local neighbourhood. In addition, temporal occlusions appear along the boundaries as the object in front covers the background and the objects that are spatially behind it. Methodology/Principal Findings From a technical point of view, the detection of motion boundaries for segmentation based on optic flow is a difficult task. This is due to the problem that flow detected along such boundaries is generally not reliable. We propose a model derived from mechanisms found in visual areas V1, MT, and MSTl of human and primate cortex that achieves robust detection along motion boundaries. It includes two separate mechanisms for both the detection of motion discontinuities and of occlusion regions based on how neurons respond to spatial and temporal contrast, respectively. The mechanisms are embedded in a biologically inspired architecture that integrates information of different model components of the visual processing due to feedback connections. In particular, mutual interactions between the detection of motion discontinuities and temporal occlusions allow a considerable improvement of the kinetic boundary detection. Conclusions/Significance A new model is proposed that uses optic flow cues to detect motion discontinuities and object occlusion. We suggest that by combining these results for motion discontinuities and object occlusion, object segmentation within the model can be improved. This idea could also be applied in other models for object segmentation. In addition, we discuss how this model is related to neurophysiological findings. The model was successfully tested both with artificial and real sequences including self and object motion. PMID:19043613

Multisensory Integration of Visual and Vestibular Signals Improves Heading Discrimination in the Presence of a Moving Object

PubMed Central

Dokka, Kalpana; DeAngelis, Gregory C.

2015-01-01

Humans and animals are fairly accurate in judging their direction of self-motion (i.e., heading) from optic flow when moving through a stationary environment. However, an object moving independently in the world alters the optic flow field and may bias heading perception if the visual system cannot dissociate object motion from self-motion. We investigated whether adding vestibular self-motion signals to optic flow enhances the accuracy of heading judgments in the presence of a moving object. Macaque monkeys were trained to report their heading (leftward or rightward relative to straight-forward) when self-motion was specified by vestibular, visual, or combined visual-vestibular signals, while viewing a display in which an object moved independently in the (virtual) world. The moving object induced significant biases in perceived heading when self-motion was signaled by either visual or vestibular cues alone. However, this bias was greatly reduced when visual and vestibular cues together signaled self-motion. In addition, multisensory heading discrimination thresholds measured in the presence of a moving object were largely consistent with the predictions of an optimal cue integration strategy. These findings demonstrate that multisensory cues facilitate the perceptual dissociation of self-motion and object motion, consistent with computational work that suggests that an appropriate decoding of multisensory visual-vestibular neurons can estimate heading while discounting the effects of object motion. SIGNIFICANCE STATEMENT Objects that move independently in the world alter the optic flow field and can induce errors in perceiving the direction of self-motion (heading). We show that adding vestibular (inertial) self-motion signals to optic flow almost completely eliminates the errors in perceived heading induced by an independently moving object. Furthermore, this increased accuracy occurs without a substantial loss in the precision. Our results thus demonstrate that

A multi-object statistical atlas adaptive for deformable registration errors in anomalous medical image segmentation

NASA Astrophysics Data System (ADS)

Botter Martins, Samuel; Vallin Spina, Thiago; Yasuda, Clarissa; Falcão, Alexandre X.

2017-02-01

Statistical Atlases have played an important role towards automated medical image segmentation. However, a challenge has been to make the atlas more adaptable to possible errors in deformable registration of anomalous images, given that the body structures of interest for segmentation might present significant differences in shape and texture. Recently, deformable registration errors have been accounted by a method that locally translates the statistical atlas over the test image, after registration, and evaluates candidate objects from a delineation algorithm in order to choose the best one as final segmentation. In this paper, we improve its delineation algorithm and extend the model to be a multi-object statistical atlas, built from control images and adaptable to anomalous images, by incorporating a texture classifier. In order to provide a first proof of concept, we instantiate the new method for segmenting, object-by-object and all objects simultaneously, the left and right brain hemispheres, and the cerebellum, without the brainstem, and evaluate it on MRT1-images of epilepsy patients before and after brain surgery, which removed portions of the temporal lobe. The results show efficiency gain with statistically significant higher accuracy, using the mean Average Symmetric Surface Distance, with respect to the original approach.

From Phenomena to Objects: Segmentation of Fuzzy Objects and its Application to Oceanic Eddies

NASA Astrophysics Data System (ADS)

Wu, Qingling

A challenging image analysis problem that has received limited attention to date is the isolation of fuzzy objects---i.e. those with inherently indeterminate boundaries---from continuous field data. This dissertation seeks to bridge the gap between, on the one hand, the recognized need for Object-Based Image Analysis of fuzzy remotely sensed features, and on the other, the optimization of existing image segmentation techniques for the extraction of more discretely bounded features. Using mesoscale oceanic eddies as a case study of a fuzzy object class evident in Sea Surface Height Anomaly (SSHA) imagery, the dissertation demonstrates firstly, that the widely used region-growing and watershed segmentation techniques can be optimized and made comparable in the absence of ground truth data using the principle of parsimony. However, they both have significant shortcomings, with the region growing procedure creating contour polygons that do not follow the shape of eddies while the watershed technique frequently subdivides eddies or groups together separate eddy objects. Secondly, it was determined that these problems can be remedied by using a novel Non-Euclidian Voronoi (NEV) tessellation technique. NEV is effective in isolating the extrema associated with eddies in SSHA data while using a non-Euclidian cost-distance based procedure (based on cumulative gradients in ocean height) to define the boundaries between fuzzy objects. Using this procedure as the first stage in isolating candidate eddy objects, a novel "region-shrinking" multicriteria eddy identification algorithm was developed that includes consideration of shape and vorticity. Eddies identified by this region-shrinking technique compare favorably with those identified by existing techniques, while simplifying and improving existing automated eddy detection algorithms. However, it also tends to find a larger number of eddies as a result of its ability to separate what other techniques identify as connected

Objects Grouping for Segmentation of Roads Network in High Resolution Images of Urban Areas

NASA Astrophysics Data System (ADS)

Maboudi, M.; Amini, J.; Hahn, M.

2016-06-01

Updated road databases are required for many purposes such as urban planning, disaster management, car navigation, route planning, traffic management and emergency handling. In the last decade, the improvement in spatial resolution of VHR civilian satellite sensors - as the main source of large scale mapping applications - was so considerable that GSD has become finer than size of common urban objects of interest such as building, trees and road parts. This technological advancement pushed the development of "Object-based Image Analysis (OBIA)" as an alternative to pixel-based image analysis methods. Segmentation as one of the main stages of OBIA provides the image objects on which most of the following processes will be applied. Therefore, the success of an OBIA approach is strongly affected by the segmentation quality. In this paper, we propose a purpose-dependent refinement strategy in order to group road segments in urban areas using maximal similarity based region merging. For investigations with the proposed method, we use high resolution images of some urban sites. The promising results suggest that the proposed approach is applicable in grouping of road segments in urban areas.

Object Segmentation and Ground Truth in 3D Embryonic Imaging.

PubMed

Rajasekaran, Bhavna; Uriu, Koichiro; Valentin, Guillaume; Tinevez, Jean-Yves; Oates, Andrew C

2016-01-01

Many questions in developmental biology depend on measuring the position and movement of individual cells within developing embryos. Yet, tools that provide this data are often challenged by high cell density and their accuracy is difficult to measure. Here, we present a three-step procedure to address this problem. Step one is a novel segmentation algorithm based on image derivatives that, in combination with selective post-processing, reliably and automatically segments cell nuclei from images of densely packed tissue. Step two is a quantitative validation using synthetic images to ascertain the efficiency of the algorithm with respect to signal-to-noise ratio and object density. Finally, we propose an original method to generate reliable and experimentally faithful ground truth datasets: Sparse-dense dual-labeled embryo chimeras are used to unambiguously measure segmentation errors within experimental data. Together, the three steps outlined here establish a robust, iterative procedure to fine-tune image analysis algorithms and microscopy settings associated with embryonic 3D image data sets.

Object Segmentation and Ground Truth in 3D Embryonic Imaging

PubMed Central

Rajasekaran, Bhavna; Uriu, Koichiro; Valentin, Guillaume; Tinevez, Jean-Yves; Oates, Andrew C.

2016-01-01

Many questions in developmental biology depend on measuring the position and movement of individual cells within developing embryos. Yet, tools that provide this data are often challenged by high cell density and their accuracy is difficult to measure. Here, we present a three-step procedure to address this problem. Step one is a novel segmentation algorithm based on image derivatives that, in combination with selective post-processing, reliably and automatically segments cell nuclei from images of densely packed tissue. Step two is a quantitative validation using synthetic images to ascertain the efficiency of the algorithm with respect to signal-to-noise ratio and object density. Finally, we propose an original method to generate reliable and experimentally faithful ground truth datasets: Sparse-dense dual-labeled embryo chimeras are used to unambiguously measure segmentation errors within experimental data. Together, the three steps outlined here establish a robust, iterative procedure to fine-tune image analysis algorithms and microscopy settings associated with embryonic 3D image data sets. PMID:27332860

Cerebrovascular plaque segmentation using object class uncertainty snake in MR images

NASA Astrophysics Data System (ADS)

Das, Bipul; Saha, Punam K.; Wolf, Ronald; Song, Hee Kwon; Wright, Alexander C.; Wehrli, Felix W.

2005-04-01

Atherosclerotic cerebrovascular disease leads to formation of lipid-laden plaques that can form emboli when ruptured causing blockage to cerebral vessels. The clinical manifestation of this event sequence is stroke; a leading cause of disability and death. In vivo MR imaging provides detailed image of vascular architecture for the carotid artery making it suitable for analysis of morphological features. Assessing the status of carotid arteries that supplies blood to the brain is of primary interest to such investigations. Reproducible quantification of carotid artery dimensions in MR images is essential for plaque analysis. Manual segmentation being the only method presently makes it time consuming and sensitive to inter and intra observer variability. This paper presents a deformable model for lumen and vessel wall segmentation of carotid artery from MR images. The major challenges of carotid artery segmentation are (a) low signal-to-noise ratio, (b) background intensity inhomogeneity and (c) indistinct inner and/or outer vessel wall. We propose a new, effective object-class uncertainty based deformable model with additional features tailored toward this specific application. Object-class uncertainty optimally utilizes MR intensity characteristics of various anatomic entities that enable the snake to avert leakage through fuzzy boundaries. To strengthen the deformable model for this application, some other properties are attributed to it in the form of (1) fully arc-based deformation using a Gaussian model to maximally exploit vessel wall smoothness, (2) construction of a forbidden region for outer-wall segmentation to reduce interferences by prominent lumen features and (3) arc-based landmark for efficient user interaction. The algorithm has been tested upon T1- and PD- weighted images. Measures of lumen area and vessel wall area are computed from segmented data of 10 patient MR images and their accuracy and reproducibility are examined. These results correspond

Modeling visual clutter perception using proto-object segmentation

PubMed Central

Yu, Chen-Ping; Samaras, Dimitris; Zelinsky, Gregory J.

2014-01-01

We introduce the proto-object model of visual clutter perception. This unsupervised model segments an image into superpixels, then merges neighboring superpixels that share a common color cluster to obtain proto-objects—defined here as spatially extended regions of coherent features. Clutter is estimated by simply counting the number of proto-objects. We tested this model using 90 images of realistic scenes that were ranked by observers from least to most cluttered. Comparing this behaviorally obtained ranking to a ranking based on the model clutter estimates, we found a significant correlation between the two (Spearman's ρ = 0.814, p < 0.001). We also found that the proto-object model was highly robust to changes in its parameters and was generalizable to unseen images. We compared the proto-object model to six other models of clutter perception and demonstrated that it outperformed each, in some cases dramatically. Importantly, we also showed that the proto-object model was a better predictor of clutter perception than an actual count of the number of objects in the scenes, suggesting that the set size of a scene may be better described by proto-objects than objects. We conclude that the success of the proto-object model is due in part to its use of an intermediate level of visual representation—one between features and objects—and that this is evidence for the potential importance of a proto-object representation in many common visual percepts and tasks. PMID:24904121

Metamerism in cephalochordates and the problem of the vertebrate head.

PubMed

Onai, Takayuki; Adachi, Noritaka; Kuratani, Shigeru

2017-01-01

The vertebrate head characteristically exhibits a complex pattern with sense organs, brain, paired eyes and jaw muscles, and the brain case is not found in other chordates. How the extant vertebrate head has evolved remains enigmatic. Historically, there have been two conflicting views on the origin of the vertebrate head, segmental and non-segmental views. According to the segmentalists, the vertebrate head is organized as a metameric structure composed of segments equivalent to those in the trunk; a metamere in the vertebrate head was assumed to consist of a somite, a branchial arch and a set of cranial nerves, considering that the head evolved from rostral segments of amphioxus-like ancestral vertebrates. Non-segmentalists, however, considered that the vertebrate head was not segmental. In that case, the ancestral state of the vertebrate head may be non-segmented, and rostral segments in amphioxus might have been secondarily gained, or extant vertebrates might have evolved through radical modifications of amphioxus-like ancestral vertebrate head. Comparative studies of mesodermal development in amphioxus and vertebrate gastrula embryos have revealed that mesodermal gene expressions become segregated into two domains anteroposteriorly to specify the head mesoderm and trunk mesoderm only in vertebrates; in this segregation, key genes such as delta and hairy, involved in segment formation, are expressed in the trunk mesoderm, but not in the head mesoderm, strongly suggesting that the head mesoderm of extant vertebrates is not segmented. Taken together, the above finding possibly adds a new insight into the origin of the vertebrate head; the vertebrate head mesoderm would have evolved through an anteroposterior polarization of the paraxial mesoderm if the ancestral vertebrate had been amphioxus-like.

Social interaction facilitates word learning in preverbal infants: Word-object mapping and word segmentation.

PubMed

Hakuno, Yoko; Omori, Takahide; Yamamoto, Jun-Ichi; Minagawa, Yasuyo

2017-08-01

In natural settings, infants learn spoken language with the aid of a caregiver who explicitly provides social signals. Although previous studies have demonstrated that young infants are sensitive to these signals that facilitate language development, the impact of real-life interactions on early word segmentation and word-object mapping remains elusive. We tested whether infants aged 5-6 months and 9-10 months could segment a word from continuous speech and acquire a word-object relation in an ecologically valid setting. In Experiment 1, infants were exposed to a live tutor, while in Experiment 2, another group of infants were exposed to a televised tutor. Results indicate that both younger and older infants were capable of segmenting a word and learning a word-object association only when the stimuli were derived from a live tutor in a natural manner, suggesting that real-life interaction enhances the learning of spoken words in preverbal infants. Copyright © 2017 Elsevier Inc. All rights reserved.

Object segmentation and recovery via neural oscillators implementing the similarity and prior knowledge gestalt rules.

PubMed

Ursino, Mauro; Magosso, Elisa; La Cara, Giuseppe-Emiliano; Cuppini, Cristiano

2006-09-01

Object recognition requires the solution of the binding and segmentation problems, i.e., grouping different features to achieve a coherent representation. Synchronization of neural activity in the gamma-band, associated with gestalt perception, has often been proposed as a putative mechanism to solve these problems, not only as to low-level processing, but also in higher cortical functions. In the present work, a network of Wilson-Cowan oscillators is used to segment simultaneous objects, and recover an object from partial or corrupted information, by implementing two gestalt rules: similarity and prior knowledge. The network consists of H different areas, each devoted to representation of a particular feature of the object, according to a topological organization. The similarity law is realized via lateral intra-area connections, arranged as a "Mexican-hat". Prior knowledge is realized via inter-area connections, which link properties belonging to a previously memorized object. A global inhibitor allows segmentation of several objects avoiding interference. Simulation results, performed using three simultaneous input objects, show that the network is able to detect an object even in difficult conditions (i.e., when some features are absent or shifted with respect to the original one). Moreover, the trade-off between sensitivity (capacity to detect true positives) and specificity (capacity to reject false positives) can be controlled acting on the extension of lateral synapses (i.e., on the level of accepted similarity). Finally, the network can also deal with correlated objects, i.e., objects which have some common features. Simulations performed using a different number of objects (2, 3, 4 or 5) suggest that the network is able to segment and recall up to four objects, but the oscillation frequency must increase, the lower the number of objects simultaneously present. The model, although quite simpler compared with neurophysiology, may represent a theoretical

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.

The effect of hardhats on head and neck response to vertical impacts from large construction objects.

PubMed

Suderman, Bethany L; Hoover, Ryan W; Ching, Randal P; Scher, Irving S

2014-12-01

We evaluated the effectiveness of hardhats in attenuating head acceleration and neck force in vertical impacts from large construction objects. Two weight-matched objects (lead shot bag and concrete block) weighing 9.1 kg were dropped from three heights (0.91 m, 1.83 m and 2.74 m) onto the head of a 50th percentile male Hybrid III anthropomorphic test device (ATD). Two headgear conditions were tested: no head protection and an ANSI Type-I, Class-E hardhat. A third headgear condition (snow sport helmet) was tested at 1.83 m for comparison with the hardhat. Hardhats significantly reduced the resultant linear acceleration for the concrete block impacts by 70-95% when compared to the unprotected head condition. Upper neck compression was also significantly reduced by 26-60% with the use of a hardhat when compared to the unprotected head condition for the 0.91 and 1.83 m drop heights for both lead shot and concrete block drop objects. In this study we found that hardhats can be effective in reducing both head accelerations and compressive neck forces for large construction objects in vertical impacts. Copyright © 2014 Elsevier Ltd. All rights reserved.

Herbig-Haro objects as the heads of radiative jets

NASA Technical Reports Server (NTRS)

Blondin, John M.; Konigl, Arieh; Fryxell, Bruce A.

1989-01-01

The interpretation of certain HH objects as the heads of nonadiabatic supersonic jets is examined using two-dimensional numerical simulations. It is found that radiative jets develop a dense shell between the jet shock and the leading bow shock when the cooling distance behind either one of these shocks is smaller than the jet radius. It is proposed that the radiatively cooling shell may account for the variable emission pattern from objects like HH 1. Also, it is suggested that HH objects with measured space velocities that exceed the spectroscopically inferred shock velocities could correspond to heavy jets in which the bow shock is effectively adiabatic. Low-excitation objects in which these velocities are comparable may represent light jets where the jet shock is nonradiative.

3D noise-resistant segmentation and tracking of unknown and occluded objects using integral imaging

NASA Astrophysics Data System (ADS)

Aloni, Doron; Jung, Jae-Hyun; Yitzhaky, Yitzhak

2017-10-01

Three dimensional (3D) object segmentation and tracking can be useful in various computer vision applications, such as: object surveillance for security uses, robot navigation, etc. We present a method for 3D multiple-object tracking using computational integral imaging, based on accurate 3D object segmentation. The method does not employ object detection by motion analysis in a video as conventionally performed (such as background subtraction or block matching). This means that the movement properties do not significantly affect the detection quality. The object detection is performed by analyzing static 3D image data obtained through computational integral imaging With regard to previous works that used integral imaging data in such a scenario, the proposed method performs the 3D tracking of objects without prior information about the objects in the scene, and it is found efficient under severe noise conditions.

[An object-oriented remote sensing image segmentation approach based on edge detection].

PubMed

Tan, Yu-Min; Huai, Jian-Zhu; Tang, Zhong-Shi

2010-06-01

Satellite sensor technology endorsed better discrimination of various landscape objects. Image segmentation approaches to extracting conceptual objects and patterns hence have been explored and a wide variety of such algorithms abound. To this end, in order to effectively utilize edge and topological information in high resolution remote sensing imagery, an object-oriented algorithm combining edge detection and region merging is proposed. Susan edge filter is firstly applied to the panchromatic band of Quickbird imagery with spatial resolution of 0.61 m to obtain the edge map. Thanks to the resulting edge map, a two-phrase region-based segmentation method operates on the fusion image from panchromatic and multispectral Quickbird images to get the final partition result. In the first phase, a quad tree grid consisting of squares with sides parallel to the image left and top borders agglomerates the square subsets recursively where the uniform measure is satisfied to derive image object primitives. Before the merger of the second phrase, the contextual and spatial information, (e. g., neighbor relationship, boundary coding) of the resulting squares are retrieved efficiently by means of the quad tree structure. Then a region merging operation is performed with those primitives, during which the criterion for region merging integrates edge map and region-based features. This approach has been tested on the QuickBird images of some site in Sanxia area and the result is compared with those of ENVI Zoom Definiens. In addition, quantitative evaluation of the quality of segmentation results is also presented. Experiment results demonstrate stable convergence and efficiency.

Image processing strategies based on saliency segmentation for object recognition under simulated prosthetic vision.

PubMed

Li, Heng; Su, Xiaofan; Wang, Jing; Kan, Han; Han, Tingting; Zeng, Yajie; Chai, Xinyu

2018-01-01

Current retinal prostheses can only generate low-resolution visual percepts constituted of limited phosphenes which are elicited by an electrode array and with uncontrollable color and restricted grayscale. Under this visual perception, prosthetic recipients can just complete some simple visual tasks, but more complex tasks like face identification/object recognition are extremely difficult. Therefore, it is necessary to investigate and apply image processing strategies for optimizing the visual perception of the recipients. This study focuses on recognition of the object of interest employing simulated prosthetic vision. We used a saliency segmentation method based on a biologically plausible graph-based visual saliency model and a grabCut-based self-adaptive-iterative optimization framework to automatically extract foreground objects. Based on this, two image processing strategies, Addition of Separate Pixelization and Background Pixel Shrink, were further utilized to enhance the extracted foreground objects. i) The results showed by verification of psychophysical experiments that under simulated prosthetic vision, both strategies had marked advantages over Direct Pixelization in terms of recognition accuracy and efficiency. ii) We also found that recognition performance under two strategies was tied to the segmentation results and was affected positively by the paired-interrelated objects in the scene. The use of the saliency segmentation method and image processing strategies can automatically extract and enhance foreground objects, and significantly improve object recognition performance towards recipients implanted a high-density implant. Copyright © 2017 Elsevier B.V. All rights reserved.

What is a segment?

PubMed

Hannibal, Roberta L; Patel, Nipam H

2013-12-17

Animals have been described as segmented for more than 2,000 years, yet a precise definition of segmentation remains elusive. Here we give the history of the definition of segmentation, followed by a discussion on current controversies in defining a segment. While there is a general consensus that segmentation involves the repetition of units along the anterior-posterior (a-p) axis, long-running debates exist over whether a segment can be composed of only one tissue layer, whether the most anterior region of the arthropod head is considered segmented, and whether and how the vertebrate head is segmented. Additionally, we discuss whether a segment can be composed of a single cell in a column of cells, or a single row of cells within a grid of cells. We suggest that 'segmentation' be used in its more general sense, the repetition of units with a-p polarity along the a-p axis, to prevent artificial classification of animals. We further suggest that this general definition be combined with an exact description of what is being studied, as well as a clearly stated hypothesis concerning the specific nature of the potential homology of structures. These suggestions should facilitate dialogue among scientists who study vastly differing segmental structures.

Head patterning and Hox gene expression in an onychophoran and its implications for the arthropod head problem.

PubMed

Eriksson, Bo Joakim; Tait, Noel N; Budd, Graham E; Janssen, Ralf; Akam, Michael

2010-09-01

The arthropod head problem has puzzled zoologists for more than a century. The head of adult arthropods is a complex structure resulting from the modification, fusion and migration of an uncertain number of segments. In contrast, onychophorans, which are the probable sister group to the arthropods, have a rather simple head comprising three segments that are well defined during development, and give rise to the adult head with three pairs of appendages specialised for sensory and food capture/manipulative purposes. Based on the expression pattern of the anterior Hox genes labial, proboscipedia, Hox3 and Deformed, we show that the third of these onychophoran segments, bearing the slime papillae, can be correlated to the tritocerebrum, the most anterior Hox-expressing arthropod segment. This implies that both the onychophoran antennae and jaws are derived from a more anterior, Hox-free region corresponding to the proto and deutocerebrum of arthropods. Our data provide molecular support for the proposal that the onychophoran head possesses a well-developed appendage that corresponds to the anterior, apparently appendage-less region of the arthropod head.

Automatic segmentation of the optic nerve head for deformation measurements in video rate optical coherence tomography

NASA Astrophysics Data System (ADS)

Hidalgo-Aguirre, Maribel; Gitelman, Julian; Lesk, Mark Richard; Costantino, Santiago

2015-11-01

Optical coherence tomography (OCT) imaging has become a standard diagnostic tool in ophthalmology, providing essential information associated with various eye diseases. In order to investigate the dynamics of the ocular fundus, we present a simple and accurate automated algorithm to segment the inner limiting membrane in video-rate optic nerve head spectral domain (SD) OCT images. The method is based on morphological operations including a two-step contrast enhancement technique, proving to be very robust when dealing with low signal-to-noise ratio images and pathological eyes. An analysis algorithm was also developed to measure neuroretinal tissue deformation from the segmented retinal profiles. The performance of the algorithm is demonstrated, and deformation results are presented for healthy and glaucomatous eyes.

What is a segment?

PubMed Central

2013-01-01

Animals have been described as segmented for more than 2,000 years, yet a precise definition of segmentation remains elusive. Here we give the history of the definition of segmentation, followed by a discussion on current controversies in defining a segment. While there is a general consensus that segmentation involves the repetition of units along the anterior-posterior (a-p) axis, long-running debates exist over whether a segment can be composed of only one tissue layer, whether the most anterior region of the arthropod head is considered segmented, and whether and how the vertebrate head is segmented. Additionally, we discuss whether a segment can be composed of a single cell in a column of cells, or a single row of cells within a grid of cells. We suggest that ‘segmentation’ be used in its more general sense, the repetition of units with a-p polarity along the a-p axis, to prevent artificial classification of animals. We further suggest that this general definition be combined with an exact description of what is being studied, as well as a clearly stated hypothesis concerning the specific nature of the potential homology of structures. These suggestions should facilitate dialogue among scientists who study vastly differing segmental structures. PMID:24345042

Automated Segmentation of the Parotid Gland Based on Atlas Registration and Machine Learning: A Longitudinal MRI Study in Head-and-Neck Radiation Therapy

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

Yang, Xiaofeng; Wu, Ning; Cheng, Guanghui

Purpose: To develop an automated magnetic resonance imaging (MRI) parotid segmentation method to monitor radiation-induced parotid gland changes in patients after head and neck radiation therapy (RT). Methods and Materials: The proposed method combines the atlas registration method, which captures the global variation of anatomy, with a machine learning technology, which captures the local statistical features, to automatically segment the parotid glands from the MRIs. The segmentation method consists of 3 major steps. First, an atlas (pre-RT MRI and manually contoured parotid gland mask) is built for each patient. A hybrid deformable image registration is used to map the pre-RTmore » MRI to the post-RT MRI, and the transformation is applied to the pre-RT parotid volume. Second, the kernel support vector machine (SVM) is trained with the subject-specific atlas pair consisting of multiple features (intensity, gradient, and others) from the aligned pre-RT MRI and the transformed parotid volume. Third, the well-trained kernel SVM is used to differentiate the parotid from surrounding tissues in the post-RT MRIs by statistically matching multiple texture features. A longitudinal study of 15 patients undergoing head and neck RT was conducted: baseline MRI was acquired prior to RT, and the post-RT MRIs were acquired at 3-, 6-, and 12-month follow-up examinations. The resulting segmentations were compared with the physicians' manual contours. Results: Successful parotid segmentation was achieved for all 15 patients (42 post-RT MRIs). The average percentage of volume differences between the automated segmentations and those of the physicians' manual contours were 7.98% for the left parotid and 8.12% for the right parotid. The average volume overlap was 91.1% ± 1.6% for the left parotid and 90.5% ± 2.4% for the right parotid. The parotid gland volume reduction at follow-up was 25% at 3 months, 27% at 6 months, and 16% at 12 months. Conclusions: We have validated

Automated segmentation of the parotid gland based on atlas registration and machine learning: a longitudinal MRI study in head-and-neck radiation therapy.

PubMed

Yang, Xiaofeng; Wu, Ning; Cheng, Guanghui; Zhou, Zhengyang; Yu, David S; Beitler, Jonathan J; Curran, Walter J; Liu, Tian

2014-12-01

To develop an automated magnetic resonance imaging (MRI) parotid segmentation method to monitor radiation-induced parotid gland changes in patients after head and neck radiation therapy (RT). The proposed method combines the atlas registration method, which captures the global variation of anatomy, with a machine learning technology, which captures the local statistical features, to automatically segment the parotid glands from the MRIs. The segmentation method consists of 3 major steps. First, an atlas (pre-RT MRI and manually contoured parotid gland mask) is built for each patient. A hybrid deformable image registration is used to map the pre-RT MRI to the post-RT MRI, and the transformation is applied to the pre-RT parotid volume. Second, the kernel support vector machine (SVM) is trained with the subject-specific atlas pair consisting of multiple features (intensity, gradient, and others) from the aligned pre-RT MRI and the transformed parotid volume. Third, the well-trained kernel SVM is used to differentiate the parotid from surrounding tissues in the post-RT MRIs by statistically matching multiple texture features. A longitudinal study of 15 patients undergoing head and neck RT was conducted: baseline MRI was acquired prior to RT, and the post-RT MRIs were acquired at 3-, 6-, and 12-month follow-up examinations. The resulting segmentations were compared with the physicians' manual contours. Successful parotid segmentation was achieved for all 15 patients (42 post-RT MRIs). The average percentage of volume differences between the automated segmentations and those of the physicians' manual contours were 7.98% for the left parotid and 8.12% for the right parotid. The average volume overlap was 91.1% ± 1.6% for the left parotid and 90.5% ± 2.4% for the right parotid. The parotid gland volume reduction at follow-up was 25% at 3 months, 27% at 6 months, and 16% at 12 months. We have validated our automated parotid segmentation algorithm in a longitudinal study

3-D segmentation of retinal blood vessels in spectral-domain OCT volumes of the optic nerve head

NASA Astrophysics Data System (ADS)

Lee, Kyungmoo; Abràmoff, Michael D.; Niemeijer, Meindert; Garvin, Mona K.; Sonka, Milan

2010-03-01

Segmentation of retinal blood vessels can provide important information for detecting and tracking retinal vascular diseases including diabetic retinopathy, arterial hypertension, arteriosclerosis and retinopathy of prematurity (ROP). Many studies on 2-D segmentation of retinal blood vessels from a variety of medical images have been performed. However, 3-D segmentation of retinal blood vessels from spectral-domain optical coherence tomography (OCT) volumes, which is capable of providing geometrically accurate vessel models, to the best of our knowledge, has not been previously studied. The purpose of this study is to develop and evaluate a method that can automatically detect 3-D retinal blood vessels from spectral-domain OCT scans centered on the optic nerve head (ONH). The proposed method utilized a fast multiscale 3-D graph search to segment retinal surfaces as well as a triangular mesh-based 3-D graph search to detect retinal blood vessels. An experiment on 30 ONH-centered OCT scans (15 right eye scans and 15 left eye scans) from 15 subjects was performed, and the mean unsigned error in 3-D of the computer segmentations compared with the independent standard obtained from a retinal specialist was 3.4 +/- 2.5 voxels (0.10 +/- 0.07 mm).

Conservation, Innovation, and Bias: Embryonic Segment Boundaries Position Posterior, but Not Anterior, Head Horns in Adult Beetles.

PubMed

Busey, Hannah A; Zattara, Eduardo E; Moczek, Armin P

2016-07-01

The integration of form and function of novel traits is a fundamental process during the developmental evolution of complex organisms, yet how novel traits and trait functions integrate into preexisting contexts remains poorly understood. Here, we explore the mechanisms by which the adult insect head has been able to integrate novel traits and features during its ontogeny, focusing on the cephalic horns of Onthophagus beetles. Specifically, using a microablation approach we investigate how different regions of the dorsal head of adult horned beetles relate to their larval and embryonic counterparts and test whether deeply conserved regional boundaries that establish the embryonic head might also facilitate or bias the positioning of cephalic horns along the dorsal adult head. We find that paired posterior horns-the most widespread horn type within the genus-are positioned along a border homologous to the embryonic clypeolabral (CL)-ocular boundary, and that this placement constitutes the ancestral form of horn positioning. In contrast, we observed that the phylogenetically much rarer anterior horns are positioned by larval head regions contained firmly within the CL segment and away from any major preexisting larval head landmarks or boundaries. Lastly, we describe the unexpected finding that ablations at medial head regions can result in ectopic outgrowths bearing terminal structures resembling the more anterior clypeal ridge. We discuss our results in the light of the developmental genetic mechanisms of head formation in holometabolous insects and the role of co-option in innovation and bias in developmental evolution. © 2016 Wiley Periodicals, Inc.

Automated MRI Segmentation for Individualized Modeling of Current Flow in the Human Head

PubMed Central

Huang, Yu; Dmochowski, Jacek P.; Su, Yuzhuo; Datta, Abhishek; Rorden, Christopher; Parra, Lucas C.

2013-01-01

Objective High-definition transcranial direct current stimulation (HD-tDCS) and high-density electroencephalography (HD-EEG) require accurate models of current flow for precise targeting and current source reconstruction. At a minimum, such modeling must capture the idiosyncratic anatomy of brain, cerebrospinal fluid (CSF) and skull for each individual subject. Currently, the process to build such high-resolution individualized models from structural magnetic resonance images (MRI) requires labor-intensive manual segmentation, even when leveraging available automated segmentation tools. Also, accurate placement of many high-density electrodes on individual scalp is a tedious procedure. The goal was to develop fully automated techniques to reduce the manual effort in such a modeling process. Approach A fully automated segmentation technique based on Statical Parametric Mapping 8 (SPM8), including an improved tissue probability map (TPM) and an automated correction routine for segmentation errors, was developed, along with an automated electrode placement tool for high-density arrays. The performance of these automated routines was evaluated against results from manual segmentation on 4 healthy subjects and 7 stroke patients. The criteria include segmentation accuracy, the difference of current flow distributions in resulting HD-tDCS models and the optimized current flow intensities on cortical targets. Main results The segmentation tool can segment out not just the brain but also provide accurate results for CSF, skull and other soft tissues with a field of view (FOV) extending to the neck. Compared to manual results, automated segmentation deviates by only 7% and 18% for normal and stroke subjects, respectively. The predicted electric fields in the brain deviate by 12% and 29% respectively, which is well within the variability observed for various modeling choices. Finally, optimized current flow intensities on cortical targets do not differ significantly

Automated MRI segmentation for individualized modeling of current flow in the human head

NASA Astrophysics Data System (ADS)

Huang, Yu; Dmochowski, Jacek P.; Su, Yuzhuo; Datta, Abhishek; Rorden, Christopher; Parra, Lucas C.

2013-12-01

Objective. High-definition transcranial direct current stimulation (HD-tDCS) and high-density electroencephalography require accurate models of current flow for precise targeting and current source reconstruction. At a minimum, such modeling must capture the idiosyncratic anatomy of the brain, cerebrospinal fluid (CSF) and skull for each individual subject. Currently, the process to build such high-resolution individualized models from structural magnetic resonance images requires labor-intensive manual segmentation, even when utilizing available automated segmentation tools. Also, accurate placement of many high-density electrodes on an individual scalp is a tedious procedure. The goal was to develop fully automated techniques to reduce the manual effort in such a modeling process. Approach. A fully automated segmentation technique based on Statical Parametric Mapping 8, including an improved tissue probability map and an automated correction routine for segmentation errors, was developed, along with an automated electrode placement tool for high-density arrays. The performance of these automated routines was evaluated against results from manual segmentation on four healthy subjects and seven stroke patients. The criteria include segmentation accuracy, the difference of current flow distributions in resulting HD-tDCS models and the optimized current flow intensities on cortical targets.Main results. The segmentation tool can segment out not just the brain but also provide accurate results for CSF, skull and other soft tissues with a field of view extending to the neck. Compared to manual results, automated segmentation deviates by only 7% and 18% for normal and stroke subjects, respectively. The predicted electric fields in the brain deviate by 12% and 29% respectively, which is well within the variability observed for various modeling choices. Finally, optimized current flow intensities on cortical targets do not differ significantly.Significance. Fully

SHERPA: an image segmentation and outline feature extraction tool for diatoms and other objects.

PubMed

Kloster, Michael; Kauer, Gerhard; Beszteri, Bánk

2014-06-25

Light microscopic analysis of diatom frustules is widely used both in basic and applied research, notably taxonomy, morphometrics, water quality monitoring and paleo-environmental studies. In these applications, usually large numbers of frustules need to be identified and/or measured. Although there is a need for automation in these applications, and image processing and analysis methods supporting these tasks have previously been developed, they did not become widespread in diatom analysis. While methodological reports for a wide variety of methods for image segmentation, diatom identification and feature extraction are available, no single implementation combining a subset of these into a readily applicable workflow accessible to diatomists exists. The newly developed tool SHERPA offers a versatile image processing workflow focused on the identification and measurement of object outlines, handling all steps from image segmentation over object identification to feature extraction, and providing interactive functions for reviewing and revising results. Special attention was given to ease of use, applicability to a broad range of data and problems, and supporting high throughput analyses with minimal manual intervention. Tested with several diatom datasets from different sources and of various compositions, SHERPA proved its ability to successfully analyze large amounts of diatom micrographs depicting a broad range of species. SHERPA is unique in combining the following features: application of multiple segmentation methods and selection of the one giving the best result for each individual object; identification of shapes of interest based on outline matching against a template library; quality scoring and ranking of resulting outlines supporting quick quality checking; extraction of a wide range of outline shape descriptors widely used in diatom studies and elsewhere; minimizing the need for, but enabling manual quality control and corrections. Although primarily

Segmentation and object-oriented classification of wetlands in a karst Florida landscape using multi-season Landsat-7 ETM+ Imagery

EPA Science Inventory

Segmentation and object-oriented processing of single-season and multi-season Landsat-7 ETM+ data was utilized for the classification of wetlands in a 1560 km2 study area of north central Florida. This segmentation and object-oriented classification outperformed the traditional ...

Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks

PubMed Central

Ibragimov, Bulat; Xing, Lei

2017-01-01

Purpose Accurate segmentation of organs-at-risks (OARs) is the key step for efficient planning of radiation therapy for head and neck (HaN) cancer treatment. In the work, we proposed the first deep learning-based algorithm, for segmentation of OARs in HaN CT images, and compared its performance against state-of-the-art automated segmentation algorithms, commercial software and inter-observer variability. Methods Convolutional neural networks (CNNs) – a concept from the field of deep learning – were used to study consistent intensity patterns of OARs from training CT images and to segment the OAR in a previously unseen test CT image. For CNN training, we extracted a representative number of positive intensity patches around voxels that belong to the OAR of interest in training CT images, and negative intensity patches around voxels that belong to the surrounding structures. These patches then passed through a sequence of CNN layers that captured local image features such as corners, end-points and edges, and combined them into more complex high-order features that can efficiently describe the OAR. The trained network was applied to classify voxels in a region of interest in the test image where the corresponding OAR is expected to be located. We then smoothed the obtained classification results by using Markov random fields algorithm. We finally extracted the largest connected component of the smoothed voxels classified as the OAR by CNN, performed dilate-erode operations to remov cavities of the component, which resulted in segmentation of the OAR in the test image. Results The performance of CNNs was validated on segmentation of spinal cord, mandible, parotid glands, submandibular glands, larynx, pharynx, eye globes, optic nerves and optic chiasm using 50 CT images. The obtained segmentation results varied from 37.4% Dice coefficient (DSC) for chiasm to 89.5% DSC for mandible. We also analyzed the performance of state-of-the-art algorithms and commercial

Wavefront Compensation Segmented Mirror Sensing and Control

NASA Technical Reports Server (NTRS)

Redding, David C.; Lou, John Z.; Kissil, Andrew; Bradford, Charles M.; Woody, David; Padin, Stephen

2012-01-01

of optical edge sensors are used per segment-to-segment edge, separated by a finite distance along the segment edge, for four optical heads, each with an imager and a collimator. By orienting the beam direction of one edge sensor pair to be +45 away from the segment edge direction, and the other sensor pair to be oriented -45 away from the segment edge direction, all six degrees of freedom of relative motion between the segments can be measured with some redundancy. The software resides in a computer that receives each of the optical edge sensor signals, as well as telescope pointing commands. It feeds back the edge sensor signals to keep the primary mirror figure within specification. It uses a feed-forward control to compensate for global effects such as decollimation of the primary and secondary mirrors due to gravity sag as the telescope pointing changes to track science objects. Three segment position actuators will be provided per segment to enable controlled motions in the piston, tip, and tilt degrees of freedom. These actuators are driven by the software, providing the optical changes needed to keep the telescope phased.

Pondering the procephalon: the segmental origin of the labrum.

PubMed

Haas, M S; Brown, S J; Beeman, R W

2001-02-01

With accumulating evidence for the appendicular nature of the labrum, the question of its actual segmental origin remains. Two existing insect head segmentation models, the linear and S-models, are reviewed, and a new model introduced. The L-/Bent-Y model proposes that the labrum is a fusion of the appendage endites of the intercalary segment and that the stomodeum is tightly integrated into this segment. This model appears to explain a wider variety of insect head segmentation phenomena. Embryological, histological, neurological and molecular evidence supporting the new model is reviewed.

Evaluation of an automatic segmentation algorithm for definition of head and neck organs at risk.

PubMed

Thomson, David; Boylan, Chris; Liptrot, Tom; Aitkenhead, Adam; Lee, Lip; Yap, Beng; Sykes, Andrew; Rowbottom, Carl; Slevin, Nicholas

2014-08-03

The accurate definition of organs at risk (OARs) is required to fully exploit the benefits of intensity-modulated radiotherapy (IMRT) for head and neck cancer. However, manual delineation is time-consuming and there is considerable inter-observer variability. This is pertinent as function-sparing and adaptive IMRT have increased the number and frequency of delineation of OARs. We evaluated accuracy and potential time-saving of Smart Probabilistic Image Contouring Engine (SPICE) automatic segmentation to define OARs for salivary-, swallowing- and cochlea-sparing IMRT. Five clinicians recorded the time to delineate five organs at risk (parotid glands, submandibular glands, larynx, pharyngeal constrictor muscles and cochleae) for each of 10 CT scans. SPICE was then used to define these structures. The acceptability of SPICE contours was initially determined by visual inspection and the total time to modify them recorded per scan. The Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm created a reference standard from all clinician contours. Clinician, SPICE and modified contours were compared against STAPLE by the Dice similarity coefficient (DSC) and mean/maximum distance to agreement (DTA). For all investigated structures, SPICE contours were less accurate than manual contours. However, for parotid/submandibular glands they were acceptable (median DSC: 0.79/0.80; mean, maximum DTA: 1.5 mm, 14.8 mm/0.6 mm, 5.7 mm). Modified SPICE contours were also less accurate than manual contours. The utilisation of SPICE did not result in time-saving/improve efficiency. Improvements in accuracy of automatic segmentation for head and neck OARs would be worthwhile and are required before its routine clinical implementation.

Head and Eye Movements Affect Object Processing in 4-Month-Old Infants More than an Artificial Orientation Cue

ERIC Educational Resources Information Center

Wahl, Sebastian; Michel, Christine; Pauen, Sabina; Hoehl, Stefanie

2013-01-01

This study investigates the effects of attention-guiding stimuli on 4-month-old infants' object processing. In the human head condition, infants saw a person turning her head and eye gaze towards or away from objects. When presented with the objects again, infants showed increased attention in terms of longer looking time measured by eye…

Dynamic Parameter Identification of Subject-Specific Body Segment Parameters Using Robotics Formalism: Case Study Head Complex.

PubMed

Díaz-Rodríguez, Miguel; Valera, Angel; Page, Alvaro; Besa, Antonio; Mata, Vicente

2016-05-01

Accurate knowledge of body segment inertia parameters (BSIP) improves the assessment of dynamic analysis based on biomechanical models, which is of paramount importance in fields such as sport activities or impact crash test. Early approaches for BSIP identification rely on the experiments conducted on cadavers or through imaging techniques conducted on living subjects. Recent approaches for BSIP identification rely on inverse dynamic modeling. However, most of the approaches are focused on the entire body, and verification of BSIP for dynamic analysis for distal segment or chain of segments, which has proven to be of significant importance in impact test studies, is rarely established. Previous studies have suggested that BSIP should be obtained by using subject-specific identification techniques. To this end, our paper develops a novel approach for estimating subject-specific BSIP based on static and dynamics identification models (SIM, DIM). We test the validity of SIM and DIM by comparing the results using parameters obtained from a regression model proposed by De Leva (1996, "Adjustments to Zatsiorsky-Seluyanov's Segment Inertia Parameters," J. Biomech., 29(9), pp. 1223-1230). Both SIM and DIM are developed considering robotics formalism. First, the static model allows the mass and center of gravity (COG) to be estimated. Second, the results from the static model are included in the dynamics equation allowing us to estimate the moment of inertia (MOI). As a case study, we applied the approach to evaluate the dynamics modeling of the head complex. Findings provide some insight into the validity not only of the proposed method but also of the application proposed by De Leva (1996, "Adjustments to Zatsiorsky-Seluyanov's Segment Inertia Parameters," J. Biomech., 29(9), pp. 1223-1230) for dynamic modeling of body segments.

Graph cuts with invariant object-interaction priors: application to intervertebral disc segmentation.

PubMed

Ben Ayed, Ismail; Punithakumar, Kumaradevan; Garvin, Gregory; Romano, Walter; Li, Shuo

2011-01-01

This study investigates novel object-interaction priors for graph cut image segmentation with application to intervertebral disc delineation in magnetic resonance (MR) lumbar spine images. The algorithm optimizes an original cost function which constrains the solution with learned prior knowledge about the geometric interactions between different objects in the image. Based on a global measure of similarity between distributions, the proposed priors are intrinsically invariant with respect to translation and rotation. We further introduce a scale variable from which we derive an original fixed-point equation (FPE), thereby achieving scale-invariance with only few fast computations. The proposed priors relax the need of costly pose estimation (or registration) procedures and large training sets (we used a single subject for training), and can tolerate shape deformations, unlike template-based priors. Our formulation leads to an NP-hard problem which does not afford a form directly amenable to graph cut optimization. We proceeded to a relaxation of the problem via an auxiliary function, thereby obtaining a nearly real-time solution with few graph cuts. Quantitative evaluations over 60 intervertebral discs acquired from 10 subjects demonstrated that the proposed algorithm yields a high correlation with independent manual segmentations by an expert. We further demonstrate experimentally the invariance of the proposed geometric attributes. This supports the fact that a single subject is sufficient for training our algorithm, and confirms the relevance of the proposed priors to disc segmentation.

Segmentation in Tardigrada and diversification of segmental patterns in Panarthropoda.

PubMed

Smith, Frank W; Goldstein, Bob

2017-05-01

The origin and diversification of segmented metazoan body plans has fascinated biologists for over a century. The superphylum Panarthropoda includes three phyla of segmented animals-Euarthropoda, Onychophora, and Tardigrada. This superphylum includes representatives with relatively simple and representatives with relatively complex segmented body plans. At one extreme of this continuum, euarthropods exhibit an incredible diversity of serially homologous segments. Furthermore, distinct tagmosis patterns are exhibited by different classes of euarthropods. At the other extreme, all tardigrades share a simple segmented body plan that consists of a head and four leg-bearing segments. The modular body plans of panarthropods make them a tractable model for understanding diversification of animal body plans more generally. Here we review results of recent morphological and developmental studies of tardigrade segmentation. These results complement investigations of segmentation processes in other panarthropods and paleontological studies to illuminate the earliest steps in the evolution of panarthropod body plans. Copyright © 2016 Elsevier Ltd. All rights reserved.

SHERPA: an image segmentation and outline feature extraction tool for diatoms and other objects

PubMed Central

2014-01-01

Background Light microscopic analysis of diatom frustules is widely used both in basic and applied research, notably taxonomy, morphometrics, water quality monitoring and paleo-environmental studies. In these applications, usually large numbers of frustules need to be identified and/or measured. Although there is a need for automation in these applications, and image processing and analysis methods supporting these tasks have previously been developed, they did not become widespread in diatom analysis. While methodological reports for a wide variety of methods for image segmentation, diatom identification and feature extraction are available, no single implementation combining a subset of these into a readily applicable workflow accessible to diatomists exists. Results The newly developed tool SHERPA offers a versatile image processing workflow focused on the identification and measurement of object outlines, handling all steps from image segmentation over object identification to feature extraction, and providing interactive functions for reviewing and revising results. Special attention was given to ease of use, applicability to a broad range of data and problems, and supporting high throughput analyses with minimal manual intervention. Conclusions Tested with several diatom datasets from different sources and of various compositions, SHERPA proved its ability to successfully analyze large amounts of diatom micrographs depicting a broad range of species. SHERPA is unique in combining the following features: application of multiple segmentation methods and selection of the one giving the best result for each individual object; identification of shapes of interest based on outline matching against a template library; quality scoring and ranking of resulting outlines supporting quick quality checking; extraction of a wide range of outline shape descriptors widely used in diatom studies and elsewhere; minimizing the need for, but enabling manual quality control and

Validation of automatic landmark identification for atlas-based segmentation for radiation treatment planning of the head-and-neck region

NASA Astrophysics Data System (ADS)

Leavens, Claudia; Vik, Torbjørn; Schulz, Heinrich; Allaire, Stéphane; Kim, John; Dawson, Laura; O'Sullivan, Brian; Breen, Stephen; Jaffray, David; Pekar, Vladimir

2008-03-01

Manual contouring of target volumes and organs at risk in radiation therapy is extremely time-consuming, in particular for treating the head-and-neck area, where a single patient treatment plan can take several hours to contour. As radiation treatment delivery moves towards adaptive treatment, the need for more efficient segmentation techniques will increase. We are developing a method for automatic model-based segmentation of the head and neck. This process can be broken down into three main steps: i) automatic landmark identification in the image dataset of interest, ii) automatic landmark-based initialization of deformable surface models to the patient image dataset, and iii) adaptation of the deformable models to the patient-specific anatomical boundaries of interest. In this paper, we focus on the validation of the first step of this method, quantifying the results of our automatic landmark identification method. We use an image atlas formed by applying thin-plate spline (TPS) interpolation to ten atlas datasets, using 27 manually identified landmarks in each atlas/training dataset. The principal variation modes returned by principal component analysis (PCA) of the landmark positions were used by an automatic registration algorithm, which sought the corresponding landmarks in the clinical dataset of interest using a controlled random search algorithm. Applying a run time of 60 seconds to the random search, a root mean square (rms) distance to the ground-truth landmark position of 9.5 +/- 0.6 mm was calculated for the identified landmarks. Automatic segmentation of the brain, mandible and brain stem, using the detected landmarks, is demonstrated.

CT-based patient modeling for head and neck hyperthermia treatment planning: manual versus automatic normal-tissue-segmentation.

PubMed

Verhaart, René F; Fortunati, Valerio; Verduijn, Gerda M; van Walsum, Theo; Veenland, Jifke F; Paulides, Margarethus M

2014-04-01

Clinical trials have shown that hyperthermia, as adjuvant to radiotherapy and/or chemotherapy, improves treatment of patients with locally advanced or recurrent head and neck (H&N) carcinoma. Hyperthermia treatment planning (HTP) guided H&N hyperthermia is being investigated, which requires patient specific 3D patient models derived from Computed Tomography (CT)-images. To decide whether a recently developed automatic-segmentation algorithm can be introduced in the clinic, we compared the impact of manual- and automatic normal-tissue-segmentation variations on HTP quality. CT images of seven patients were segmented automatically and manually by four observers, to study inter-observer and intra-observer geometrical variation. To determine the impact of this variation on HTP quality, HTP was performed using the automatic and manual segmentation of each observer, for each patient. This impact was compared to other sources of patient model uncertainties, i.e. varying gridsizes and dielectric tissue properties. Despite geometrical variations, manual and automatic generated 3D patient models resulted in an equal, i.e. 1%, variation in HTP quality. This variation was minor with respect to the total of other sources of patient model uncertainties, i.e. 11.7%. Automatically generated 3D patient models can be introduced in the clinic for H&N HTP. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Cutting assembly including expanding wall segments of auger

DOEpatents

Treuhaft, Martin B.; Oser, Michael S.

1983-01-01

A mining auger comprises a cutting head carried at one end of a tubular shaft and a plurality of wall segments which in a first position thereof are disposed side by side around said shaft and in a second position thereof are disposed oblique to said shaft. A vane projects outwardly from each wall segment. When the wall segments are in their first position, the vanes together form a substantially continuous helical wall. A cutter is mounted on the peripheral edge of each of the vanes. When the wall segments are in their second position, the cutters on the vanes are disposed radially outward from the perimeter of the cutting head.

Automatic Cell Segmentation in Fluorescence Images of Confluent Cell Monolayers Using Multi-object Geometric Deformable Model.

PubMed

Yang, Zhen; Bogovic, John A; Carass, Aaron; Ye, Mao; Searson, Peter C; Prince, Jerry L

2013-03-13

With the rapid development of microscopy for cell imaging, there is a strong and growing demand for image analysis software to quantitatively study cell morphology. Automatic cell segmentation is an important step in image analysis. Despite substantial progress, there is still a need to improve the accuracy, efficiency, and adaptability to different cell morphologies. In this paper, we propose a fully automatic method for segmenting cells in fluorescence images of confluent cell monolayers. This method addresses several challenges through a combination of ideas. 1) It realizes a fully automatic segmentation process by first detecting the cell nuclei as initial seeds and then using a multi-object geometric deformable model (MGDM) for final segmentation. 2) To deal with different defects in the fluorescence images, the cell junctions are enhanced by applying an order-statistic filter and principal curvature based image operator. 3) The final segmentation using MGDM promotes robust and accurate segmentation results, and guarantees no overlaps and gaps between neighboring cells. The automatic segmentation results are compared with manually delineated cells, and the average Dice coefficient over all distinguishable cells is 0.88.

Multiple Semantic Matching on Augmented N-partite Graph for Object Co-segmentation.

PubMed

Wang, Chuan; Zhang, Hua; Yang, Liang; Cao, Xiaochun; Xiong, Hongkai

2017-09-08

Recent methods for object co-segmentation focus on discovering single co-occurring relation of candidate regions representing the foreground of multiple images. However, region extraction based only on low and middle level information often occupies a large area of background without the help of semantic context. In addition, seeking single matching solution very likely leads to discover local parts of common objects. To cope with these deficiencies, we present a new object cosegmentation framework, which takes advantages of semantic information and globally explores multiple co-occurring matching cliques based on an N-partite graph structure. To this end, we first propose to incorporate candidate generation with semantic context. Based on the regions extracted from semantic segmentation of each image, we design a merging mechanism to hierarchically generate candidates with high semantic responses. Secondly, all candidates are taken into consideration to globally formulate multiple maximum weighted matching cliques, which complements the discovery of part of the common objects induced by a single clique. To facilitate the discovery of multiple matching cliques, an N-partite graph, which inherently excludes intralinks between candidates from the same image, is constructed to separate multiple cliques without additional constraints. Further, we augment the graph with an additional virtual node in each part to handle irrelevant matches when the similarity between two candidates is too small. Finally, with the explored multiple cliques, we statistically compute pixel-wise co-occurrence map for each image. Experimental results on two benchmark datasets, i.e., iCoseg and MSRC datasets, achieve desirable performance and demonstrate the effectiveness of our proposed framework.

A Fully Automated Method to Detect and Segment a Manufactured Object in an Underwater Color Image

NASA Astrophysics Data System (ADS)

Barat, Christian; Phlypo, Ronald

2010-12-01

We propose a fully automated active contours-based method for the detection and the segmentation of a moored manufactured object in an underwater image. Detection of objects in underwater images is difficult due to the variable lighting conditions and shadows on the object. The proposed technique is based on the information contained in the color maps and uses the visual attention method, combined with a statistical approach for the detection and an active contour for the segmentation of the object to overcome the above problems. In the classical active contour method the region descriptor is fixed and the convergence of the method depends on the initialization. With our approach, this dependence is overcome with an initialization using the visual attention results and a criterion to select the best region descriptor. This approach improves the convergence and the processing time while providing the advantages of a fully automated method.

Object-based delineation and classification of alluvial fans by application of mean-shift segmentation and support vector machines

NASA Astrophysics Data System (ADS)

Pipaud, Isabel; Lehmkuhl, Frank

2017-09-01

In the field of geomorphology, automated extraction and classification of landforms is one of the most active research areas. Until the late 2000s, this task has primarily been tackled using pixel-based approaches. As these methods consider pixels and pixel neighborhoods as the sole basic entities for analysis, they cannot account for the irregular boundaries of real-world objects. Object-based analysis frameworks emerging from the field of remote sensing have been proposed as an alternative approach, and were successfully applied in case studies falling in the domains of both general and specific geomorphology. In this context, the a-priori selection of scale parameters or bandwidths is crucial for the segmentation result, because inappropriate parametrization will either result in over-segmentation or insufficient segmentation. In this study, we describe a novel supervised method for delineation and classification of alluvial fans, and assess its applicability using a SRTM 1‧‧ DEM scene depicting a section of the north-eastern Mongolian Altai, located in northwest Mongolia. The approach is premised on the application of mean-shift segmentation and the use of a one-class support vector machine (SVM) for classification. To consider variability in terms of alluvial fan dimension and shape, segmentation is performed repeatedly for different weightings of the incorporated morphometric parameters as well as different segmentation bandwidths. The final classification layer is obtained by selecting, for each real-world object, the most appropriate segmentation result according to fuzzy membership values derived from the SVM classification. Our results show that mean-shift segmentation and SVM-based classification provide an effective framework for delineation and classification of a particular landform. Variable bandwidths and terrain parameter weightings were identified as being crucial for consideration of intra-class variability, and, in turn, for a constantly

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.

Intuitive Terrain Reconstruction Using Height Observation-Based Ground Segmentation and 3D Object Boundary Estimation

PubMed Central

Song, Wei; Cho, Kyungeun; Um, Kyhyun; Won, Chee Sun; Sim, Sungdae

2012-01-01

Mobile robot operators must make rapid decisions based on information about the robot’s surrounding environment. This means that terrain modeling and photorealistic visualization are required for the remote operation of mobile robots. We have produced a voxel map and textured mesh from the 2D and 3D datasets collected by a robot’s array of sensors, but some upper parts of objects are beyond the sensors’ measurements and these parts are missing in the terrain reconstruction result. This result is an incomplete terrain model. To solve this problem, we present a new ground segmentation method to detect non-ground data in the reconstructed voxel map. Our method uses height histograms to estimate the ground height range, and a Gibbs-Markov random field model to refine the segmentation results. To reconstruct a complete terrain model of the 3D environment, we develop a 3D boundary estimation method for non-ground objects. We apply a boundary detection technique to the 2D image, before estimating and refining the actual height values of the non-ground vertices in the reconstructed textured mesh. Our proposed methods were tested in an outdoor environment in which trees and buildings were not completely sensed. Our results show that the time required for ground segmentation is faster than that for data sensing, which is necessary for a real-time approach. In addition, those parts of objects that were not sensed are accurately recovered to retrieve their real-world appearances. PMID:23235454

Intuitive terrain reconstruction using height observation-based ground segmentation and 3D object boundary estimation.

PubMed

Song, Wei; Cho, Kyungeun; Um, Kyhyun; Won, Chee Sun; Sim, Sungdae

2012-12-12

Mobile robot operators must make rapid decisions based on information about the robot's surrounding environment. This means that terrain modeling and photorealistic visualization are required for the remote operation of mobile robots. We have produced a voxel map and textured mesh from the 2D and 3D datasets collected by a robot's array of sensors, but some upper parts of objects are beyond the sensors' measurements and these parts are missing in the terrain reconstruction result. This result is an incomplete terrain model. To solve this problem, we present a new ground segmentation method to detect non-ground data in the reconstructed voxel map. Our method uses height histograms to estimate the ground height range, and a Gibbs-Markov random field model to refine the segmentation results. To reconstruct a complete terrain model of the 3D environment, we develop a 3D boundary estimation method for non-ground objects. We apply a boundary detection technique to the 2D image, before estimating and refining the actual height values of the non-ground vertices in the reconstructed textured mesh. Our proposed methods were tested in an outdoor environment in which trees and buildings were not completely sensed. Our results show that the time required for ground segmentation is faster than that for data sensing, which is necessary for a real-time approach. In addition, those parts of objects that were not sensed are accurately recovered to retrieve their real-world appearances.

Automatic segmentation of low-visibility moving objects through energy analyis of the local 3D spectrum

NASA Astrophysics Data System (ADS)

Nestares, Oscar; Miravet, Carlos; Santamaria, Javier; Fonolla Navarro, Rafael

1999-05-01

Automatic object segmentation in highly noisy image sequences, composed by a translating object over a background having a different motion, is achieved through joint motion-texture analysis. Local motion and/or texture is characterized by the energy of the local spatio-temporal spectrum, as different textures undergoing different translational motions display distinctive features in their 3D (x,y,t) spectra. Measurements of local spectrum energy are obtained using a bank of directional 3rd order Gaussian derivative filters in a multiresolution pyramid in space- time (10 directions, 3 resolution levels). These 30 energy measurements form a feature vector describing texture-motion for every pixel in the sequence. To improve discrimination capability and reduce computational cost, we automatically select those 4 features (channels) that best discriminate object from background, under the assumptions that the object is smaller than the background and has a different velocity or texture. In this way we reject features irrelevant or dominated by noise, that could yield wrong segmentation results. This method has been successfully applied to sequences with extremely low visibility and for objects that are even invisible for the eye in absence of motion.

Assessment of parental tooth-brushing following instruction with single-headed and triple-headed toothbrushes.

PubMed

Telishevesky, Yoel S; Levin, Liran; Ashkenazi, Malka

2012-01-01

The purpose of this study was to evaluate the effect of toothbrush design on the ability of parents to effectively brush their children's teeth. Parents of children (mean age=5.1±0.75 years old) from 4 kindergarten schools were randomly assigned to receive instruction in brushing their children's teeth using a manual single-headed toothbrush (2 schools) or a triple-headed toothbrush (2 schools). The parents' ability to brush their children's teeth was evaluated according to a novel toothbrush performing skill index (Ashkenazi Index), based on 2 criteria: (1) placement of the toothbrush on each tooth segment to be brushed ("reach"); and (2) completion of enough strokes on each segment ("stay"). One month after instruction, tooth-brushing ability was re-evaluated and plaque index of the children's teeth was assessed. One month after instruction, parents using the triple-headed toothbrush received significantly higher scores on the tooth-brushing performance index (~86%), than did those in the single-headed group (~61%; P=.001). The plaque index was significantly higher in the single-headed group (0.97±0.38) vs the triple-headed group (0.72±0.29; P<.01). The tooth-brushing performance index correlated negatively with the plaque index (P<.01). A triple-headed toothbrush promotes more consistent tooth-brushing by parents than does a single-headed toothbrush.

Computer object segmentation by nonlinear image enhancement, multidimensional clustering, and geometrically constrained contour optimization

NASA Astrophysics Data System (ADS)

Bruynooghe, Michel M.

1998-04-01

In this paper, we present a robust method for automatic object detection and delineation in noisy complex images. The proposed procedure is a three stage process that integrates image segmentation by multidimensional pixel clustering and geometrically constrained optimization of deformable contours. The first step is to enhance the original image by nonlinear unsharp masking. The second step is to segment the enhanced image by multidimensional pixel clustering, using our reducible neighborhoods clustering algorithm that has a very interesting theoretical maximal complexity. Then, candidate objects are extracted and initially delineated by an optimized region merging algorithm, that is based on ascendant hierarchical clustering with contiguity constraints and on the maximization of average contour gradients. The third step is to optimize the delineation of previously extracted and initially delineated objects. Deformable object contours have been modeled by cubic splines. An affine invariant has been used to control the undesired formation of cusps and loops. Non linear constrained optimization has been used to maximize the external energy. This avoids the difficult and non reproducible choice of regularization parameters, that are required by classical snake models. The proposed method has been applied successfully to the detection of fine and subtle microcalcifications in X-ray mammographic images, to defect detection by moire image analysis, and to the analysis of microrugosities of thin metallic films. The later implementation of the proposed method on a digital signal processor associated to a vector coprocessor would allow the design of a real-time object detection and delineation system for applications in medical imaging and in industrial computer vision.

Tape Placement Head for Applying Thermoplastic Tape to an Object

NASA Technical Reports Server (NTRS)

Cope, Ralph D. (Inventor); Funck, Steve B. (Inventor); Gruber, Mark B. (Inventor); Lamontia, Mark A. (Inventor); Johnson, Anthony D. (Inventor)

2008-01-01

A tape placement head for applying thermoplastic tape to an object includes a heated feeder which guides the tape/tow to a heated zone. The heated zone has a line compactor having a single row of at least one movable heated member. An area compactor is located in the heated zone downstream from the line compactor. The area compactor includes a plurality of rows of movable feet which are extendable toward the tape/tow different distances with respect to each other to conform to the shape of the object. A shim is located between the heated compactors and the tape/tow. A chilled compactor is in a chilled zone downstream from the heated zone. The chilled zone includes a line chilled compactor and an area chilled compactor. A chilled shim is mounted between the chilled compactor and the tape/tow.

Vessel segmentation in 3D spectral OCT scans of the retina

NASA Astrophysics Data System (ADS)

Niemeijer, Meindert; Garvin, Mona K.; van Ginneken, Bram; Sonka, Milan; Abràmoff, Michael D.

2008-03-01

The latest generation of spectral optical coherence tomography (OCT) scanners is able to image 3D cross-sectional volumes of the retina at a high resolution and high speed. These scans offer a detailed view of the structure of the retina. Automated segmentation of the vessels in these volumes may lead to more objective diagnosis of retinal vascular disease including hypertensive retinopathy, retinopathy of prematurity. Additionally, vessel segmentation can allow color fundus images to be registered to these 3D volumes, possibly leading to a better understanding of the structure and localization of retinal structures and lesions. In this paper we present a method for automatically segmenting the vessels in a 3D OCT volume. First, the retina is automatically segmented into multiple layers, using simultaneous segmentation of their boundary surfaces in 3D. Next, a 2D projection of the vessels is produced by only using information from certain segmented layers. Finally, a supervised, pixel classification based vessel segmentation approach is applied to the projection image. We compared the influence of two methods for the projection on the performance of the vessel segmentation on 10 optic nerve head centered 3D OCT scans. The method was trained on 5 independent scans. Using ROC analysis, our proposed vessel segmentation system obtains an area under the curve of 0.970 when compared with the segmentation of a human observer.

Optic Disc and Optic Cup Segmentation Methodologies for Glaucoma Image Detection: A Survey

PubMed Central

Almazroa, Ahmed; Burman, Ritambhar; Raahemifar, Kaamran; Lakshminarayanan, Vasudevan

2015-01-01

Glaucoma is the second leading cause of loss of vision in the world. Examining the head of optic nerve (cup-to-disc ratio) is very important for diagnosing glaucoma and for patient monitoring after diagnosis. Images of optic disc and optic cup are acquired by fundus camera as well as Optical Coherence Tomography. The optic disc and optic cup segmentation techniques are used to isolate the relevant parts of the retinal image and to calculate the cup-to-disc ratio. The main objective of this paper is to review segmentation methodologies and techniques for the disc and cup boundaries which are utilized to calculate the disc and cup geometrical parameters automatically and accurately to help the professionals in the glaucoma to have a wide view and more details about the optic nerve head structure using retinal fundus images. We provide a brief description of each technique, highlighting its classification and performance metrics. The current and future research directions are summarized and discussed. PMID:26688751

Resolving occlusion and segmentation errors in multiple video object tracking

NASA Astrophysics Data System (ADS)

Cheng, Hsu-Yung; Hwang, Jenq-Neng

2009-02-01

In this work, we propose a method to integrate the Kalman filter and adaptive particle sampling for multiple video object tracking. The proposed framework is able to detect occlusion and segmentation error cases and perform adaptive particle sampling for accurate measurement selection. Compared with traditional particle filter based tracking methods, the proposed method generates particles only when necessary. With the concept of adaptive particle sampling, we can avoid degeneracy problem because the sampling position and range are dynamically determined by parameters that are updated by Kalman filters. There is no need to spend time on processing particles with very small weights. The adaptive appearance for the occluded object refers to the prediction results of Kalman filters to determine the region that should be updated and avoids the problem of using inadequate information to update the appearance under occlusion cases. The experimental results have shown that a small number of particles are sufficient to achieve high positioning and scaling accuracy. Also, the employment of adaptive appearance substantially improves the positioning and scaling accuracy on the tracking results.

Contour-Driven Atlas-Based Segmentation

PubMed Central

Wachinger, Christian; Fritscher, Karl; Sharp, Greg; Golland, Polina

2016-01-01

We propose new methods for automatic segmentation of images based on an atlas of manually labeled scans and contours in the image. First, we introduce a Bayesian framework for creating initial label maps from manually annotated training images. Within this framework, we model various registration- and patch-based segmentation techniques by changing the deformation field prior. Second, we perform contour-driven regression on the created label maps to refine the segmentation. Image contours and image parcellations give rise to non-stationary kernel functions that model the relationship between image locations. Setting the kernel to the covariance function in a Gaussian process establishes a distribution over label maps supported by image structures. Maximum a posteriori estimation of the distribution over label maps conditioned on the outcome of the atlas-based segmentation yields the refined segmentation. We evaluate the segmentation in two clinical applications: the segmentation of parotid glands in head and neck CT scans and the segmentation of the left atrium in cardiac MR angiography images. PMID:26068202

An efficient fully unsupervised video object segmentation scheme using an adaptive neural-network classifier architecture.

PubMed

Doulamis, A; Doulamis, N; Ntalianis, K; Kollias, S

2003-01-01

In this paper, an unsupervised video object (VO) segmentation and tracking algorithm is proposed based on an adaptable neural-network architecture. The proposed scheme comprises: 1) a VO tracking module and 2) an initial VO estimation module. Object tracking is handled as a classification problem and implemented through an adaptive network classifier, which provides better results compared to conventional motion-based tracking algorithms. Network adaptation is accomplished through an efficient and cost effective weight updating algorithm, providing a minimum degradation of the previous network knowledge and taking into account the current content conditions. A retraining set is constructed and used for this purpose based on initial VO estimation results. Two different scenarios are investigated. The first concerns extraction of human entities in video conferencing applications, while the second exploits depth information to identify generic VOs in stereoscopic video sequences. Human face/ body detection based on Gaussian distributions is accomplished in the first scenario, while segmentation fusion is obtained using color and depth information in the second scenario. A decision mechanism is also incorporated to detect time instances for weight updating. Experimental results and comparisons indicate the good performance of the proposed scheme even in sequences with complicated content (object bending, occlusion).

A novel segmentation approach for implementation of MRAC in head PET/MRI employing Short-TE MRI and 2-point Dixon method in a fuzzy C-means framework

NASA Astrophysics Data System (ADS)

Khateri, Parisa; Rad, Hamidreza Saligheh; Jafari, Amir Homayoun; Ay, Mohammad Reza

2014-01-01

Quantitative PET image reconstruction requires an accurate map of attenuation coefficients of the tissue under investigation at 511 keV (μ-map), and in order to correct the emission data for attenuation. The use of MRI-based attenuation correction (MRAC) has recently received lots of attention in the scientific literature. One of the major difficulties facing MRAC has been observed in the areas where bone and air collide, e.g. ethmoidal sinuses in the head area. Bone is intrinsically not detectable by conventional MRI, making it difficult to distinguish air from bone. Therefore, development of more versatile MR sequences to label the bone structure, e.g. ultra-short echo-time (UTE) sequences, certainly plays a significant role in novel methodological developments. However, long acquisition time and complexity of UTE sequences limit its clinical applications. To overcome this problem, we developed a novel combination of Short-TE (ShTE) pulse sequence to detect bone signal with a 2-point Dixon technique for water-fat discrimination, along with a robust image segmentation method based on fuzzy clustering C-means (FCM) to segment the head area into four classes of air, bone, soft tissue and adipose tissue. The imaging protocol was set on a clinical 3 T Tim Trio and also 1.5 T Avanto (Siemens Medical Solution, Erlangen, Germany) employing a triple echo time pulse sequence in the head area. The acquisition parameters were as follows: TE1/TE2/TE3=0.98/4.925/6.155 ms, TR=8 ms, FA=25 on the 3 T system, and TE1/TE2/TE3=1.1/2.38/4.76 ms, TR=16 ms, FA=18 for the 1.5 T system. The second and third echo-times belonged to the Dixon decomposition to distinguish soft and adipose tissues. To quantify accuracy, sensitivity and specificity of the bone segmentation algorithm, resulting classes of MR-based segmented bone were compared with the manual segmented one by our expert neuro-radiologist. Results for both 3 T and 1.5 T systems show that bone segmentation applied in several

Development of head and trunk mesoderm in the dogfish, Scyliorhinus torazame: I. Embryology and morphology of the head cavities and related structures.

PubMed

Adachi, Noritaka; Kuratani, Shigeru

2012-01-01

Vertebrate head segmentation has attracted the attention of comparative and evolutionary morphologists for centuries, given its importance for understanding the developmental body plan of vertebrates and its evolutionary origin. In particular, the segmentation of the mesoderm is central to the problem. The shark embryo has provided a canonical morphological scheme of the head, with its epithelialized coelomic cavities (head cavities), which have often been regarded as head somites. To understand the evolutionary significance of the head cavities, the embryonic development of the mesoderm was investigated at the morphological and histological levels in the shark, Scyliorhinus torazame. Unlike somites and some enterocoelic mesodermal components in other vertebrates, the head cavities in S. torazame appeared as irregular cyst(s) in the originally unsegmented mesenchymal head mesoderm, and not via segmentation of an undivided coelom. The mandibular cavity appeared first in the paraxial part of the mandibular mesoderm, followed by the hyoid cavity, and the premandibular cavity was the last to form. The prechordal plate was recognized as a rhomboid roof of the preoral gut, continuous with the rostral notochord, and was divided anteroposteriorly into two parts by the growth of the hypothalamic primordium. Of those, the posterior part was likely to differentiate into the premandibular cavity, and the anterior part disappeared later. The head cavities and somites in the trunk exhibited significant differences, in terms of histological appearance and timing of differentiation. The mandibular cavity developed a rostral process secondarily; its homology to the anterior cavity reported in some elasmobranch embryos is discussed. © 2012 Wiley Periodicals, Inc.

Chin plate with a detachable C-tube head serves for both osteotomy fixation and orthodontic anchorage.

PubMed

Seo, Kyung-Won; Nahm, Kyung-Yen; Kim, Seong-Hun; Chung, Kyu-Rhim; Nelson, Gerald

2013-07-01

This article reports the dual function of a double-Y miniplate with a detachable C-tube head (C-chin plate; Jin Biomed Co., Bucheon, Korea) used to fixate an anterior segmental osteotomy and provide skeletal anchorage during orthodontic tooth movement. Cases were selected for this study from patients who underwent anterior segmental osteotomy under local anesthesia. A detachable C-tube head portion was combined with a double-Y chin plate. The double-Y chin plates were fixated between the osteotomy segments and the mandibular base with screws in a conventional way. The C-tube head portion exited the tissue near the mucogingival junction. Biocreative Chin Plates were placed on the anterior segmental osteotomy sites. The device allowed 3 points of fixation: 1, minor postosteotomy vertical adjustment of the segment during healing; 2, minor shift of the midline during healing; and 3, to serve as temporary skeletal anchorage device during the post-anterior segmental osteotomy orthodontic treatment. When tooth movement goals are accomplished, the C-tube head of the chin plate can be easily detached from the fixation miniplate by twisting the head using a Weingart plier under local anesthesia. This dual-purpose device spares the patient from the need for 2 separate installations for stabilization of osteotomy segments. The dual-purpose double-Y miniplate combined with a C-tube head (Biocreative Chin Plate) provided versatile application of 3 points of post-osteotomy fixation and of temporary skeletal anchorage for orthodontic tooth movement.

Why Do We Move Our Head to Look at an Object in Our Peripheral Region? Lateral Viewing Interferes with Attentive Search

PubMed Central

Nakashima, Ryoichi; Shioiri, Satoshi

2014-01-01

Why do we frequently fixate an object of interest presented peripherally by moving our head as well as our eyes, even when we are capable of fixating the object with an eye movement alone (lateral viewing)? Studies of eye-head coordination for gaze shifts have suggested that the degree of eye-head coupling could be determined by an unconscious weighing of the motor costs and benefits of executing a head movement. The present study investigated visual perceptual effects of head direction as an additional factor impacting on a cost-benefit organization of eye-head control. Three experiments using visual search tasks were conducted, manipulating eye direction relative to head orientation (front or lateral viewing). Results show that lateral viewing increased the time required to detect a target in a search for the letter T among letter L distractors, a serial attentive search task, but not in a search for T among letter O distractors, a parallel preattentive search task (Experiment 1). The interference could not be attributed to either a deleterious effect of lateral gaze on the accuracy of saccadic eye movements, nor to potentially problematic optical effects of binocular lateral viewing, because effect of head directions was obtained under conditions in which the task was accomplished without saccades (Experiment 2), and during monocular viewing (Experiment 3). These results suggest that a difference between the head and eye directions interferes with visual processing, and that the interference can be explained by the modulation of attention by the relative positions of the eyes and head (or head direction). PMID:24647634

Origin and evolution of the panarthropod head - A palaeobiological and developmental perspective.

PubMed

Ortega-Hernández, Javier; Janssen, Ralf; Budd, Graham E

2017-05-01

The panarthropod head represents a complex body region that has evolved through the integration and functional specialization of the anterior appendage-bearing segments. Advances in the developmental biology of diverse extant organisms have led to a substantial clarity regarding the relationships of segmental homology between Onychophora (velvet worms), Tardigrada (water bears), and Euarthropoda (e.g. arachnids, myriapods, crustaceans, hexapods). The improved understanding of the segmental organization in panarthropods offers a novel perspective for interpreting the ubiquitous Cambrian fossil record of these successful animals. A combined palaeobiological and developmental approach to the study of the panarthropod head through deep time leads us to propose a consensus hypothesis for the intricate evolutionary history of this important tagma. The contribution of exceptionally preserved brains in Cambrian fossils - together with the recognition of segmentally informative morphological characters - illuminate the polarity for major anatomical features. The euarthropod stem-lineage provides a detailed view of the step-wise acquisition of critical characters, including the origin of a multiappendicular head formed by the fusion of several segments, and the transformation of the ancestral protocerebral limb pair into the labrum, following the postero-ventral migration of the mouth opening. Stem-group onychophorans demonstrate an independent ventral migration of the mouth and development of a multisegmented head, as well as the differentiation of the deutocerebral limbs as expressed in extant representatives. The anterior organization of crown-group Tardigrada retains several ancestral features, such as an anterior-facing mouth and one-segmented head. The proposed model aims to clarify contentious issues on the evolution of the panarthropod head, and lays the foundation from which to further address this complex subject in the future. Copyright © 2016 Elsevier Ltd. All

Objective classification of different head and neck positions and their influence on the radiographic pharyngeal diameter in sport horses

PubMed Central

2014-01-01

Background Various head and neck positions in sport horses are significant as they can interfere with upper airway flow mechanics during exercise. Until now, research has focused on subjectively described head and neck positions. The objective of this study was to develop an objective, reproducible method for quantifying head and neck positions accurately. Results Determining the angle between the ridge of the nose and the horizontal plane (ground angle) together with the angle between the ridge of nose and the line connecting the neck and the withers (withers angle) has provided values that allow precise identification of three preselected head and neck positions for performing sport horses. The pharyngeal diameter, determined on lateral radiographs of 35 horses, differed significantly between the established flexed position and the remaining two head and neck positions (extended and neutral). There was a significant correlation between the pharyngeal diameter and the ground angle (Spearman’s rank correlation coefficient −0.769, p < 0.01) as well as between the pharyngeal diameter and the withers angle (Spearman’s rank correlation coefficient 0.774, p < 0.01). Conclusion The combination of the ground angle and the withers angle is a suitable tool for evaluating and distinguishing frequently used head and neck positions in sport horses. The ground angle and the withers angle show significant correlation with the measured pharyngeal diameter in resting horses. Hence, these angles provide an appropriate method for assessing the degree of head and neck flexion. Further research is required to examine the influence of increasing head and neck flexion and the related pharyngeal diameter on upper airway function in exercising horses. PMID:24886564

Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem.

PubMed

Wang, Jun Yi; Ngo, Michael M; Hessl, David; Hagerman, Randi J; Rivera, Susan M

2016-01-01

Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate whether SegAdapter, a machine learning-based method, is useful for automatically correcting large segmentation errors and disagreement in anatomical definition. We further assessed the robustness of the method in handling size of training set, differences in head coil usage, and amount of brain atrophy. High resolution T1-weighted images were acquired from 30 healthy controls scanned with either an 8-channel or 32-channel head coil. Ten patients, who suffered from brain atrophy because of fragile X-associated tremor/ataxia syndrome, were scanned using the 32-channel head coil. The initial segmentations of the cerebellum and brainstem were generated automatically using Freesurfer. Subsequently, Freesurfer's segmentations were both manually corrected to serve as the gold standard and automatically corrected by SegAdapter. Using only 5 scans in the training set, spatial overlap with manual segmentation in Dice coefficient improved significantly from 0.956 (for Freesurfer segmentation) to 0.978 (for SegAdapter-corrected segmentation) for the cerebellum and from 0.821 to 0.954 for the brainstem. Reducing the training set size to 2 scans only decreased the Dice coefficient ≤0.002 for the cerebellum and ≤ 0.005 for the brainstem compared to the use of training set size of 5 scans in corrective learning. The method was also robust in handling differences between the training set and the test set in head coil usage and the amount of brain atrophy, which reduced spatial overlap only by <0.01. These results suggest that the combination of automated segmentation and corrective learning provides a valuable method for accurate and efficient segmentation of the cerebellum and brainstem, particularly in large-scale neuroimaging

Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem

PubMed Central

Wang, Jun Yi; Ngo, Michael M.; Hessl, David; Hagerman, Randi J.; Rivera, Susan M.

2016-01-01

Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate whether SegAdapter, a machine learning-based method, is useful for automatically correcting large segmentation errors and disagreement in anatomical definition. We further assessed the robustness of the method in handling size of training set, differences in head coil usage, and amount of brain atrophy. High resolution T1-weighted images were acquired from 30 healthy controls scanned with either an 8-channel or 32-channel head coil. Ten patients, who suffered from brain atrophy because of fragile X-associated tremor/ataxia syndrome, were scanned using the 32-channel head coil. The initial segmentations of the cerebellum and brainstem were generated automatically using Freesurfer. Subsequently, Freesurfer’s segmentations were both manually corrected to serve as the gold standard and automatically corrected by SegAdapter. Using only 5 scans in the training set, spatial overlap with manual segmentation in Dice coefficient improved significantly from 0.956 (for Freesurfer segmentation) to 0.978 (for SegAdapter-corrected segmentation) for the cerebellum and from 0.821 to 0.954 for the brainstem. Reducing the training set size to 2 scans only decreased the Dice coefficient ≤0.002 for the cerebellum and ≤ 0.005 for the brainstem compared to the use of training set size of 5 scans in corrective learning. The method was also robust in handling differences between the training set and the test set in head coil usage and the amount of brain atrophy, which reduced spatial overlap only by <0.01. These results suggest that the combination of automated segmentation and corrective learning provides a valuable method for accurate and efficient segmentation of the cerebellum and brainstem, particularly in large-scale neuroimaging

Allocation of Home Office Expenses to Segments and Business Unit General and Administrative Expenses to Final Cost Objectives

DTIC Science & Technology

1992-02-16

3 0 B. Cost Accounting Standard 418 ..................................................... 3 1 1. D efinitio n s ...objective" as an activity for which a separate measurement of cost is desired. C. Horngren , Cost Accounting . A Managerial Emphasis 21 (5th ed. 1982...Segments and Business Unit General and Administrative Expenses to Final Cost Objectives 6. AUTHOR( S ) Stephen Thomas Lynch, Major 7. PERFORMING

Image Segmentation Using Minimum Spanning Tree

NASA Astrophysics Data System (ADS)

Dewi, M. P.; Armiati, A.; Alvini, S.

2018-04-01

This research aim to segmented the digital image. The process of segmentation is to separate the object from the background. So the main object can be processed for the other purposes. Along with the development of technology in digital image processing application, the segmentation process becomes increasingly necessary. The segmented image which is the result of the segmentation process should accurate due to the next process need the interpretation of the information on the image. This article discussed the application of minimum spanning tree on graph in segmentation process of digital image. This method is able to separate an object from the background and the image will change to be the binary images. In this case, the object that being the focus is set in white, while the background is black or otherwise.

Vestibulospinal control of reflex and voluntary head movement

NASA Technical Reports Server (NTRS)

Boyle, R.; Peterson, B. W. (Principal Investigator)

2001-01-01

Secondary canal-related vestibulospinal neurons respond to an externally applied movement of the head in the form of a firing rate modulation that encodes the angular velocity of the movement, and reflects in large part the input "head velocity in space" signal carried by the semicircular canal afferents. In addition to the head velocity signal, the vestibulospinal neurons can carry a more processed signal that includes eye position or eye velocity, or both (see Boyle on ref. list). To understand the control signals used by the central vestibular pathways in the generation of reflex head stabilization, such as the vestibulocollic reflex (VCR), and the maintenance of head posture, it is essential to record directly from identified vestibulospinal neurons projecting to the cervical spinal segments in the alert animal. The present report discusses two key features of the primate vestibulospinal system. First, the termination morphology of vestibulospinal axons in the cervical segments of the spinal cord is described to lay the structural basis of vestibulospinal control of head/neck posture and movement. And second, the head movement signal content carried by the same class of secondary vestibulospinal neurons during the actual execution of the VCR and during self-generated, or active, rapid head movements is presented.

Comparison of five segmentation tools for 18F-fluoro-deoxy-glucose-positron emission tomography-based target volume definition in head and neck cancer.

PubMed

Schinagl, Dominic A X; Vogel, Wouter V; Hoffmann, Aswin L; van Dalen, Jorn A; Oyen, Wim J; Kaanders, Johannes H A M

2007-11-15

Target-volume delineation for radiation treatment to the head and neck area traditionally is based on physical examination, computed tomography (CT), and magnetic resonance imaging. Additional molecular imaging with (18)F-fluoro-deoxy-glucose (FDG)-positron emission tomography (PET) may improve definition of the gross tumor volume (GTV). In this study, five methods for tumor delineation on FDG-PET are compared with CT-based delineation. Seventy-eight patients with Stages II-IV squamous cell carcinoma of the head and neck area underwent coregistered CT and FDG-PET. The primary tumor was delineated on CT, and five PET-based GTVs were obtained: visual interpretation, applying an isocontour of a standardized uptake value of 2.5, using a fixed threshold of 40% and 50% of the maximum signal intensity, and applying an adaptive threshold based on the signal-to-background ratio. Absolute GTV volumes were compared, and overlap analyses were performed. The GTV method of applying an isocontour of a standardized uptake value of 2.5 failed to provide successful delineation in 45% of cases. For the other PET delineation methods, volume and shape of the GTV were influenced heavily by the choice of segmentation tool. On average, all threshold-based PET-GTVs were smaller than on CT. Nevertheless, PET frequently detected significant tumor extension outside the GTV delineated on CT (15-34% of PET volume). The choice of segmentation tool for target-volume definition of head and neck cancer based on FDG-PET images is not trivial because it influences both volume and shape of the resulting GTV. With adequate delineation, PET may add significantly to CT- and physical examination-based GTV definition.

A 3D interactive multi-object segmentation tool using local robust statistics driven active contours.

PubMed

Gao, Yi; Kikinis, Ron; Bouix, Sylvain; Shenton, Martha; Tannenbaum, Allen

2012-08-01

Extracting anatomical and functional significant structures renders one of the important tasks for both the theoretical study of the medical image analysis, and the clinical and practical community. In the past, much work has been dedicated only to the algorithmic development. Nevertheless, for clinical end users, a well designed algorithm with an interactive software is necessary for an algorithm to be utilized in their daily work. Furthermore, the software would better be open sourced in order to be used and validated by not only the authors but also the entire community. Therefore, the contribution of the present work is twofolds: first, we propose a new robust statistics based conformal metric and the conformal area driven multiple active contour framework, to simultaneously extract multiple targets from MR and CT medical imagery in 3D. Second, an open source graphically interactive 3D segmentation tool based on the aforementioned contour evolution is implemented and is publicly available for end users on multiple platforms. In using this software for the segmentation task, the process is initiated by the user drawn strokes (seeds) in the target region in the image. Then, the local robust statistics are used to describe the object features, and such features are learned adaptively from the seeds under a non-parametric estimation scheme. Subsequently, several active contours evolve simultaneously with their interactions being motivated by the principles of action and reaction-this not only guarantees mutual exclusiveness among the contours, but also no longer relies upon the assumption that the multiple objects fill the entire image domain, which was tacitly or explicitly assumed in many previous works. In doing so, the contours interact and converge to equilibrium at the desired positions of the desired multiple objects. Furthermore, with the aim of not only validating the algorithm and the software, but also demonstrating how the tool is to be used, we provide

A 3D Interactive Multi-object Segmentation Tool using Local Robust Statistics Driven Active Contours

PubMed Central

Gao, Yi; Kikinis, Ron; Bouix, Sylvain; Shenton, Martha; Tannenbaum, Allen

2012-01-01

Extracting anatomical and functional significant structures renders one of the important tasks for both the theoretical study of the medical image analysis, and the clinical and practical community. In the past, much work has been dedicated only to the algorithmic development. Nevertheless, for clinical end users, a well designed algorithm with an interactive software is necessary for an algorithm to be utilized in their daily work. Furthermore, the software would better be open sourced in order to be used and validated by not only the authors but also the entire community. Therefore, the contribution of the present work is twofolds: First, we propose a new robust statistics based conformal metric and the conformal area driven multiple active contour framework, to simultaneously extract multiple targets from MR and CT medical imagery in 3D. Second, an open source graphically interactive 3D segmentation tool based on the aforementioned contour evolution is implemented and is publicly available for end users on multiple platforms. In using this software for the segmentation task, the process is initiated by the user drawn strokes (seeds) in the target region in the image. Then, the local robust statistics are used to describe the object features, and such features are learned adaptively from the seeds under a non-parametric estimation scheme. Subsequently, several active contours evolve simultaneously with their interactions being motivated by the principles of action and reaction — This not only guarantees mutual exclusiveness among the contours, but also no longer relies upon the assumption that the multiple objects fill the entire image domain, which was tacitly or explicitly assumed in many previous works. In doing so, the contours interact and converge to equilibrium at the desired positions of the desired multiple objects. Furthermore, with the aim of not only validating the algorithm and the software, but also demonstrating how the tool is to be used, we

Analysis and segmentation of images in case of solving problems of detecting and tracing objects on real-time video

NASA Astrophysics Data System (ADS)

Ezhova, Kseniia; Fedorenko, Dmitriy; Chuhlamov, Anton

2016-04-01

The article deals with the methods of image segmentation based on color space conversion, and allow the most efficient way to carry out the detection of a single color in a complex background and lighting, as well as detection of objects on a homogeneous background. The results of the analysis of segmentation algorithms of this type, the possibility of their implementation for creating software. The implemented algorithm is very time-consuming counting, making it a limited application for the analysis of the video, however, it allows us to solve the problem of analysis of objects in the image if there is no dictionary of images and knowledge bases, as well as the problem of choosing the optimal parameters of the frame quantization for video analysis.

SKIPing With Head Start Teachers: Influence of T-SKIP on Object-Control Skills.

PubMed

Brian, Ali; Goodway, Jacqueline D; Logan, Jessica A; Sutherland, Sue

2017-12-01

Children from disadvantaged settings are at risk for delays in their object-control (OC) skills. Fundamental motor skill interventions, such as the Successful Kinesthetic Instruction for Preschoolers (SKIP) Program, are highly successful when led by motor development experts. However, few preschools employ such experts. This study examined the extent to which Head Start teachers delivering an 8-week teacher-led SKIP (T-SKIP) intervention elicited learning of OC skills for Head Start children. Head Start teachers (n = 5) delivered T-SKIP for 8 weeks (450 min). Control teachers (n = 5) implemented the typical standard of practice, or well-equipped free play. All children (N = 122) were pretested and posttested on the OC Skill subscale of the Test of Gross Motor Development-2. Descriptive analyses at pretest identified 81% of the children were developmentally delayed in OC skills (below the 30th percentile). A 2-level hierarchical linear model demonstrated the effectiveness of T-SKIP with significant differences (β = 4.70), t(8) = 7.02, p < .001, η2 = .56, between T-SKIP children (n = 63) and control children (n = 59) at posttest. Head Start teachers who delivered T-SKIP could bring about positive changes in children's OC skills, thereby remediating the initial developmental delays presented. Control children remained delayed in their OC skills in spite of daily well-equipped free play, giving rise to concerns about their future motor competence and physical activity levels.

SU-C-BRA-04: Automated Segmentation of Head-And-Neck CT Images for Radiotherapy Treatment Planning Via Multi-Atlas Machine Learning (MAML)

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

Ren, X; Gao, H; Sharp, G

Purpose: Accurate image segmentation is a crucial step during image guided radiation therapy. This work proposes multi-atlas machine learning (MAML) algorithm for automated segmentation of head-and-neck CT images. Methods: As the first step, the algorithm utilizes normalized mutual information as similarity metric, affine registration combined with multiresolution B-Spline registration, and then fuses together using the label fusion strategy via Plastimatch. As the second step, the following feature selection strategy is proposed to extract five feature components from reference or atlas images: intensity (I), distance map (D), box (B), center of gravity (C) and stable point (S). The box feature Bmore » is novel. It describes a relative position from each point to minimum inscribed rectangle of ROI. The center-of-gravity feature C is the 3D Euclidean distance from a sample point to the ROI center of gravity, and then S is the distance of the sample point to the landmarks. Then, we adopt random forest (RF) in Scikit-learn, a Python module integrating a wide range of state-of-the-art machine learning algorithms as classifier. Different feature and atlas strategies are used for different ROIs for improved performance, such as multi-atlas strategy with reference box for brainstem, and single-atlas strategy with reference landmark for optic chiasm. Results: The algorithm was validated on a set of 33 CT images with manual contours using a leave-one-out cross-validation strategy. Dice similarity coefficients between manual contours and automated contours were calculated: the proposed MAML method had an improvement from 0.79 to 0.83 for brainstem and 0.11 to 0.52 for optic chiasm with respect to multi-atlas segmentation method (MA). Conclusion: A MAML method has been proposed for automated segmentation of head-and-neck CT images with improved performance. It provides the comparable result in brainstem and the improved result in optic chiasm compared with MA. Xuhua Ren

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

Geometric and dosimetric evaluations of atlas-based segmentation methods of MR images in the head and neck region.

PubMed

Kieselmann, Jennifer Petra; Kamerling, Cornelis Philippus; Burgos, Ninon; Menten, Martin J; Fuller, Clifton David; Nill, Simeon; Cardoso, M Jorge; Oelfke, Uwe

2018-06-08

Owing to its excellent soft-tissue contrast, magnetic resonance (MR) imaging has found an increased application in radiation therapy (RT). Harnessing these properties for treatment planning, automated segmentation methods can alleviate the manual workload burden to the clinical workflow. We investigated atlas-based segmentation methods of organs at risk (OARs) in the head and neck (H&N) region: one approach selecting the most similar atlas from a library of segmented images and two multi-atlas approaches. The latter were based on weighted majority voting and an iterative atlas-fusion approach called STEPS. We built the atlas library from pre-treatment T1-weighted MR images of 12 patients with manual contours of the parotids, spinal cord and mandible, delineated by a clinician. Following a leave-one-out cross-validation strategy, we measured geometric accuracy calculating Dice similarity coefficients (DSC), standard and 95% Hausdorff distances (HD and HD95), as well as the mean surface distance (MSD), whereby the manual contours served as the gold standard. To benchmark the algorithm, we determined the inter-expert variability (IEV) between three experts. To investigate the dosimetric effect of segmentation inaccuracies, we implemented an auto-planning strategy within the treatment planning system Monaco (Elekta AB, Stockholm, Sweden). For each set of auto-segmented volumes of interest (VOIs), we generated a plan for a 9-beam step and shoot intensity modulated RT treatment, designed according to our institution's clinical H\\&N protocol. Superimposing the dose distributions on the gold standard VOIs, we calculated dose differences to OARs caused by contouring differences between auto-segmented and gold standard VOIs. We investigated the correlation between geometric and dosimetric differences. The mean DSC was larger than 0.8 and the mean MSD smaller than 2mm for the multi-atlas approaches, resulting in a geometric

Multi-scale hippocampal parcellation improves atlas-based segmentation accuracy

NASA Astrophysics Data System (ADS)

Plassard, Andrew J.; McHugo, Maureen; Heckers, Stephan; Landman, Bennett A.

2017-02-01

Known for its distinct role in memory, the hippocampus is one of the most studied regions of the brain. Recent advances in magnetic resonance imaging have allowed for high-contrast, reproducible imaging of the hippocampus. Typically, a trained rater takes 45 minutes to manually trace the hippocampus and delineate the anterior from the posterior segment at millimeter resolution. As a result, there has been a significant desire for automated and robust segmentation of the hippocampus. In this work we use a population of 195 atlases based on T1-weighted MR images with the left and right hippocampus delineated into the head and body. We initialize the multi-atlas segmentation to a region directly around each lateralized hippocampus to both speed up and improve the accuracy of registration. This initialization allows for incorporation of nearly 200 atlases, an accomplishment which would typically involve hundreds of hours of computation per target image. The proposed segmentation results in a Dice similiarity coefficient over 0.9 for the full hippocampus. This result outperforms a multi-atlas segmentation using the BrainCOLOR atlases (Dice 0.85) and FreeSurfer (Dice 0.75). Furthermore, the head and body delineation resulted in a Dice coefficient over 0.87 for both structures. The head and body volume measurements also show high reproducibility on the Kirby 21 reproducibility population (R2 greater than 0.95, p < 0.05 for all structures). This work signifies the first result in an ongoing work to develop a robust tool for measurement of the hippocampus and other temporal lobe structures.

Automated segmentation and tracking of non-rigid objects in time-lapse microscopy videos of polymorphonuclear neutrophils.

PubMed

Brandes, Susanne; Mokhtari, Zeinab; Essig, Fabian; Hünniger, Kerstin; Kurzai, Oliver; Figge, Marc Thilo

2015-02-01

Time-lapse microscopy is an important technique to study the dynamics of various biological processes. The labor-intensive manual analysis of microscopy videos is increasingly replaced by automated segmentation and tracking methods. These methods are often limited to certain cell morphologies and/or cell stainings. In this paper, we present an automated segmentation and tracking framework that does not have these restrictions. In particular, our framework handles highly variable cell shapes and does not rely on any cell stainings. Our segmentation approach is based on a combination of spatial and temporal image variations to detect moving cells in microscopy videos. This method yields a sensitivity of 99% and a precision of 95% in object detection. The tracking of cells consists of different steps, starting from single-cell tracking based on a nearest-neighbor-approach, detection of cell-cell interactions and splitting of cell clusters, and finally combining tracklets using methods from graph theory. The segmentation and tracking framework was applied to synthetic as well as experimental datasets with varying cell densities implying different numbers of cell-cell interactions. We established a validation framework to measure the performance of our tracking technique. The cell tracking accuracy was found to be >99% for all datasets indicating a high accuracy for connecting the detected cells between different time points. Copyright © 2014 Elsevier B.V. All rights reserved.

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-Scale and Object-Oriented Analysis for Mountain Terrain Segmentation and Geomorphological Assessment

NASA Astrophysics Data System (ADS)

Marston, B. K.; Bishop, M. P.; Shroder, J. F.

2009-12-01

Digital terrain analysis of mountain topography is widely utilized for mapping landforms, assessing the role of surface processes in landscape evolution, and estimating the spatial variation of erosion. Numerous geomorphometry techniques exist to characterize terrain surface parameters, although their utility to characterize the spatial hierarchical structure of the topography and permit an assessment of the erosion/tectonic impact on the landscape is very limited due to scale and data integration issues. To address this problem, we apply scale-dependent geomorphometric and object-oriented analyses to characterize the hierarchical spatial structure of mountain topography. Specifically, we utilized a high resolution digital elevation model to characterize complex topography in the Shimshal Valley in the Western Himalaya of Pakistan. To accomplish this, we generate terrain objects (geomorphological features and landform) including valley floors and walls, drainage basins, drainage network, ridge network, slope facets, and elemental forms based upon curvature. Object-oriented analysis was used to characterize object properties accounting for object size, shape, and morphometry. The spatial overlay and integration of terrain objects at various scales defines the nature of the hierarchical organization. Our results indicate that variations in the spatial complexity of the terrain hierarchical organization is related to the spatio-temporal influence of surface processes and landscape evolution dynamics. Terrain segmentation and the integration of multi-scale terrain information permits further assessment of process domains and erosion, tectonic impact potential, and natural hazard potential. We demonstrate this with landform mapping and geomorphological assessment examples.

Comparison of using single- or multi-polarimetric TerraSAR-X images for segmentation and classification of man-made maritime objects

NASA Astrophysics Data System (ADS)

Teutsch, Michael; Saur, Günter

2011-11-01

Spaceborne SAR imagery offers high capability for wide-ranging maritime surveillance especially in situations, where AIS (Automatic Identification System) data is not available. Therefore, maritime objects have to be detected and optional information such as size, orientation, or object/ship class is desired. In recent research work, we proposed a SAR processing chain consisting of pre-processing, detection, segmentation, and classification for single-polarimetric (HH) TerraSAR-X StripMap images to finally assign detection hypotheses to class "clutter", "non-ship", "unstructured ship", or "ship structure 1" (bulk carrier appearance) respectively "ship structure 2" (oil tanker appearance). In this work, we extend the existing processing chain and are now able to handle full-polarimetric (HH, HV, VH, VV) TerraSAR-X data. With the possibility of better noise suppression using the different polarizations, we slightly improve both the segmentation and the classification process. In several experiments we demonstrate the potential benefit for segmentation and classification. Precision of size and orientation estimation as well as correct classification rates are calculated individually for single- and quad-polarization and compared to each other.

An evaluation of flight path formats head-up and head-down

NASA Technical Reports Server (NTRS)

Sexton, George A.; Moody, Laura E.; Evans, Joanne; Williams, Kenneth E.

1988-01-01

Flight path primary flight display formats were incorporated on head-up and head-down electronic displays and integrated into an Advanced Concepts Flight Simulator. Objective and subjective data were collected while ten airline pilots evaluated the formats by flying an approach and landing task under various ceiling, visibility and wind conditions. Deviations from referenced/commanded airspeed, horizontal track, vertical track and touchdown point were smaller using the head-up display (HUD) format than the head-down display (HDD) format, but not significantly smaller. Subjectively, the pilots overwhelmingly preferred (1) flight path formats over attitude formats used in current aircraft, and (2) the head-up presentation over the head-down, primarily because it eliminated the head-down to head-up transition during low visibility landing approaches. This report describes the simulator, the flight displays, the format evaluation, and the results of the objective and subjective data.

Identification of uncommon objects in containers

DOEpatents

Bremer, Peer-Timo; Kim, Hyojin; Thiagarajan, Jayaraman J.

2017-09-12

A system for identifying in an image an object that is commonly found in a collection of images and for identifying a portion of an image that represents an object based on a consensus analysis of segmentations of the image. The system collects images of containers that contain objects for generating a collection of common objects within the containers. To process the images, the system generates a segmentation of each image. The image analysis system may also generate multiple segmentations for each image by introducing variations in the selection of voxels to be merged into a segment. The system then generates clusters of the segments based on similarity among the segments. Each cluster represents a common object found in the containers. Once the clustering is complete, the system may be used to identify common objects in images of new containers based on similarity between segments of images and the clusters.

Cambrian stem-group annelids and a metameric origin of the annelid head.

PubMed

Parry, Luke; Vinther, Jakob; Edgecombe, Gregory D

2015-10-01

The oldest fossil annelids come from the Early Cambrian Sirius Passet and Guanshan biotas and Middle Cambrian Burgess Shale. While these are among the best preserved polychaete fossils, their relationship to living taxa is contentious, having been interpreted either as members of extant clades or as a grade outside the crown group. New morphological observations from five Cambrian species include the oldest polychaete with head appendages, a new specimen of Pygocirrus from Sirius Passet, and an undescribed form from the Burgess Shale. We propose that the palps of Canadia are on an anterior segment bearing neuropodia and that the head of Phragmochaeta is formed of a segment bearing biramous parapodia and chaetae. The unusual anatomy of these taxa suggests that the head is not differentiated into a prostomium and peristomium, that palps are derived from a modified parapodium and that the annelid head was originally a parapodium-bearing segment. Canadia, Phragmochaeta and the Marble Canyon annelid share the presence of protective notochaetae, interpreted as a primitive character state subsequently lost in Pygocirrus and Burgessochaeta, in which the head is clearly differentiated from the trunk. © 2015 The Authors.

Automatic Brain Portion Segmentation From Magnetic Resonance Images of Head Scans Using Gray Scale Transformation and Morphological Operations.

PubMed

Somasundaram, Karuppanagounder; Ezhilarasan, Kamalanathan

2015-01-01

To develop an automatic skull stripping method for magnetic resonance imaging (MRI) of human head scans. The proposed method is based on gray scale transformation and morphological operations. The proposed method has been tested with 20 volumes of normal T1-weighted images taken from Internet Brain Segmentation Repository. Experimental results show that the proposed method gives better results than the popular skull stripping methods Brain Extraction Tool and Brain Surface Extractor. The average value of Jaccard and Dice coefficients are 0.93 and 0.962 respectively. In this article, we have proposed a novel skull stripping method using intensity transformation and morphological operations. This is a low computational complexity method but gives competitive or better results than that of the popular skull stripping methods Brain Surface Extractor and Brain Extraction Tool.

Three-dimensional analysis of cervical spine segmental motion in rotation.

PubMed

Zhao, Xiong; Wu, Zi-Xiang; Han, Bao-Jun; Yan, Ya-Bo; Zhang, Yang; Lei, Wei

2013-06-20

The movements of the cervical spine during head rotation are too complicated to measure using conventional radiography or computed tomography (CT) techniques. In this study, we measure three-dimensional segmental motion of cervical spine rotation in vivo using a non-invasive measurement technique. Sixteen healthy volunteers underwent three-dimensional CT of the cervical spine during head rotation. Occiput (Oc) - T1 reconstructions were created of volunteers in each of 3 positions: supine and maximum left and right rotations of the head with respect to the bosom. Segmental motions were calculated using Euler angles and volume merge methods in three major planes. Mean maximum axial rotation of the cervical spine to one side was 1.6° to 38.5° at each level. Coupled lateral bending opposite to lateral bending was observed in the upper cervical levels, while in the subaxial cervical levels, it was observed in the same direction as axial rotation. Coupled extension was observed in the cervical levels of C5-T1, while coupled flexion was observed in the cervical levels of Oc-C5. The three-dimensional cervical segmental motions in rotation were accurately measured with the non-invasive measure. These findings will be helpful as the basis for understanding cervical spine movement in rotation and abnormal conditions. The presented data also provide baseline segmental motions for the design of prostheses for the cervical spine.

Implementation of the Canadian CT Head Rule and Its Association With Use of Computed Tomography Among Patients With Head Injury

PubMed Central

Sharp, Adam L.; Huang, Brian Z.; Tang, Tania; Shen, Ernest; Melnick, Edward R.; Venkatesh, Arjun K.; Kanter, Michael H.; Gould, Michael K.

2018-01-01

Study objective Approximately 1 in 3 computed tomography (CT) scans performed for head injury may be avoidable. We evaluate the association of implementation of the Canadian CT Head Rule on head CT imaging in community emergency departments (EDs). Methods We conducted an interrupted time-series analysis of encounters from January 2014 to December 2015 in 13 Southern California EDs. Adult health plan members with a trauma diagnosis and Glasgow Coma Scale score at ED triage were included. A multicomponent intervention included clinical leadership endorsement, physician education, and integrated clinical decision support. The primary outcome was the proportion of patients receiving a head CT. The unit of analysis was ED encounter, and we compared CT use pre- and postintervention with generalized estimating equations segmented logistic regression, with physician as a clustering variable. Secondary analysis described the yield of identified head injuries pre- and postintervention. Results Included were 44,947 encounters (28,751 preintervention and 16,196 postintervention), resulting in 14,633 (32.6%) head CTs (9,758 preintervention and 4,875 postintervention), with an absolute 5.3% (95% confidence interval [CI] 2.5% to 8.1%) reduction in CT use postintervention. Adjusted pre-post comparison showed a trend in decreasing odds of imaging (odds ratio 0.98; 95% CI 0.96 to 0.99). All but one ED reduced CTs postintervention (0.3% to 8.7%, one ED 0.3% increase), but no interaction between the intervention and study site over time existed (P=.34). After the intervention, diagnostic yield of CT-identified intracranial injuries increased by 2.3% (95% CI 1.5% to 3.1%). Conclusion A multicomponent implementation of the Canadian CT Head Rule was associated with a modest reduction in CT use and an increased diagnostic yield of head CTs for adult trauma encounters in community EDs. PMID:28739290

SU-E-J-220: Evaluation of Atlas-Based Auto-Segmentation (ABAS) in Head-And-Neck Adaptive Radiotherapy

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

Liu, Q; Yan, D

2014-06-01

Purpose: Evaluate the accuracy of atlas-based auto segmentation of organs at risk (OARs) on both helical CT (HCT) and cone beam CT (CBCT) images in head and neck (HN) cancer adaptive radiotherapy (ART). Methods: Six HN patients treated in the ART process were included in this study. For each patient, three images were selected: pretreatment planning CT (PreTx-HCT), in treatment CT for replanning (InTx-HCT) and a CBCT acquired in the same day of the InTx-HCT. Three clinical procedures of auto segmentation and deformable registration performed in the ART process were evaluated: a) auto segmentation on PreTx-HCT using multi-subject atlases, b)more » intra-patient propagation of OARs from PreTx-HCT to InTx-HCT using deformable HCT-to-HCT image registration, and c) intra-patient propagation of OARs from PreTx-HCT to CBCT using deformable CBCT-to-HCT image registration. Seven OARs (brainstem, cord, L/R parotid, L/R submandibular gland and mandible) were manually contoured on PreTx-HCT and InTx-HCT for comparison. In addition, manual contours on InTx-CT were copied on the same day CBCT, and a local region rigid body registration was performed accordingly for each individual OAR. For procedures a) and b), auto contours were compared to manual contours, and for c) auto contours were compared to those rigidly transferred contours on CBCT. Dice similarity coefficients (DSC) and mean surface distances of agreement (MSDA) were calculated for evaluation. Results: For procedure a), the mean DSC/MSDA of most OARs are >80%/±2mm. For intra-patient HCT-to-HCT propagation, the Resultimproved to >85%/±1.5mm. Compared to HCT-to-HCT, the mean DSC for HCT-to-CBCT propagation drops ∼2–3% and MSDA increases ∼0.2mm. This Resultindicates that the inferior imaging quality of CBCT seems only degrade auto propagation performance slightly. Conclusion: Auto segmentation and deformable propagation can generate OAR structures on HCT and CBCT images with clinically acceptable accuracy

Rigid shape matching by segmentation averaging.

PubMed

Wang, Hongzhi; Oliensis, John

2010-04-01

We use segmentations to match images by shape. The new matching technique does not require point-to-point edge correspondence and is robust to small shape variations and spatial shifts. To address the unreliability of segmentations computed bottom-up, we give a closed form approximation to an average over all segmentations. Our method has many extensions, yielding new algorithms for tracking, object detection, segmentation, and edge-preserving smoothing. For segmentation, instead of a maximum a posteriori approach, we compute the "central" segmentation minimizing the average distance to all segmentations of an image. For smoothing, instead of smoothing images based on local structures, we smooth based on the global optimal image structures. Our methods for segmentation, smoothing, and object detection perform competitively, and we also show promising results in shape-based tracking.

Poster — Thur Eve — 59: Atlas Selection for Automated Segmentation of Pelvic CT for Prostate Radiotherapy

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

Mallawi, A; Farrell, T; Diamond, K

2014-08-15

Automated atlas-based segmentation has recently been evaluated for use in planning prostate cancer radiotherapy. In the typical approach, the essential step is the selection of an atlas from a database that best matches the target image. This work proposes an atlas selection strategy and evaluates its impact on the final segmentation accuracy. Prostate length (PL), right femoral head diameter (RFHD), and left femoral head diameter (LFHD) were measured in CT images of 20 patients. Each subject was then taken as the target image to which all remaining 19 images were affinely registered. For each pair of registered images, the overlapmore » between prostate and femoral head contours was quantified using the Dice Similarity Coefficient (DSC). Finally, we designed an atlas selection strategy that computed the ratio of PL (prostate segmentation), RFHD (right femur segmentation), and LFHD (left femur segmentation) between the target subject and each subject in the atlas database. Five atlas subjects yielding ratios nearest to one were then selected for further analysis. RFHD and LFHD were excellent parameters for atlas selection, achieving a mean femoral head DSC of 0.82 ± 0.06. PL had a moderate ability to select the most similar prostate, with a mean DSC of 0.63 ± 0.18. The DSC obtained with the proposed selection method were slightly lower than the maximums established using brute force, but this does not include potential improvements expected with deformable registration. Atlas selection based on PL for prostate and femoral diameter for femoral heads provides reasonable segmentation accuracy.« less

Head-Neck Biomechanics in Simulated Rear Impact

PubMed Central

Yoganandan, Narayan; Pintar, Frank A.; Cusick, Joseph F.; Kleinberger, Michael

1998-01-01

The first objective of this study is to present an overview of the human cadaver studies aimed to determine the biomechanics of the head-neck in a simulated rear crash. The need for kinematic studies to better understand the mechanisms of load transfer to the human head-neck complex is emphasized. Based on this need, a methodology is developed to delineate the dynamic kinematics of the human head-neck complex. Intact human cadaver head-neck complexes were subjected to postero-anterior impact using a mini-sled pendulum device. The integrity of the soft tissues including the musculature and skin were maintained. The kinematic data were recorded using high-speed photography coupled with retroreflective targets placed at various regions of the human head-neck complex. The overall and segmental kinematics of the entire head-neck complex, and the localized facet joint motions were determined. During the initial stages of loading, a transient decoupling of the head occurred with respect to the neck exhibiting a lag of the cranium. The upper cervical spine-head undergoes local flexion concomitant with a lag of the head while the lower cervical spinal column is in local extension. This establishes a reverse curvature to the cervical head-neck complex. With continued loading, head motion ensues and approximately at the end of the loading phase, the entire head-neck complex is under the extension mode with a single curvature. In contrast, the lower cervical spine facet joint kinematics show varying compression and sliding. While both the anterior and posterior-most regions of the facet joint slide, the posterior-most region (mean: 2.84 mm) of the joint compresses more than the anterior-most (mean: 2.02 mm) region. These varying kinematics at the ends of the facet joint result in a pinching mechanism. These biomechanical kinematic findings may be correlated to the presence of headaches and neck pain (Lord, Bogduk et al. 1992; Barnsley, Lord et al. 1995), based on the unique

Use your head! Perception of action possibilities by means of an object attached to the head.

PubMed

Wagman, Jeffrey B; Hajnal, Alen

2016-03-01

Perceiving any environmental property requires spontaneously assembling a smart perceptual instrument-a task-specific measurement device assembled across potentially independent anatomical units. Previous research has shown that to a large degree, perception of a given environmental property is anatomically independent. We attempted to provide stronger evidence for this proposal by investigating perception by an organization of anatomical and inert components that likely requires the spontaneous assembly of a novel smart perceptual instrument-a rod attached to the head. Specifically, we compared cephalic and manual perception of whether an inclined surface affords standing on. In both conditions, perception reflected the action capabilities of the perceiver and not the appendage used to wield the rod. Such results provide stronger evidence for anatomical independence of perception within a given perceptual system and highlight that flexible task-specific detection units can be assembled across units that span the body and inert objects.

Automated MRI segmentation for individualized modeling of current flow in the human head.

PubMed

Huang, Yu; Dmochowski, Jacek P; Su, Yuzhuo; Datta, Abhishek; Rorden, Christopher; Parra, Lucas C

2013-12-01

High-definition transcranial direct current stimulation (HD-tDCS) and high-density electroencephalography require accurate models of current flow for precise targeting and current source reconstruction. At a minimum, such modeling must capture the idiosyncratic anatomy of the brain, cerebrospinal fluid (CSF) and skull for each individual subject. Currently, the process to build such high-resolution individualized models from structural magnetic resonance images requires labor-intensive manual segmentation, even when utilizing available automated segmentation tools. Also, accurate placement of many high-density electrodes on an individual scalp is a tedious procedure. The goal was to develop fully automated techniques to reduce the manual effort in such a modeling process. A fully automated segmentation technique based on Statical Parametric Mapping 8, including an improved tissue probability map and an automated correction routine for segmentation errors, was developed, along with an automated electrode placement tool for high-density arrays. The performance of these automated routines was evaluated against results from manual segmentation on four healthy subjects and seven stroke patients. The criteria include segmentation accuracy, the difference of current flow distributions in resulting HD-tDCS models and the optimized current flow intensities on cortical targets. The segmentation tool can segment out not just the brain but also provide accurate results for CSF, skull and other soft tissues with a field of view extending to the neck. Compared to manual results, automated segmentation deviates by only 7% and 18% for normal and stroke subjects, respectively. The predicted electric fields in the brain deviate by 12% and 29% respectively, which is well within the variability observed for various modeling choices. Finally, optimized current flow intensities on cortical targets do not differ significantly. Fully automated individualized modeling may now be feasible

Spared Ability to Perceive Direction of Locomotor Heading and Scene-Relative Object Movement Despite Inability to Perceive Relative Motion

PubMed Central

Vaina, Lucia M.; Buonanno, Ferdinando; Rushton, Simon K.

2014-01-01

Background All contemporary models of perception of locomotor heading from optic flow (the characteristic patterns of retinal motion that result from self-movement) begin with relative motion. Therefore it would be expected that an impairment on perception of relative motion should impact on the ability to judge heading and other 3D motion tasks. Material/Methods We report two patients with occipital lobe lesions whom we tested on a battery of motion tasks. Patients were impaired on all tests that involved relative motion in plane (motion discontinuity, form from differences in motion direction or speed). Despite this they retained the ability to judge their direction of heading relative to a target. A potential confound is that observers can derive information about heading from scale changes bypassing the need to use optic flow. Therefore we ran further experiments in which we isolated optic flow and scale change. Results Patients’ performance was in normal ranges on both tests. The finding that ability to perceive heading can be retained despite an impairment on ability to judge relative motion questions the assumption that heading perception proceeds from initial processing of relative motion. Furthermore, on a collision detection task, SS and SR’s performance was significantly better for simulated forward movement of the observer in the 3D scene, than for the static observer. This suggests that in spite of severe deficits on relative motion in the frontoparlel (xy) plane, information from self-motion helped identification objects moving along an intercept 3D relative motion trajectory. Conclusions This result suggests a potential use of a flow parsing strategy to detect in a 3D world the trajectory of moving objects when the observer is moving forward. These results have implications for developing rehabilitation strategies for deficits in visually guided navigation. PMID:25183375

Automatic tissue segmentation of head and neck MR images for hyperthermia treatment planning

NASA Astrophysics Data System (ADS)

Fortunati, Valerio; Verhaart, René F.; Niessen, Wiro J.; Veenland, Jifke F.; Paulides, Margarethus M.; van Walsum, Theo

2015-08-01

A hyperthermia treatment requires accurate, patient-specific treatment planning. This planning is based on 3D anatomical models which are generally derived from computed tomography. Because of its superior soft tissue contrast, magnetic resonance imaging (MRI) information can be introduced to improve the quality of these 3D patient models and therefore the treatment planning itself. Thus, we present here an automatic atlas-based segmentation algorithm for MR images of the head and neck. Our method combines multiatlas local weighting fusion with intensity modelling. The accuracy of the method was evaluated using a leave-one-out cross validation experiment over a set of 11 patients for which manual delineation were available. The accuracy of the proposed method was high both in terms of the Dice similarity coefficient (DSC) and the 95th percentile Hausdorff surface distance (HSD) with median DSC higher than 0.8 for all tissues except sclera. For all tissues, except the spine tissues, the accuracy was approaching the interobserver agreement/variability both in terms of DSC and HSD. The positive effect of adding the intensity modelling to the multiatlas fusion decreased when a more accurate atlas fusion method was used. Using the proposed approach we improved the performance of the approach previously presented for H&N hyperthermia treatment planning, making the method suitable for clinical application.

Segment scheduling method for reducing 360° video streaming latency

NASA Astrophysics Data System (ADS)

Gudumasu, Srinivas; Asbun, Eduardo; He, Yong; Ye, Yan

2017-09-01

360° video is an emerging new format in the media industry enabled by the growing availability of virtual reality devices. It provides the viewer a new sense of presence and immersion. Compared to conventional rectilinear video (2D or 3D), 360° video poses a new and difficult set of engineering challenges on video processing and delivery. Enabling comfortable and immersive user experience requires very high video quality and very low latency, while the large video file size poses a challenge to delivering 360° video in a quality manner at scale. Conventionally, 360° video represented in equirectangular or other projection formats can be encoded as a single standards-compliant bitstream using existing video codecs such as H.264/AVC or H.265/HEVC. Such method usually needs very high bandwidth to provide an immersive user experience. While at the client side, much of such high bandwidth and the computational power used to decode the video are wasted because the user only watches a small portion (i.e., viewport) of the entire picture. Viewport dependent 360°video processing and delivery approaches spend more bandwidth on the viewport than on non-viewports and are therefore able to reduce the overall transmission bandwidth. This paper proposes a dual buffer segment scheduling algorithm for viewport adaptive streaming methods to reduce latency when switching between high quality viewports in 360° video streaming. The approach decouples the scheduling of viewport segments and non-viewport segments to ensure the viewport segment requested matches the latest user head orientation. A base layer buffer stores all lower quality segments, and a viewport buffer stores high quality viewport segments corresponding to the most recent viewer's head orientation. The scheduling scheme determines viewport requesting time based on the buffer status and the head orientation. This paper also discusses how to deploy the proposed scheduling design for various viewport adaptive video

A bio-inspired method and system for visual object-based attention and segmentation

NASA Astrophysics Data System (ADS)

Huber, David J.; Khosla, Deepak

2010-04-01

This paper describes a method and system of human-like attention and object segmentation in visual scenes that (1) attends to regions in a scene in their rank of saliency in the image, (2) extracts the boundary of an attended proto-object based on feature contours, and (3) can be biased to boost the attention paid to specific features in a scene, such as those of a desired target object in static and video imagery. The purpose of the system is to identify regions of a scene of potential importance and extract the region data for processing by an object recognition and classification algorithm. The attention process can be performed in a default, bottom-up manner or a directed, top-down manner which will assign a preference to certain features over others. One can apply this system to any static scene, whether that is a still photograph or imagery captured from video. We employ algorithms that are motivated by findings in neuroscience, psychology, and cognitive science to construct a system that is novel in its modular and stepwise approach to the problems of attention and region extraction, its application of a flooding algorithm to break apart an image into smaller proto-objects based on feature density, and its ability to join smaller regions of similar features into larger proto-objects. This approach allows many complicated operations to be carried out by the system in a very short time, approaching real-time. A researcher can use this system as a robust front-end to a larger system that includes object recognition and scene understanding modules; it is engineered to function over a broad range of situations and can be applied to any scene with minimal tuning from the user.

Volumetric Assessment of Swallowing Muscles: A Comparison of CT and MRI Segmentation.

PubMed

Sporns, Kim Barbara; Hanning, Uta; Schmidt, Rene; Muhle, Paul; Wirth, Rainer; Zimmer, Sebastian; Dziewas, Rainer; Suntrup-Krueger, Sonja; Sporns, Peter Bernhard; Heindel, Walter; Schwindt, Wolfram

2018-05-01

 Recent retrospective studies have proposed a high correlation between atrophy of swallowing muscles, age, severity of dysphagia and aspiration status based on computed tomography (CT). However, ionizing radiation poses an ethical barrier to research in prospective non-patient populations. Hence, there is a need to prove the efficacy of techniques that rely on noninvasive methods and produce high-resolution soft tissue images such as magnetic resonance imaging (MRI). The objective of this study was therefore to compare the segmentation results of swallowing muscles using CT and MRI.  Retrospective study of 21 patients (median age: 46.6; gender: 11 female) who underwent CT and MRI of the head and neck region within a time frame of less than 50 days because of suspected head and neck cancer using contrast agent. CT and MR images were segmented by two blinded readers using Medical Imaging Toolkit (MITK) and both modalities were tested (with the equivalence test) regarding the segmented muscle volumes. Adjustment for multiple testing was performed using the Bonferroni test and the potential time effect of the muscle volumes and the time interval between the modalities was assessed by a spearman correlation. The study was approved by the local ethics committee.  The median volumes for each muscle belly of the digastric muscle derived from CT were 3051 mm 3 (left) and 2969 mm 3 (right), and from MRI they were 3218 mm 3 (left) and 3027 mm 3 (right). The median volume of the geniohyoid muscle was 6580 mm 3 on CT and 6648 mm 3 on MRI. The interrater reliability was high for all segmented muscles. The mean time interval between the CT and MRI examinations was 34 days (IQR 25; 41). The muscle differences of each muscle between the two modalities did not reveal significant correlation to the time interval between the examinations (digastric left r = 0.003 and digastric right r = -0.008; geniohyoid muscle r = 0.075).  CT-based segmentation and

MR imaging assessment of the lateral head of the gastrocnemius muscle: prevalence of segmental anomalous origins in children and young adults.

PubMed

Kim, Hee Kyung; Laor, Tal; Racadio, Judy M

2008-12-01

Variations in the lower extremity musculature have been identified, including an anomalous origin of the medial head of the gastrocnemius muscle. Anomalies of the lateral head of the gastrocnemius muscle (LGN) have been less frequently described, especially in children. To describe the MR imaging appearance, frequency and clinical symptoms associated with anatomic variations of the LGN in children and young adults. A retrospective review of 465 knee MR imaging examinations was performed. The site of origin of the LGN was identified as either normal, lateral segmental anomalous origin (LSAO), or medial accessory anomalous origin (MAAO). The clinical indication for imaging was recorded. An anatomic variation of the LGN was identified in 16 patients (3.4%). Nine patients had LSAO, and five of these had symptoms referable to or abnormalities of the patella. Seven patients had MAAO, and three of these had chronic nontraumatic knee pain. Anatomic variations of the LGN are not rare in young patients, occurring with a frequency of 3.4% in our series. It is unknown whether these anomalies play a role in the etiology of patellofemoral pain or unexplained joint pain in children.

Figure/Ground Segmentation via a Haptic Glance: Attributing Initial Finger Contacts to Objects or Their Supporting Surfaces.

PubMed

Pawluk, D; Kitada, R; Abramowicz, A; Hamilton, C; Lederman, S J

2011-01-01

The current study addresses the well-known "figure/ground" problem in human perception, a fundamental topic that has received surprisingly little attention from touch scientists to date. Our approach is grounded in, and directly guided by, current knowledge concerning the nature of haptic processing. Given inherent figure/ground ambiguity in natural scenes and limited sensory inputs from first contact (a "haptic glance"), we consider first whether people are even capable of differentiating figure from ground (Experiments 1 and 2). Participants were required to estimate the strength of their subjective impression that they were feeling an object (i.e., figure) as opposed to just the supporting structure (i.e., ground). Second, we propose a tripartite factor classification scheme to further assess the influence of kinetic, geometric (Experiments 1 and 2), and material (Experiment 2) factors on haptic figure/ground segmentation, complemented by more open-ended subjective responses obtained at the end of the experiment. Collectively, the results indicate that under certain conditions it is possible to segment figure from ground via a single haptic glance with a reasonable degree of certainty, and that all three factor classes influence the estimated likelihood that brief, spatially distributed fingertip contacts represent contact with an object and/or its background supporting structure.

Objective measurement of head movement differences in children with and without autism spectrum disorder.

PubMed

Martin, Katherine B; Hammal, Zakia; Ren, Gang; Cohn, Jeffrey F; Cassell, Justine; Ogihara, Mitsunori; Britton, Jennifer C; Gutierrez, Anibal; Messinger, Daniel S

2018-01-01

Deficits in motor movement in children with autism spectrum disorder (ASD) have typically been characterized qualitatively by human observers. Although clinicians have noted the importance of atypical head positioning (e.g. social peering and repetitive head banging) when diagnosing children with ASD, a quantitative understanding of head movement in ASD is lacking. Here, we conduct a quantitative comparison of head movement dynamics in children with and without ASD using automated, person-independent computer-vision based head tracking (Zface). Because children with ASD often exhibit preferential attention to nonsocial versus social stimuli, we investigated whether children with and without ASD differed in their head movement dynamics depending on stimulus sociality. The current study examined differences in head movement dynamics in children with ( n  = 21) and without ASD ( n  = 21). Children were video-recorded while watching a 16-min video of social and nonsocial stimuli. Three dimensions of rigid head movement-pitch (head nods), yaw (head turns), and roll (lateral head inclinations)-were tracked using Zface. The root mean square of pitch, yaw, and roll was calculated to index the magnitude of head angular displacement (quantity of head movement) and angular velocity (speed). Compared with children without ASD, children with ASD exhibited greater yaw displacement, indicating greater head turning, and greater velocity of yaw and roll, indicating faster head turning and inclination. Follow-up analyses indicated that differences in head movement dynamics were specific to the social rather than the nonsocial stimulus condition. Head movement dynamics (displacement and velocity) were greater in children with ASD than in children without ASD, providing a quantitative foundation for previous clinical reports. Head movement differences were evident in lateral (yaw and roll) but not vertical (pitch) movement and were specific to a social rather than nonsocial

Video segmentation using keywords

NASA Astrophysics Data System (ADS)

Ton-That, Vinh; Vong, Chi-Tai; Nguyen-Dao, Xuan-Truong; Tran, Minh-Triet

2018-04-01

At DAVIS-2016 Challenge, many state-of-art video segmentation methods achieve potential results, but they still much depend on annotated frames to distinguish between background and foreground. It takes a lot of time and efforts to create these frames exactly. In this paper, we introduce a method to segment objects from video based on keywords given by user. First, we use a real-time object detection system - YOLOv2 to identify regions containing objects that have labels match with the given keywords in the first frame. Then, for each region identified from the previous step, we use Pyramid Scene Parsing Network to assign each pixel as foreground or background. These frames can be used as input frames for Object Flow algorithm to perform segmentation on entire video. We conduct experiments on a subset of DAVIS-2016 dataset in half the size of its original size, which shows that our method can handle many popular classes in PASCAL VOC 2012 dataset with acceptable accuracy, about 75.03%. We suggest widely testing by combining other methods to improve this result in the future.

Tooth segmentation system with intelligent editing for cephalometric analysis

NASA Astrophysics Data System (ADS)

Chen, Shoupu

2015-03-01

Cephalometric analysis is the study of the dental and skeletal relationship in the head, and it is used as an assessment and planning tool for improved orthodontic treatment of a patient. Conventional cephalometric analysis identifies bony and soft-tissue landmarks in 2D cephalometric radiographs, in order to diagnose facial features and abnormalities prior to treatment, or to evaluate the progress of treatment. Recent studies in orthodontics indicate that there are persistent inaccuracies and inconsistencies in the results provided using conventional 2D cephalometric analysis. Obviously, plane geometry is inappropriate for analyzing anatomical volumes and their growth; only a 3D analysis is able to analyze the three-dimensional, anatomical maxillofacial complex, which requires computing inertia systems for individual or groups of digitally segmented teeth from an image volume of a patient's head. For the study of 3D cephalometric analysis, the current paper proposes a system for semi-automatically segmenting teeth from a cone beam computed tomography (CBCT) volume with two distinct features, including an intelligent user-input interface for automatic background seed generation, and a graphics processing unit (GPU) acceleration mechanism for three-dimensional GrowCut volume segmentation. Results show a satisfying average DICE score of 0.92, with the use of the proposed tooth segmentation system, by 15 novice users who segmented a randomly sampled tooth set. The average GrowCut processing time is around one second per tooth, excluding user interaction time.

Efficient threshold for volumetric segmentation

NASA Astrophysics Data System (ADS)

Burdescu, Dumitru D.; Brezovan, Marius; Stanescu, Liana; Stoica Spahiu, Cosmin; Ebanca, Daniel

2015-07-01

Image segmentation plays a crucial role in effective understanding of digital images. However, the research on the existence of general purpose segmentation algorithm that suits for variety of applications is still very much active. Among the many approaches in performing image segmentation, graph based approach is gaining popularity primarily due to its ability in reflecting global image properties. Volumetric image segmentation can simply result an image partition composed by relevant regions, but the most fundamental challenge in segmentation algorithm is to precisely define the volumetric extent of some object, which may be represented by the union of multiple regions. The aim in this paper is to present a new method to detect visual objects from color volumetric images and efficient threshold. We present a unified framework for volumetric image segmentation and contour extraction that uses a virtual tree-hexagonal structure defined on the set of the image voxels. The advantage of using a virtual tree-hexagonal network superposed over the initial image voxels is that it reduces the execution time and the memory space used, without losing the initial resolution of the image.

Analysis of manual segmentation in paranasal CT images.

PubMed

Tingelhoff, Kathrin; Eichhorn, Klaus W G; Wagner, Ingo; Kunkel, Maria E; Moral, Analia I; Rilk, Markus E; Wahl, Friedrich M; Bootz, Friedrich

2008-09-01

Manual segmentation is often used for evaluation of automatic or semi-automatic segmentation. The purpose of this paper is to describe the inter and intraindividual variability, the dubiety of manual segmentation as a gold standard and to find reasons for the discrepancy. We realized two experiments. In the first one ten ENT surgeons, ten medical students and one engineer outlined the right maxillary sinus and ethmoid sinuses manually on a standard CT dataset of a human head. In the second experiment two participants outlined maxillary sinus and ethmoid sinuses five times consecutively. Manual segmentation was accomplished with custom software using a line segmentation tool. The first experiment shows the interindividual variability of manual segmentation which is higher for ethmoidal sinuses than for maxillary sinuses. The variability can be caused by the level of experience, different interpretation of the CT data or different levels of accuracy. The second experiment shows intraindividual variability which is lower than interindividual variability. Most variances in both experiments appear during segmentation of ethmoidal sinuses and outlining hiatus semilunaris. Concerning the inter and intraindividual variances the segmentation result of one manual segmenter could not directly be used as gold standard for the evaluation of automatic segmentation algorithms.

A NDVI assisted remote sensing image adaptive scale segmentation method

NASA Astrophysics Data System (ADS)

Zhang, Hong; Shen, Jinxiang; Ma, Yanmei

2018-03-01

Multiscale segmentation of images can effectively form boundaries of different objects with different scales. However, for the remote sensing image which widely coverage with complicated ground objects, the number of suitable segmentation scales, and each of the scale size is still difficult to be accurately determined, which severely restricts the rapid information extraction of the remote sensing image. A great deal of experiments showed that the normalized difference vegetation index (NDVI) can effectively express the spectral characteristics of a variety of ground objects in remote sensing images. This paper presents a method using NDVI assisted adaptive segmentation of remote sensing images, which segment the local area by using NDVI similarity threshold to iteratively select segmentation scales. According to the different regions which consist of different targets, different segmentation scale boundaries could be created. The experimental results showed that the adaptive segmentation method based on NDVI can effectively create the objects boundaries for different ground objects of remote sensing images.

A segmentation editing framework based on shape change statistics

NASA Astrophysics Data System (ADS)

Mostapha, Mahmoud; Vicory, Jared; Styner, Martin; Pizer, Stephen

2017-02-01

Segmentation is a key task in medical image analysis because its accuracy significantly affects successive steps. Automatic segmentation methods often produce inadequate segmentations, which require the user to manually edit the produced segmentation slice by slice. Because editing is time-consuming, an editing tool that enables the user to produce accurate segmentations by only drawing a sparse set of contours would be needed. This paper describes such a framework as applied to a single object. Constrained by the additional information enabled by the manually segmented contours, the proposed framework utilizes object shape statistics to transform the failed automatic segmentation to a more accurate version. Instead of modeling the object shape, the proposed framework utilizes shape change statistics that were generated to capture the object deformation from the failed automatic segmentation to its corresponding correct segmentation. An optimization procedure was used to minimize an energy function that consists of two terms, an external contour match term and an internal shape change regularity term. The high accuracy of the proposed segmentation editing approach was confirmed by testing it on a simulated data set based on 10 in-vivo infant magnetic resonance brain data sets using four similarity metrics. Segmentation results indicated that our method can provide efficient and adequately accurate segmentations (Dice segmentation accuracy increase of 10%), with very sparse contours (only 10%), which is promising in greatly decreasing the work expected from the user.

Influence of optic disc leakage on objective optic nerve head assessment in patients with uveitis.

PubMed

Heinz, Carsten; Kogelboom, Katy; Heiligenhaus, Arnd

2016-02-01

Secondary glaucoma is a common complication in patients with uveitis. Heidelberg Retina Tomography (HRT) and retinal nerve fiber layer (RNFL) thickness on optical coherence tomography (OCT) are widely used for examining optic nerve head changes. We evaluated these parameters in patients with uveitis and secondary glaucoma and with inflammatory papillary leakage on fluorescein angiography. Prospective single-center analysis of patients with uveitis, evaluating the impact of optic disc leakage on objective optic disc imaging parameters. Overall, 96 eyes of 59 patients were included. Papillary leakage was found in 42 eyes (43.8 %), and secondary glaucoma was found in 41 eyes (42.7 %). Glaucoma and papillary leakage were present in 12 (29 %) eyes with leakage and in 29 (54 %) eyes without leakage (p = 0.023). Neuroretinal rim area (p = 0.004), rim volume on HRT (p = 0.004), and RNFL thickness on OCT (p = 0.0008) were significantly increased in eyes with papillary leakage, while RNFL on HRT was unchanged (p = 0.255). When only eyes with normal IOP were examined, all objective parameters on OCT and HRT were significantly increased, whereas in eyes with secondary glaucoma, there was only a trend in the same direction, which did not reach significance. A comparison of eyes with secondary glaucoma and optic disc leakage to normal eyes with no glaucoma or leakage revealed no difference in any of the parameters. The objective parameters of optic nerve head imaging tools are significantly influenced by papillary leakage. In patients with secondary glaucoma and papillary leakage, these techniques are unable to detect and monitor glaucomatous damage.

Early embryonic development of the head region of Gryllus assimilis Fabricius, 1775 (Orthoptera, Insecta).

PubMed

Liu, Yu; Maas, Andreas; Waloszek, Dieter

2010-09-01

We report our investigations on the embryonic development of Gryllus assimilis, with particular attention to the head. Significant findings revealed with scanning electron microscopy (SEM) images include: (1) the pre-antennal lobes represent the anterior-most segment that does not bear any appendages; (2) each of the lobes consists of central and marginal regions; (3) the central region thereof develops into the protocerebrum and the optic lobes, whereas the marginal region thereof becomes the anterior portion of the head capsule; (4) the initial position of the antennal segment is posterior to the mouth region; (5) appendage anlagen are transitorily present in the intercalary segment, and they later vanish together with the segment itself; (6) a bulged sternum appears to develop from the ventral surface of the mandibular, maxillary and labial segments. Embryonic features are then compared across the Insecta and further extended to the embryos of a spider (Araneae, Chelicerata). Striking similarities shared by the anterior-most region of the insect and spider embryos lead the authors to conclude that such comparison should be further undertaken to cover the entire Euarthropoda. This will help us to understand the embryology and evolution of the arthropod head. Copyright © 2010 Elsevier Ltd. All rights reserved.

Segmenting overlapping nano-objects in atomic force microscopy image

NASA Astrophysics Data System (ADS)

Wang, Qian; Han, Yuexing; Li, Qing; Wang, Bing; Konagaya, Akihiko

2018-01-01

Recently, techniques for nanoparticles have rapidly been developed for various fields, such as material science, medical, and biology. In particular, methods of image processing have widely been used to automatically analyze nanoparticles. A technique to automatically segment overlapping nanoparticles with image processing and machine learning is proposed. Here, two tasks are necessary: elimination of image noises and action of the overlapping shapes. For the first task, mean square error and the seed fill algorithm are adopted to remove noises and improve the quality of the original image. For the second task, four steps are needed to segment the overlapping nanoparticles. First, possibility split lines are obtained by connecting the high curvature pixels on the contours. Second, the candidate split lines are classified with a machine learning algorithm. Third, the overlapping regions are detected with the method of density-based spatial clustering of applications with noise (DBSCAN). Finally, the best split lines are selected with a constrained minimum value. We give some experimental examples and compare our technique with two other methods. The results can show the effectiveness of the proposed technique.

Stabilization and mobility of the head, neck and trunk in horses during overground locomotion: comparisons with humans and other primates.

PubMed

Dunbar, Donald C; Macpherson, Jane M; Simmons, Roger W; Zarcades, Athina

2008-12-01

Segmental kinematics were investigated in horses during overground locomotion and compared with published reports on humans and other primates to determine the impact of a large neck on rotational mobility (> 20 deg.) and stability (< or = 20 deg.) of the head and trunk. Three adult horses (Equus caballus) performing walks, trots and canters were videotaped in lateral view. Data analysis included locomotor velocity, segmental positions, pitch and linear displacements and velocities, and head displacement frequencies. Equine, human and monkey skulls and cervical spines were measured to estimate eye and vestibular arc length during head pitch displacements. Horses stabilized all three segments in all planes during all three gaits, unlike monkeys and humans who make large head pitch and yaw rotations during walks, and monkeys that make large trunk pitch rotations during gallops. Equine head angular displacements and velocities, with some exceptions during walks, were smaller than in humans and other primates. Nevertheless, owing to greater off-axis distances, orbital and vestibular arc lengths remained larger in horses, with the exception of head-neck axial pitch during trots, in which equine arc lengths were smaller than in running humans. Unlike monkeys and humans, equine head peak-frequency ranges fell within the estimated range in which inertia has a compensatory stabilizing effect. This inertial effect was typically over-ridden, however, by muscular or ligamentous intervention. Thus, equine head pitch was not consistently compensatory, as reported in humans. The equine neck isolated the head from the trunk enabling both segments to provide a spatial reference frame.

Deformable M-Reps for 3D Medical Image Segmentation.

PubMed

Pizer, Stephen M; Fletcher, P Thomas; Joshi, Sarang; Thall, Andrew; Chen, James Z; Fridman, Yonatan; Fritsch, Daniel S; Gash, Graham; Glotzer, John M; Jiroutek, Michael R; Lu, Conglin; Muller, Keith E; Tracton, Gregg; Yushkevich, Paul; Chaney, Edward L

2003-11-01

M-reps (formerly called DSLs) are a multiscale medial means for modeling and rendering 3D solid geometry. They are particularly well suited to model anatomic objects and in particular to capture prior geometric information effectively in deformable models segmentation approaches. The representation is based on figural models , which define objects at coarse scale by a hierarchy of figures - each figure generally a slab representing a solid region and its boundary simultaneously. This paper focuses on the use of single figure models to segment objects of relatively simple structure. A single figure is a sheet of medial atoms, which is interpolated from the model formed by a net, i.e., a mesh or chain, of medial atoms (hence the name m-reps ), each atom modeling a solid region via not only a position and a width but also a local figural frame giving figural directions and an object angle between opposing, corresponding positions on the boundary implied by the m-rep. The special capability of an m-rep is to provide spatial and orientational correspondence between an object in two different states of deformation. This ability is central to effective measurement of both geometric typicality and geometry to image match, the two terms of the objective function optimized in segmentation by deformable models. The other ability of m-reps central to effective segmentation is their ability to support segmentation at multiple levels of scale, with successively finer precision. Objects modeled by single figures are segmented first by a similarity transform augmented by object elongation, then by adjustment of each medial atom, and finally by displacing a dense sampling of the m-rep implied boundary. While these models and approaches also exist in 2D, we focus on 3D objects. The segmentation of the kidney from CT and the hippocampus from MRI serve as the major examples in this paper. The accuracy of segmentation as compared to manual, slice-by-slice segmentation is reported.

Deformable M-Reps for 3D Medical Image Segmentation

PubMed Central

Pizer, Stephen M.; Fletcher, P. Thomas; Joshi, Sarang; Thall, Andrew; Chen, James Z.; Fridman, Yonatan; Fritsch, Daniel S.; Gash, Graham; Glotzer, John M.; Jiroutek, Michael R.; Lu, Conglin; Muller, Keith E.; Tracton, Gregg; Yushkevich, Paul; Chaney, Edward L.

2013-01-01

M-reps (formerly called DSLs) are a multiscale medial means for modeling and rendering 3D solid geometry. They are particularly well suited to model anatomic objects and in particular to capture prior geometric information effectively in deformable models segmentation approaches. The representation is based on figural models, which define objects at coarse scale by a hierarchy of figures – each figure generally a slab representing a solid region and its boundary simultaneously. This paper focuses on the use of single figure models to segment objects of relatively simple structure. A single figure is a sheet of medial atoms, which is interpolated from the model formed by a net, i.e., a mesh or chain, of medial atoms (hence the name m-reps), each atom modeling a solid region via not only a position and a width but also a local figural frame giving figural directions and an object angle between opposing, corresponding positions on the boundary implied by the m-rep. The special capability of an m-rep is to provide spatial and orientational correspondence between an object in two different states of deformation. This ability is central to effective measurement of both geometric typicality and geometry to image match, the two terms of the objective function optimized in segmentation by deformable models. The other ability of m-reps central to effective segmentation is their ability to support segmentation at multiple levels of scale, with successively finer precision. Objects modeled by single figures are segmented first by a similarity transform augmented by object elongation, then by adjustment of each medial atom, and finally by displacing a dense sampling of the m-rep implied boundary. While these models and approaches also exist in 2D, we focus on 3D objects. The segmentation of the kidney from CT and the hippocampus from MRI serve as the major examples in this paper. The accuracy of segmentation as compared to manual, slice-by-slice segmentation is reported. PMID

Scale Changes Provide an Alternative Cue For the Discrimination of Heading, But Not Object Motion

PubMed Central

Calabro, Finnegan J.; Vaina, Lucia Maria

2016-01-01

Background Understanding the dynamics of our surrounding environments is a task usually attributed to the detection of motion based on changes in luminance across space. Yet a number of other cues, both dynamic and static, have been shown to provide useful information about how we are moving and how objects around us move. One such cue, based on changes in spatial frequency, or scale, over time has been shown to be useful in conveying motion in depth even in the absence of a coherent, motion-defined flow field (optic flow). Material/Methods 16 right handed healthy observers (ages 18–28) participated in the behavioral experiments described in this study. Using analytical behavioral methods we investigate the functional specificity of this cue by measuring the ability of observers to perform tasks of heading (direction of self-motion) and 3D trajectory discrimination on the basis of scale changes and optic flow. Results Statistical analyses of performance on the test-experiments in comparison to the control experiments suggests that while scale changes may be involved in the detection of heading, they are not correctly integrated with translational motion and, thus, do not provide a correct discrimination of 3D object trajectories. Conclusions These results have the important implication for the type of visual guided navigation that can be done by an observer blind to optic flow. Scale change is an important alternative cue for self-motion. PMID:27231114

Scale Changes Provide an Alternative Cue For the Discrimination of Heading, But Not Object Motion.

PubMed

Calabro, Finnegan J; Vaina, Lucia Maria

2016-05-27

BACKGROUND Understanding the dynamics of our surrounding environments is a task usually attributed to the detection of motion based on changes in luminance across space. Yet a number of other cues, both dynamic and static, have been shown to provide useful information about how we are moving and how objects around us move. One such cue, based on changes in spatial frequency, or scale, over time has been shown to be useful in conveying motion in depth even in the absence of a coherent, motion-defined flow field (optic flow). MATERIAL AND METHODS 16 right handed healthy observers (ages 18-28) participated in the behavioral experiments described in this study. Using analytical behavioral methods we investigate the functional specificity of this cue by measuring the ability of observers to perform tasks of heading (direction of self-motion) and 3D trajectory discrimination on the basis of scale changes and optic flow. RESULTS Statistical analyses of performance on the test-experiments in comparison to the control experiments suggests that while scale changes may be involved in the detection of heading, they are not correctly integrated with translational motion and, thus, do not provide a correct discrimination of 3D object trajectories. CONCLUSIONS These results have the important implication for the type of visual guided navigation that can be done by an observer blind to optic flow. Scale change is an important alternative cue for self-motion.

Open-source software platform for medical image segmentation applications

NASA Astrophysics Data System (ADS)

Namías, R.; D'Amato, J. P.; del Fresno, M.

2017-11-01

Segmenting 2D and 3D images is a crucial and challenging problem in medical image analysis. Although several image segmentation algorithms have been proposed for different applications, no universal method currently exists. Moreover, their use is usually limited when detection of complex and multiple adjacent objects of interest is needed. In addition, the continually increasing volumes of medical imaging scans require more efficient segmentation software design and highly usable applications. In this context, we present an extension of our previous segmentation framework which allows the combination of existing explicit deformable models in an efficient and transparent way, handling simultaneously different segmentation strategies and interacting with a graphic user interface (GUI). We present the object-oriented design and the general architecture which consist of two layers: the GUI at the top layer, and the processing core filters at the bottom layer. We apply the framework for segmenting different real-case medical image scenarios on public available datasets including bladder and prostate segmentation from 2D MRI, and heart segmentation in 3D CT. Our experiments on these concrete problems show that this framework facilitates complex and multi-object segmentation goals while providing a fast prototyping open-source segmentation tool.

Augmented-reality visualization of brain structures with stereo and kinetic depth cues: system description and initial evaluation with head phantom

NASA Astrophysics Data System (ADS)

Maurer, Calvin R., Jr.; Sauer, Frank; Hu, Bo; Bascle, Benedicte; Geiger, Bernhard; Wenzel, Fabian; Recchi, Filippo; Rohlfing, Torsten; Brown, Christopher R.; Bakos, Robert J.; Maciunas, Robert J.; Bani-Hashemi, Ali R.

2001-05-01

We are developing a video see-through head-mounted display (HMD) augmented reality (AR) system for image-guided neurosurgical planning and navigation. The surgeon wears a HMD that presents him with the augmented stereo view. The HMD is custom fitted with two miniature color video cameras that capture a stereo view of the real-world scene. We are concentrating specifically at this point on cranial neurosurgery, so the images will be of the patient's head. A third video camera, operating in the near infrared, is also attached to the HMD and is used for head tracking. The pose (i.e., position and orientation) of the HMD is used to determine where to overlay anatomic structures segmented from preoperative tomographic images (e.g., CT, MR) on the intraoperative video images. Two SGI 540 Visual Workstation computers process the three video streams and render the augmented stereo views for display on the HMD. The AR system operates in real time at 30 frames/sec with a temporal latency of about three frames (100 ms) and zero relative lag between the virtual objects and the real-world scene. For an initial evaluation of the system, we created AR images using a head phantom with actual internal anatomic structures (segmented from CT and MR scans of a patient) realistically positioned inside the phantom. When using shaded renderings, many users had difficulty appreciating overlaid brain structures as being inside the head. When using wire frames, and texture-mapped dot patterns, most users correctly visualized brain anatomy as being internal and could generally appreciate spatial relationships among various objects. The 3D perception of these structures is based on both stereoscopic depth cues and kinetic depth cues, with the user looking at the head phantom from varying positions. The perception of the augmented visualization is natural and convincing. The brain structures appear rigidly anchored in the head, manifesting little or no apparent swimming or jitter. The initial

Semantic Image Segmentation with Contextual Hierarchical Models.

PubMed

Seyedhosseini, Mojtaba; Tasdizen, Tolga

2016-05-01

Semantic segmentation is the problem of assigning an object label to each pixel. It unifies the image segmentation and object recognition problems. The importance of using contextual information in semantic segmentation frameworks has been widely realized in the field. We propose a contextual framework, called contextual hierarchical model (CHM), which learns contextual information in a hierarchical framework for semantic segmentation. At each level of the hierarchy, a classifier is trained based on downsampled input images and outputs of previous levels. Our model then incorporates the resulting multi-resolution contextual information into a classifier to segment the input image at original resolution. This training strategy allows for optimization of a joint posterior probability at multiple resolutions through the hierarchy. Contextual hierarchical model is purely based on the input image patches and does not make use of any fragments or shape examples. Hence, it is applicable to a variety of problems such as object segmentation and edge detection. We demonstrate that CHM performs at par with state-of-the-art on Stanford background and Weizmann horse datasets. It also outperforms state-of-the-art edge detection methods on NYU depth dataset and achieves state-of-the-art on Berkeley segmentation dataset (BSDS 500).

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.

Heading and head injuries in soccer.

PubMed

Kirkendall, D T; Jordan, S E; Garrett, W E

2001-01-01

In the world of sports, soccer is unique because of the purposeful use of the unprotected head for controlling and advancing the ball. This skill obviously places the player at risk of head injury and the game does carry some risk. Head injury can be a result of contact of the head with another head (or other body parts), ground, goal post, other unknown objects or even the ball. Such impacts can lead to contusions, fractures, eye injuries, concussions or even, in rare cases, death. Coaches, players, parents and physicians are rightly concerned about the risk of head injury in soccer. Current research shows that selected soccer players have some degree of cognitive dysfunction. It is important to determine the reasons behind such deficits. Purposeful heading has been blamed, but a closer look at the studies that focus on heading has revealed methodological concerns that question the validity of blaming purposeful heading of the ball. The player's history and age (did they play when the ball was leather and could absorb significant amounts of water), alcohol intake, drug intake, learning disabilities, concussion definition and control group use/composition are all factors that cloud the ability to blame purposeful heading. What does seem clear is that a player's history of concussive episodes is a more likely explanation for cognitive deficits. While it is likely that the subconcussive impact of purposeful heading is a doubtful factor in the noted deficits, it is unknown whether multiple subconcussive impacts might have some lingering effects. In addition, it is unknown whether the noted deficits have any affect on daily life. Proper instruction in the technique is critical because if the ball contacts an unprepared head (as in accidental head-ball contacts), the potential for serious injury is possible. To further our understanding of the relationship of heading, head injury and cognitive deficits, we need to: learn more about the actual impact of a ball on the

Stabilization and mobility of the head, neck and trunk in horses during overground locomotion: comparisons with humans and other primates

PubMed Central

Dunbar, Donald C.; Macpherson, Jane M.; Simmons, Roger W.; Zarcades, Athina

2009-01-01

SUMMARY Segmental kinematics were investigated in horses during overground locomotion and compared with published reports on humans and other primates to determine the impact of a large neck on rotational mobility (>20deg.) and stability (≤20deg.) of the head and trunk. Three adult horses (Equus caballus) performing walks, trots and canters were videotaped in lateral view. Data analysis included locomotor velocity, segmental positions, pitch and linear displacements and velocities, and head displacement frequencies. Equine, human and monkey skulls and cervical spines were measured to estimate eye and vestibular arc length during head pitch displacements. Horses stabilized all three segments in all planes during all three gaits, unlike monkeys and humans who make large head pitch and yaw rotations during walks, and monkeys that make large trunk pitch rotations during gallops. Equine head angular displacements and velocities, with some exceptions during walks, were smaller than in humans and other primates. Nevertheless, owing to greater off-axis distances, orbital and vestibular arc lengths remained larger in horses, with the exception of head–neck axial pitch during trots, in which equine arc lengths were smaller than in running humans. Unlike monkeys and humans, equine head peak-frequency ranges fell within the estimated range in which inertia has a compensatory stabilizing effect. This inertial effect was typically over-ridden, however, by muscular or ligamentous intervention. Thus, equine head pitch was not consistently compensatory, as reported in humans. The equine neck isolated the head from the trunk enabling both segments to provide a spatial reference frame. PMID:19043061

Risk segmentation: goal or problem?

PubMed

Feldman, R; Dowd, B

2000-07-01

This paper traces the evolution of economists' views about risk segmentation in health insurance markets. Originally seen as a desirable goal, risk segmentation has come to be viewed as leading to abnormal profits, wasted resources, and inefficient limitations on coverage and services. We suggest that risk segmentation may be efficient if one takes an ex post view (i.e., after consumers' risks are known). From this perspective, managed care may be a much better method for achieving risk segmentation than limitations on coverage. The most serious objection to risk segmentation is the ex ante concern that it undermines long-term insurance contracts that would protect consumers against changes in lifetime risk.

Effects of Nerve Injury and Segmental Regeneration on the Cellular Correlates of Neural Morphallaxis

PubMed Central

Martinez, Veronica G.; Manson, Josiah M.B.; Zoran, Mark J.

2009-01-01

Functional recovery of neural networks after injury requires a series of signaling events similar to the embryonic processes that governed initial network construction. Neural morphallaxis, a form of nervous system regeneration, involves reorganization of adult neural connectivity patterns. Neural morphallaxis in the worm, Lumbriculus variegatus, occurs during asexual reproduction and segmental regeneration, as body fragments acquire new positional identities along the anterior–posterior axis. Ectopic head (EH) formation, induced by ventral nerve cord lesion, generated morphallactic plasticity including the reorganization of interneuronal sensory fields and the induction of a molecular marker of neural morphallaxis. Morphallactic changes occurred only in segments posterior to an EH. Neither EH formation, nor neural morphallaxis was observed after dorsal body lesions, indicating a role for nerve cord injury in morphallaxis induction. Furthermore, a hierarchical system of neurobehavioral control was observed, where anterior heads were dominant and an EH controlled body movements only in the absence of the anterior head. Both suppression of segmental regeneration and blockade of asexual fission, after treatment with boric acid, disrupted the maintenance of neural morphallaxis, but did not block its induction. Therefore, segmental regeneration (i.e., epimorphosis) may not be required for the induction of morphallactic remodeling of neural networks. However, on-going epimorphosis appears necessary for the long-term consolidation of cellular and molecular mechanisms underlying the morphallaxis of neural circuitry. PMID:18561185

Monte Carlo-based QA for IMRT of head and neck cancers

NASA Astrophysics Data System (ADS)

Tang, F.; Sham, J.; Ma, C.-M.; Li, J.-S.

2007-06-01

It is well-known that the presence of large air cavity in a dense medium (or patient) introduces significant electronic disequilibrium when irradiated with megavoltage X-ray field. This condition may worsen by the possible use of tiny beamlets in intensity-modulated radiation therapy (IMRT). Commercial treatment planning systems (TPSs), in particular those based on the pencil-beam method, do not provide accurate dose computation for the lungs and other cavity-laden body sites such as the head and neck. In this paper we present the use of Monte Carlo (MC) technique for dose re-calculation of IMRT of head and neck cancers. In our clinic, a turn-key software system is set up for MC calculation and comparison with TPS-calculated treatment plans as part of the quality assurance (QA) programme for IMRT delivery. A set of 10 off-the-self PCs is employed as the MC calculation engine with treatment plan parameters imported from the TPS via a graphical user interface (GUI) which also provides a platform for launching remote MC simulation and subsequent dose comparison with the TPS. The TPS-segmented intensity maps are used as input for the simulation hence skipping the time-consuming simulation of the multi-leaf collimator (MLC). The primary objective of this approach is to assess the accuracy of the TPS calculations in the presence of air cavities in the head and neck whereas the accuracy of leaf segmentation is verified by fluence measurement using a fluoroscopic camera-based imaging device. This measurement can also validate the correct transfer of intensity maps to the record and verify system. Comparisons between TPS and MC calculations of 6 MV IMRT for typical head and neck treatments review regional consistency in dose distribution except at and around the sinuses where our pencil-beam-based TPS sometimes over-predicts the dose by up to 10%, depending on the size of the cavities. In addition, dose re-buildup of up to 4% is observed at the posterior nasopharyngeal

Bracing of the trunk and neck has a differential effect on head control during gait

PubMed Central

Russell, D. M.; Kelleran, K.; Walker, M. L.

2015-01-01

During gait, the trunk and neck are believed to play an important role in dissipating the transmission of forces from the ground to the head. This attenuation process is important to ensure head control is maintained. The aim of the present study was to assess the impact of externally restricting the motion of the trunk and/or neck segments on acceleration patterns of the upper body and head and related trunk muscle activity. Twelve healthy adults performed three walking trials on a flat, straight 65-m walkway, under four different bracing conditions: 1) control-no brace; 2) neck-braced; 3) trunk-braced; and 4) neck-trunk braced. Three-dimensional acceleration from the head, neck (C7) and lower trunk (L3) were collected, as was muscle activity from trunk. Results revealed that, when the neck and/or trunk were singularly braced, an overall decrease in the ability of the trunk to attenuate gait-related oscillations was observed, which led to increases in the amplitude of vertical acceleration for all segments. However, when the trunk and neck were braced together, acceleration amplitude across all segments decreased in line with increased attenuation from the neck to the head. Bracing was also reflected by increased activity in erector spinae, decreased abdominal muscle activity and lower trunk muscle coactivation. Overall, it would appear that the neuromuscular system of young, healthy individuals was able to maintain a consistent pattern of head acceleration, irrespective of the level of bracing, and that priority was placed over the control of vertical head accelerations during these gait tasks. PMID:26180113

Head stabilization in whooping cranes

USGS Publications Warehouse

Kinloch, M.R.; Cronin, T.W.; Olsen, Glenn H.; Chavez-Ramirez, Felipe

2005-01-01

The whooping crane (Grus americana) is the tallest bird in North America, yet not much is known about its visual ecology. How these birds overcome their unusual height to identify, locate, track, and capture prey items is not well understood. There have been many studies on head and eye stabilization in large wading birds (herons and egrets), but the pattern of head movement and stabilization during foraging is unclear. Patterns of head movement and stabilization during walking were examined in whooping cranes at Patuxent Wildlife Research Center, Laurel, Maryland USA. Four whooping cranes (1 male and 3 females) were videotaped for this study. All birds were already acclimated to the presence of people and to food rewards. Whooping cranes were videotaped using both digital and Hi-8 Sony video cameras (Sony Corporation, 7-35 Kitashinagawa, 6-Chome, Shinagawa-ku, Tokyo, Japan), placed on a tripod and set at bird height in the cranes' home pens. The cranes were videotaped repeatedly, at different locations in the pens and while walking (or running) at different speeds. Rewards (meal worms, smelt, crickets and corn) were used to entice the cranes to walk across the camera's view plane. The resulting videotape was analyzed at the University of Maryland at Baltimore County. Briefly, we used a computerized reduced graphic model of a crane superimposed over each frame of analyzed tape segments by means of a custom written program (T. W. Cronin, using C++) with the ability to combine video and computer graphic input. The speed of the birds in analyzed segments ranged from 0.30 m/s to 2.64 m/s, and the proportion of time the head was stabilized ranged from 79% to 0%, respectively. The speed at which the proportion reached 0% was 1.83 m/s. The analyses suggest that the proportion of time the head is stable decreases as speed of the bird increases. In all cases, birds were able to reach their target prey with little difficulty. Thus when cranes are walking searching for food

Unsupervised segmentation with dynamical units.

PubMed

Rao, A Ravishankar; Cecchi, Guillermo A; Peck, Charles C; Kozloski, James R

2008-01-01

In this paper, we present a novel network to separate mixtures of inputs that have been previously learned. A significant capability of the network is that it segments the components of each input object that most contribute to its classification. The network consists of amplitude-phase units that can synchronize their dynamics, so that separation is determined by the amplitude of units in an output layer, and segmentation by phase similarity between input and output layer units. Learning is unsupervised and based on a Hebbian update, and the architecture is very simple. Moreover, efficient segmentation can be achieved even when there is considerable superposition of the inputs. The network dynamics are derived from an objective function that rewards sparse coding in the generalized amplitude-phase variables. We argue that this objective function can provide a possible formal interpretation of the binding problem and that the implementation of the network architecture and dynamics is biologically plausible.

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

Gland segmentation in prostate histopathological images

PubMed Central

Singh, Malay; Kalaw, Emarene Mationg; Giron, Danilo Medina; Chong, Kian-Tai; Tan, Chew Lim; Lee, Hwee Kuan

2017-01-01

Abstract. Glandular structural features are important for the tumor pathologist in the assessment of cancer malignancy of prostate tissue slides. The varying shapes and sizes of glands combined with the tedious manual observation task can result in inaccurate assessment. There are also discrepancies and low-level agreement among pathologists, especially in cases of Gleason pattern 3 and pattern 4 prostate adenocarcinoma. An automated gland segmentation system can highlight various glandular shapes and structures for further analysis by the pathologist. These objective highlighted patterns can help reduce the assessment variability. We propose an automated gland segmentation system. Forty-three hematoxylin and eosin-stained images were acquired from prostate cancer tissue slides and were manually annotated for gland, lumen, periacinar retraction clefting, and stroma regions. Our automated gland segmentation system was trained using these manual annotations. It identifies these regions using a combination of pixel and object-level classifiers by incorporating local and spatial information for consolidating pixel-level classification results into object-level segmentation. Experimental results show that our method outperforms various texture and gland structure-based gland segmentation algorithms in the literature. Our method has good performance and can be a promising tool to help decrease interobserver variability among pathologists. PMID:28653016

Automated segmentation and dose-volume analysis with DICOMautomaton

NASA Astrophysics Data System (ADS)

Clark, H.; Thomas, S.; Moiseenko, V.; Lee, R.; Gill, B.; Duzenli, C.; Wu, J.

2014-03-01

Purpose: Exploration of historical data for regional organ dose sensitivity is limited by the effort needed to (sub-)segment large numbers of contours. A system has been developed which can rapidly perform autonomous contour sub-segmentation and generic dose-volume computations, substantially reducing the effort required for exploratory analyses. Methods: A contour-centric approach is taken which enables lossless, reversible segmentation and dramatically reduces computation time compared with voxel-centric approaches. Segmentation can be specified on a per-contour, per-organ, or per-patient basis, and can be performed along either an embedded plane or in terms of the contour's bounds (e.g., split organ into fractional-volume/dose pieces along any 3D unit vector). More complex segmentation techniques are available. Anonymized data from 60 head-and-neck cancer patients were used to compare dose-volume computations with Varian's EclipseTM (Varian Medical Systems, Inc.). Results: Mean doses and Dose-volume-histograms computed agree strongly with Varian's EclipseTM. Contours which have been segmented can be injected back into patient data permanently and in a Digital Imaging and Communication in Medicine (DICOM)-conforming manner. Lossless segmentation persists across such injection, and remains fully reversible. Conclusions: DICOMautomaton allows researchers to rapidly, accurately, and autonomously segment large amounts of data into intricate structures suitable for analyses of regional organ dose sensitivity.

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.

Differential approach to strategies of segmental stabilisation in postural control.

PubMed

Isableu, Brice; Ohlmann, Théophile; Crémieux, Jacques; Amblard, Bernard

2003-05-01

The present paper attempts to clarify the between-subjects variability exhibited in both segmental stabilisation strategies and their subordinated or associated sensory contribution. Previous data have emphasised close relationships between the interindividual variability in both the visual control of posture and the spatial visual perception. In this study, we focused on the possible relationships that might link perceptual visual field dependence-independence and the visual contribution to segmental stabilisation strategies. Visual field dependent (FD) and field independent (FI) subjects were selected on the basis of their extreme score in a static rod and frame test where an estimation of the subjective vertical was required. In the postural test, the subjects stood in the sharpened Romberg position in darkness or under normal or stroboscopic illumination, in front of either a vertical or a tilted frame. Strategies of segmental stabilisation of the head, shoulders and hip in the roll plane were analysed by means of their anchoring index (AI). Our hypothesis was that FD subjects might use mainly visual cues for calibrating not only their spatial perception but also their strategies of segmental stabilisation. In the case of visual cue disturbances, a greater visual dependency to the strategies of segmental stabilisation in FD subjects should be validated by observing more systematic "en bloc" functioning (i.e. negative AI) between two adjacent segments. The main results are the following: 1. Strategies of segmental stabilisation differed between both groups and differences were amplified with the deprivation of either total vision and/or static visual cues. 2. In the absence of total vision and/or static visual cues, FD subjects have shown an increased efficiency of the hip stabilisation in space strategy and an "en bloc" operation of the shoulder-hip unit (whole trunk). The last "en bloc" operation was extended to the whole head-trunk unit in darkness

Self Occlusion and Disocclusion in Causal Video Object Segmentation

DTIC Science & Technology

2015-12-18

computation is parameter- free in contrast to [4, 32, 10]. Taylor et al . [30] perform layer segmentation in longer video sequences leveraging occlusion cues...shows that our method recovers from errors in the first frame (short of failed detection). 4413 image ground truth Lee et al . [19] Grundman et al . [14...Ochs et al . [23] Taylor et al . [30] ours Figure 7. Sample Visual Results on FBMS-59. Comparison of various state-of-the-art methods. Only a single

Intelligent multi-spectral IR image segmentation

NASA Astrophysics Data System (ADS)

Lu, Thomas; Luong, Andrew; Heim, Stephen; Patel, Maharshi; Chen, Kang; Chao, Tien-Hsin; Chow, Edward; Torres, Gilbert

2017-05-01

This article presents a neural network based multi-spectral image segmentation method. A neural network is trained on the selected features of both the objects and background in the longwave (LW) Infrared (IR) images. Multiple iterations of training are performed until the accuracy of the segmentation reaches satisfactory level. The segmentation boundary of the LW image is used to segment the midwave (MW) and shortwave (SW) IR images. A second neural network detects the local discontinuities and refines the accuracy of the local boundaries. This article compares the neural network based segmentation method to the Wavelet-threshold and Grab-Cut methods. Test results have shown increased accuracy and robustness of this segmentation scheme for multi-spectral IR images.

Utility of Readout-Segmented Echo-Planar Imaging-Based Diffusion Kurtosis Imaging for Differentiating Malignant from Benign Masses in Head and Neck Region.

PubMed

Ma, Gao; Xu, Xiao-Quan; Hu, Hao; Su, Guo-Yi; Shen, Jie; Shi, Hai-Bin; Wu, Fei-Yun

2018-01-01

To compare the diagnostic performance of readout-segmented echo-planar imaging (RS-EPI)-based diffusion kurtosis imaging (DKI) and that of diffusion-weighted imaging (DWI) for differentiating malignant from benign masses in head and neck region. Between December 2014 and April 2016, we retrospectively enrolled 72 consecutive patients with head and neck masses who had undergone RS-EPI-based DKI scan (b value of 0, 500, 1000, and 1500 s/mm 2 ) for pretreatment evaluation. Imaging data were post-processed by using monoexponential and diffusion kurtosis (DK) model for quantitation of apparent diffusion coefficient (ADC), apparent diffusion for Gaussian distribution (D app ), and apparent kurtosis coefficient (K app ). Unpaired t test and Mann-Whitney U test were used to compare differences of quantitative parameters between malignant and benign groups. Receiver operating characteristic curve analyses were performed to determine and compare the diagnostic ability of quantitative parameters in predicting malignancy. Malignant group demonstrated significantly lower ADC (0.754 ± 0.167 vs. 1.222 ± 0.420, p < 0.001) and D app (1.029 ± 0.226 vs. 1.640 ± 0.445, p < 0.001) while higher K app (1.344 ± 0.309 vs. 0.715 ± 0.249, p < 0.001) than benign group. Using a combination of D app and K app as diagnostic index, significantly better differentiating performance was achieved than using ADC alone (area under curve: 0.956 vs. 0.876, p = 0.042). Compared to DWI, DKI could provide additional data related to tumor heterogeneity with significantly better differentiating performance. Its derived quantitative metrics could serve as a promising imaging biomarker for differentiating malignant from benign masses in head and neck region.

Arizona Head Start for Homeless Children and Families Project. 1995-96 Evaluation Report.

ERIC Educational Resources Information Center

Mulholland, Lori

Homeless families with children constitute the fastest growing segment of the United States homeless population. This study evaluated Year 2 of the Arizona Head Start for Homeless Children and Families Project, designed to meet educational and social needs of homeless children and families, and to assist Head Start agencies in developing effective…

Arizona Head Start for Homeless Children and Families Project. 1994-95 Evaluation Report.

ERIC Educational Resources Information Center

Mulholland, Lori; Greene, Andrea

Homeless families with children comprise the fastest growing segment of the United States homeless population. This study evaluated Year 1 of the Arizona Head Start for Homeless Children and Families Project, designed to meet educational and social needs of homeless children and families, and to assist Head Start agencies in developing effective…

Miniplate with a bendable C-tube head allows the clinician to alter biomechanical advantage without physically moving the skeletal anchorage device.

PubMed

Seo, Kyung-Won; Ahn, Hyo-Won; Kim, Seong-Hun; Chung, Kyu-Rhim; Nelson, Gerald

2014-01-01

This article introduces a binary function of a miniplate with a bendable C-tube head used in corticotomy-assisted segment intrusion. The advantage of the device is that the point of force application can be altered without having to move the miniplate or place an additional anchorage device. Cases for this study were selected from patients who received perisegmental corticotomy with compression osteogenesis (Speedy Surgical Orthodontics) for segmental intrusion. For the skeletal anchorage on patients who received Speedy Surgical Orthodontics for posterior segment intrusion to improve on severe open bite correction, the C-tube was placed on the buccal wall of the maxilla for traction of orthopedic force as a temporary skeletal anchorage. The C-tube head portion is made with titanium grade II, which makes bending easy with a Weingart plier. This adjustment regains distance and range needed to continue intrusion of posterior segment. As an alternative to orthognathic surgery to correct a severe open bite, perisegmental corticotomy combined with orthopedic force application from a temporary skeletal anchorage device can be used. The corticotomy-assisted segment intrusion is a 2-stage procedure: first, the corticotomy is performed in the palate and 2 weeks later in the buccal alveolus. A C-plate was placed in the midpalatal area, and a C-tube was placed apical to the buccal corticotomy site. Elastics were used with orthopedic forces to induce compression osteogenesis. As the intrusion took place, the elastic stretched, and resultant force and range in the buccal segment decreased. The C-tube head was adjusted by bending to gain more distance, reviving the elastic force on the posterior segment until desired intrusion was accomplished. The miniplate with a bendable C-tube head serves for temporary skeletal anchorage of orthopedic traction force to achieve segmental intrusion and has the advantage that the bendable head can be adjusted to improve the force application for

Local and global evaluation for remote sensing image segmentation

NASA Astrophysics Data System (ADS)

Su, Tengfei; Zhang, Shengwei

2017-08-01

In object-based image analysis, how to produce accurate segmentation is usually a very important issue that needs to be solved before image classification or target recognition. The study for segmentation evaluation method is key to solving this issue. Almost all of the existent evaluation strategies only focus on the global performance assessment. However, these methods are ineffective for the situation that two segmentation results with very similar overall performance have very different local error distributions. To overcome this problem, this paper presents an approach that can both locally and globally quantify segmentation incorrectness. In doing so, region-overlapping metrics are utilized to quantify each reference geo-object's over and under-segmentation error. These quantified error values are used to produce segmentation error maps which have effective illustrative power to delineate local segmentation error patterns. The error values for all of the reference geo-objects are aggregated through using area-weighted summation, so that global indicators can be derived. An experiment using two scenes of very different high resolution images showed that the global evaluation part of the proposed approach was almost as effective as other two global evaluation methods, and the local part was a useful complement to comparing different segmentation results.

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

Virtual Surveyor based Object Extraction from Airborne LiDAR data

NASA Astrophysics Data System (ADS)

Habib, Md. Ahsan

Topographic feature detection of land cover from LiDAR data is important in various fields - city planning, disaster response and prevention, soil conservation, infrastructure or forestry. In recent years, feature classification, compliant with Object-Based Image Analysis (OBIA) methodology has been gaining traction in remote sensing and geographic information science (GIS). In OBIA, the LiDAR image is first divided into meaningful segments called object candidates. This results, in addition to spectral values, in a plethora of new information such as aggregated spectral pixel values, morphology, texture, context as well as topology. Traditional nonparametric segmentation methods rely on segmentations at different scales to produce a hierarchy of semantically significant objects. Properly tuned scale parameters are, therefore, imperative in these methods for successful subsequent classification. Recently, some progress has been made in the development of methods for tuning the parameters for automatic segmentation. However, researchers found that it is very difficult to automatically refine the tuning with respect to each object class present in the scene. Moreover, due to the relative complexity of real-world objects, the intra-class heterogeneity is very high, which leads to over-segmentation. Therefore, the method fails to deliver correctly many of the new segment features. In this dissertation, a new hierarchical 3D object segmentation algorithm called Automatic Virtual Surveyor based Object Extracted (AVSOE) is presented. AVSOE segments objects based on their distinct geometric concavity/convexity. This is achieved by strategically mapping the sloping surface, which connects the object to its background. Further analysis produces hierarchical decomposition of objects to its sub-objects at a single scale level. Extensive qualitative and qualitative results are presented to demonstrate the efficacy of this hierarchical segmentation approach.

Colour application on mammography image segmentation

NASA Astrophysics Data System (ADS)

Embong, R.; Aziz, N. M. Nik Ab.; Karim, A. H. Abd; Ibrahim, M. R.

2017-09-01

The segmentation process is one of the most important steps in image processing and computer vision since it is vital in the initial stage of image analysis. Segmentation of medical images involves complex structures and it requires precise segmentation result which is necessary for clinical diagnosis such as the detection of tumour, oedema, and necrotic tissues. Since mammography images are grayscale, researchers are looking at the effect of colour in the segmentation process of medical images. Colour is known to play a significant role in the perception of object boundaries in non-medical colour images. Processing colour images require handling more data, hence providing a richer description of objects in the scene. Colour images contain ten percent (10%) additional edge information as compared to their grayscale counterparts. Nevertheless, edge detection in colour image is more challenging than grayscale image as colour space is considered as a vector space. In this study, we implemented red, green, yellow, and blue colour maps to grayscale mammography images with the purpose of testing the effect of colours on the segmentation of abnormality regions in the mammography images. We applied the segmentation process using the Fuzzy C-means algorithm and evaluated the percentage of average relative error of area for each colour type. The results showed that all segmentation with the colour map can be done successfully even for blurred and noisy images. Also the size of the area of the abnormality region is reduced when compare to the segmentation area without the colour map. The green colour map segmentation produced the smallest percentage of average relative error (10.009%) while yellow colour map segmentation gave the largest percentage of relative error (11.367%).

Assessment of Multiresolution Segmentation for Extracting Greenhouses from WORLDVIEW-2 Imagery

NASA Astrophysics Data System (ADS)

Aguilar, M. A.; Aguilar, F. J.; García Lorca, A.; Guirado, E.; Betlej, M.; Cichon, P.; Nemmaoui, A.; Vallario, A.; Parente, C.

2016-06-01

The latest breed of very high resolution (VHR) commercial satellites opens new possibilities for cartographic and remote sensing applications. In this way, object based image analysis (OBIA) approach has been proved as the best option when working with VHR satellite imagery. OBIA considers spectral, geometric, textural and topological attributes associated with meaningful image objects. Thus, the first step of OBIA, referred to as segmentation, is to delineate objects of interest. Determination of an optimal segmentation is crucial for a good performance of the second stage in OBIA, the classification process. The main goal of this work is to assess the multiresolution segmentation algorithm provided by eCognition software for delineating greenhouses from WorldView- 2 multispectral orthoimages. Specifically, the focus is on finding the optimal parameters of the multiresolution segmentation approach (i.e., Scale, Shape and Compactness) for plastic greenhouses. The optimum Scale parameter estimation was based on the idea of local variance of object heterogeneity within a scene (ESP2 tool). Moreover, different segmentation results were attained by using different combinations of Shape and Compactness values. Assessment of segmentation quality based on the discrepancy between reference polygons and corresponding image segments was carried out to identify the optimal setting of multiresolution segmentation parameters. Three discrepancy indices were used: Potential Segmentation Error (PSE), Number-of-Segments Ratio (NSR) and Euclidean Distance 2 (ED2).

Medical image segmentation using 3D MRI data

NASA Astrophysics Data System (ADS)

Voronin, V.; Marchuk, V.; Semenishchev, E.; Cen, Yigang; Agaian, S.

2017-05-01

Precise segmentation of three-dimensional (3D) magnetic resonance imaging (MRI) image can be a very useful computer aided diagnosis (CAD) tool in clinical routines. Accurate automatic extraction a 3D component from images obtained by magnetic resonance imaging (MRI) is a challenging segmentation problem due to the small size objects of interest (e.g., blood vessels, bones) in each 2D MRA slice and complex surrounding anatomical structures. Our objective is to develop a specific segmentation scheme for accurately extracting parts of bones from MRI images. In this paper, we use a segmentation algorithm to extract the parts of bones from Magnetic Resonance Imaging (MRI) data sets based on modified active contour method. As a result, the proposed method demonstrates good accuracy in a comparison between the existing segmentation approaches on real MRI data.

Bioimpedance Measurement of Segmental Fluid Volumes and Hemodynamics

NASA Technical Reports Server (NTRS)

Montgomery, Leslie D.; Wu, Yi-Chang; Ku, Yu-Tsuan E.; Gerth, Wayne A.; DeVincenzi, D. (Technical Monitor)

2000-01-01

Bioimpedance has become a useful tool to measure changes in body fluid compartment volumes. An Electrical Impedance Spectroscopic (EIS) system is described that extends the capabilities of conventional fixed frequency impedance plethysmographic (IPG) methods to allow examination of the redistribution of fluids between the intracellular and extracellular compartments of body segments. The combination of EIS and IPG techniques was evaluated in the human calf, thigh, and torso segments of eight healthy men during 90 minutes of six degree head-down tilt (HDT). After 90 minutes HDT the calf and thigh segments significantly (P < 0.05) lost conductive volume (eight and four percent, respectively) while the torso significantly (P < 0.05) gained volume (approximately three percent). Hemodynamic responses calculated from pulsatile IPG data also showed a segmental pattern consistent with vascular fluid loss from the lower extremities and vascular engorgement in the torso. Lumped-parameter equivalent circuit analyses of EIS data for the calf and thigh indicated that the overall volume decreases in these segments arose from reduced extracellular volume that was not completely balanced by increased intracellular volume. The combined use of IPG and EIS techniques enables noninvasive tracking of multi-segment volumetric and hemodynamic responses to environmental and physiological stresses.

Added value of integrated circuit detector in head CT: objective and subjective image quality in comparison to conventional detector design.

PubMed

Korn, Andreas; Bender, Benjamin; Spira, Daniel; Schabel, Christoph; Bhadelia, Rafeeque; Claussen, Claus; Ernemann, Ulrike; Brodoefel, Harald

2014-12-01

A new computed tomography (CT) detector with integrated electric components and shorter conducting pathways has recently been introduced to decrease system inherent electronic noise. The purpose of this study was to assess the potential benefit of such integrated circuit detector (ICD) in head CT by comparing objective and subjective image quality in low-dose examinations with a conventional detector design. Using a conventional detector, reduced-dose noncontrast head CT (255 mAs; effective dose, 1.7 mSv) was performed in 25 consecutive patients. Following transition to ICD, 25 consecutive patients were scanned using identical imaging parameters. Images in both groups were reconstructed with iterative reconstruction (IR) and filtered back projection (FBP) and assessed in terms of quantitative and qualitative image quality. Acquisition of head CT using ICD increased signal-to-noise ratio of gray and white matter by 14% (10.0 ± 1.6 vs. 11.4 ± 2.5; P = .02) and 17% (8.2 ± 0.8 vs. 9.6 ± 1.5; P = .000). The associated improvement in contrast-to-noise ratio was 12% (2.0 ± 0.5 vs. 2.2 ± 0.6; P = .121). In addition, there was a 51% increase in objective image sharpness (582 ± 85 vs. 884.5 ± 191; change in HU/Pixel; P < .000). Compared to standard acquisitions, subjective grading of noise and overall image quality scores were significantly improved with ICD (2.1 ± 0.3 vs. 1.6 ± 0.3; P < .000; 2.0 ± 0.5 vs. 1.6 ± 0.3; P = .001). Moreover, streak artifacts in the posterior fossa were substantially reduced (2.3 ± 0.7 vs. 1.7 ± 0.5; P = .004). At the same radiation level, acquisition of head CT with ICD achieves superior objective and subjective image quality and provides potential for significant dose reduction. Copyright © 2014 AUR. Published by Elsevier Inc. All rights reserved.

Automated 3D segmentation of intraretinal layers from optic nerve head optical coherence tomography images

NASA Astrophysics Data System (ADS)

Antony, Bhavna J.; Abràmoff, Michael D.; Lee, Kyungmoo; Sonkova, Pavlina; Gupta, Priya; Kwon, Young; Niemeijer, Meindert; Hu, Zhihong; Garvin, Mona K.

2010-03-01

Optical coherence tomography (OCT), being a noninvasive imaging modality, has begun to find vast use in the diagnosis and management of ocular diseases such as glaucoma, where the retinal nerve fiber layer (RNFL) has been known to thin. Furthermore, the recent availability of the considerably larger volumetric data with spectral-domain OCT has increased the need for new processing techniques. In this paper, we present an automated 3-D graph-theoretic approach for the segmentation of 7 surfaces (6 layers) of the retina from 3-D spectral-domain OCT images centered on the optic nerve head (ONH). The multiple surfaces are detected simultaneously through the computation of a minimum-cost closed set in a vertex-weighted graph constructed using edge/regional information, and subject to a priori determined varying surface interaction and smoothness constraints. The method also addresses the challenges posed by presence of the large blood vessels and the optic disc. The algorithm was compared to the average manual tracings of two observers on a total of 15 volumetric scans, and the border positioning error was found to be 7.25 +/- 1.08 μm and 8.94 +/- 3.76 μm for the normal and glaucomatous eyes, respectively. The RNFL thickness was also computed for 26 normal and 70 glaucomatous scans where the glaucomatous eyes showed a significant thinning (p < 0.01, mean thickness 73.7 +/- 32.7 μm in normal eyes versus 60.4 +/- 25.2 μm in glaucomatous eyes).

Quantitative assessment in thermal image segmentation for artistic objects

NASA Astrophysics Data System (ADS)

Yousefi, Bardia; Sfarra, Stefano; Maldague, Xavier P. V.

2017-07-01

The application of the thermal and infrared technology in different areas of research is considerably increasing. These applications involve Non-destructive Testing (NDT), Medical analysis (Computer Aid Diagnosis/Detection- CAD), Arts and Archaeology among many others. In the arts and archaeology field, infrared technology provides significant contributions in term of finding defects of possible impaired regions. This has been done through a wide range of different thermographic experiments and infrared methods. The proposed approach here focuses on application of some known factor analysis methods such as standard Non-Negative Matrix Factorization (NMF) optimized by gradient-descent-based multiplicative rules (SNMF1) and standard NMF optimized by Non-negative least squares (NNLS) active-set algorithm (SNMF2) and eigen decomposition approaches such as Principal Component Thermography (PCT), Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT) to obtain the thermal features. On one hand, these methods are usually applied as preprocessing before clustering for the purpose of segmentation of possible defects. On the other hand, a wavelet based data fusion combines the data of each method with PCT to increase the accuracy of the algorithm. The quantitative assessment of these approaches indicates considerable segmentation along with the reasonable computational complexity. It shows the promising performance and demonstrated a confirmation for the outlined properties. In particular, a polychromatic wooden statue and a fresco were analyzed using the above mentioned methods and interesting results were obtained.

Multi-scale image segmentation method with visual saliency constraints and its application

NASA Astrophysics Data System (ADS)

Chen, Yan; Yu, Jie; Sun, Kaimin

2018-03-01

Object-based image analysis method has many advantages over pixel-based methods, so it is one of the current research hotspots. It is very important to get the image objects by multi-scale image segmentation in order to carry out object-based image analysis. The current popular image segmentation methods mainly share the bottom-up segmentation principle, which is simple to realize and the object boundaries obtained are accurate. However, the macro statistical characteristics of the image areas are difficult to be taken into account, and fragmented segmentation (or over-segmentation) results are difficult to avoid. In addition, when it comes to information extraction, target recognition and other applications, image targets are not equally important, i.e., some specific targets or target groups with particular features worth more attention than the others. To avoid the problem of over-segmentation and highlight the targets of interest, this paper proposes a multi-scale image segmentation method with visually saliency graph constraints. Visual saliency theory and the typical feature extraction method are adopted to obtain the visual saliency information, especially the macroscopic information to be analyzed. The visual saliency information is used as a distribution map of homogeneity weight, where each pixel is given a weight. This weight acts as one of the merging constraints in the multi- scale image segmentation. As a result, pixels that macroscopically belong to the same object but are locally different can be more likely assigned to one same object. In addition, due to the constraint of visual saliency model, the constraint ability over local-macroscopic characteristics can be well controlled during the segmentation process based on different objects. These controls will improve the completeness of visually saliency areas in the segmentation results while diluting the controlling effect for non- saliency background areas. Experiments show that this method works

Segments on Western Rim of Endeavour Crater, Mars

NASA Image and Video Library

2017-04-19

This orbital image of the western rim of Mars' Endeavour Crater covers an area about 5 miles (8 kilometers) east-west by about 9 miles (14 kilometers) north-south and indicates the names of some of the raised segments of the rim. NASA's Mars Exploration Rover Opportunity arrived at Endeavour in 2011 after exploring smaller craters to the northwest during its first six years on Mars. It initially explored the "Cape York" segment, then headed south. It reached the northern end of "Cape Tribulation" in late 2014 and the southern tip of that segment in April 2017. A key destination in the "Cape Byron" segment is "Perseverance Valley," where the rover team plans to investigate whether the valley was carved by water, wind or a debris flow initiated by water. This image is from the Context Camera on NASA's Mars Reconnaissance Orbiter. Malin Space Science Systems, San Diego, California, built and operates that camera. NASA's Jet Propulsion Laboratory, a division of Caltech in Pasadena, California, built and operates Opportunity. https://photojournal.jpl.nasa.gov/catalog/PIA21490

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.

Patterns of local-regional recurrence following parotid-sparing conformal and segmental intensity-modulated radiotherapy for head and neck cancer.

PubMed

Dawson, L A; Anzai, Y; Marsh, L; Martel, M K; Paulino, A; Ship, J A; Eisbruch, A

2000-03-15

To analyze the patterns of local-regional recurrence in patients with head and neck cancer treated with parotid-sparing conformal and segmental intensity-modulated radiotherapy (IMRT). Fifty-eight patients with head and neck cancer were treated with bilateral neck radiation (RT) using conformal or segmental IMRT techniques, while sparing a substantial portion of one parotid gland. The targets for CT-based RT planning included the gross tumor volume (GTV) (primary tumor and lymph node metastases) and the clinical target volume (CTV) (postoperative tumor bed, expansions of the GTVs and lymph node groups at risk of subclinical disease). Lymph node targets at risk of subclinical disease included the bilateral jugulodigastric and lower jugular lymph nodes, bilateral retropharyngeal lymph nodes at risk, and high jugular nodes at the base of skull in the side of the neck at highest risk (containing clinical neck metastases and/or ipsilateral to the primary tumor). The CTVs were expanded by 5 mm to yield planning target volumes (PTVs). Planning goals included coverage of all PTVs (with a minimum of 95% of the prescribed dose) and sparing of a substantial portion of the parotid gland in the side of the neck at less risk. The median RT doses to the gross tumor, the operative bed, and the subclinical disease PTVs were 70.4 Gy, 61.2 Gy, and 50.4 Gy respectively. All recurrences were defined on CT scans obtained at the time of recurrence, transferred to the pretreatment CT dataset used for RT planning, and analyzed using dose-volume histograms. The recurrences were classified as 1) "in-field," in which 95% or more of the recurrence volume (V(recur)) was within the 95% isodose; 2) "marginal," in which 20% to 95% of V(recur) was within the 95% isodose; or 3) "outside," in which less than 20% of V(recur) was within the 95% isodose. With a median follow-up of 27 months (range 6 to 60 months), 10 regional recurrences, 5 local recurrences (including one noninvasive recurrence) and 1

On the evaluation of segmentation editing tools

PubMed Central

Heckel, Frank; Moltz, Jan H.; Meine, Hans; Geisler, Benjamin; Kießling, Andreas; D’Anastasi, Melvin; dos Santos, Daniel Pinto; Theruvath, Ashok Joseph; Hahn, Horst K.

2014-01-01

Abstract. Efficient segmentation editing tools are important components in the segmentation process, as no automatic methods exist that always generate sufficient results. Evaluating segmentation editing algorithms is challenging, because their quality depends on the user’s subjective impression. So far, no established methods for an objective, comprehensive evaluation of such tools exist and, particularly, intermediate segmentation results are not taken into account. We discuss the evaluation of editing algorithms in the context of tumor segmentation in computed tomography. We propose a rating scheme to qualitatively measure the accuracy and efficiency of editing tools in user studies. In order to objectively summarize the overall quality, we propose two scores based on the subjective rating and the quantified segmentation quality over time. Finally, a simulation-based evaluation approach is discussed, which allows a more reproducible evaluation without the need for human input. This automated evaluation complements user studies, allowing a more convincing evaluation, particularly during development, where frequent user studies are not possible. The proposed methods have been used to evaluate two dedicated editing algorithms on 131 representative tumor segmentations. We show how the comparison of editing algorithms benefits from the proposed methods. Our results also show the correlation of the suggested quality score with the qualitative ratings. PMID:26158063

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

Figure-ground segmentation based on class-independent shape priors

NASA Astrophysics Data System (ADS)

Li, Yang; Liu, Yang; Liu, Guojun; Guo, Maozu

2018-01-01

We propose a method to generate figure-ground segmentation by incorporating shape priors into the graph-cuts algorithm. Given an image, we first obtain a linear representation of an image and then apply directional chamfer matching to generate class-independent, nonparametric shape priors, which provide shape clues for the graph-cuts algorithm. We then enforce shape priors in a graph-cuts energy function to produce object segmentation. In contrast to previous segmentation methods, the proposed method shares shape knowledge for different semantic classes and does not require class-specific model training. Therefore, the approach obtains high-quality segmentation for objects. We experimentally validate that the proposed method outperforms previous approaches using the challenging PASCAL VOC 2010/2012 and Berkeley (BSD300) segmentation datasets.

Development of the head and trunk mesoderm in the dogfish, Scyliorhinus torazame: II. Comparison of gene expression between the head mesoderm and somites with reference to the origin of the vertebrate head.

PubMed

Adachi, Noritaka; Takechi, Masaki; Hirai, Tamami; Kuratani, Shigeru

2012-01-01

The vertebrate mesoderm differs distinctly between the head and trunk, and the evolutionary origin of the head mesoderm remains enigmatic. Although the presence of somite-like segmentation in the head mesoderm of model animals is generally denied at molecular developmental levels, the appearance of head cavities in elasmobranch embryos has not been explained, and the possibility that they may represent vestigial head somites once present in an amphioxus-like ancestor has not been ruled out entirely. To examine whether the head cavities in the shark embryo exhibit any molecular signatures reminiscent of trunk somites, we isolated several developmentally key genes, including Pax1, Pax3, Pax7, Pax9, Myf5, Sonic hedgehog, and Patched2, which are involved in myogenic and chondrogenic differentiation in somites, and Pitx2, Tbx1, and Engrailed2, which are related to the patterning of the head mesoderm, from an elasmobranch species, Scyliorhinus torazame. Observation of the expression patterns of these genes revealed that most were expressed in patterns that resembled those found in amniote embryos. In addition, the head cavities did not exhibit an overt similarity to somites; that is, the similarity was no greater than that of the unsegmented head mesoderm in other vertebrates. Moreover, the shark head mesoderm showed an amniote-like somatic/visceral distinction according to the expression of Pitx2, Tbx1, and Engrailed2. We conclude that the head cavities do not represent a manifestation of ancestral head somites; rather, they are more likely to represent a derived trait obtained in the lineage of gnathostomes. © 2012 Wiley Periodicals, Inc.

A shape-based segmentation method for mobile laser scanning point clouds

NASA Astrophysics Data System (ADS)

Yang, Bisheng; Dong, Zhen

2013-07-01

Segmentation of mobile laser point clouds of urban scenes into objects is an important step for post-processing (e.g., interpretation) of point clouds. Point clouds of urban scenes contain numerous objects with significant size variability, complex and incomplete structures, and holes or variable point densities, raising great challenges for the segmentation of mobile laser point clouds. This paper addresses these challenges by proposing a shape-based segmentation method. The proposed method first calculates the optimal neighborhood size of each point to derive the geometric features associated with it, and then classifies the point clouds according to geometric features using support vector machines (SVMs). Second, a set of rules are defined to segment the classified point clouds, and a similarity criterion for segments is proposed to overcome over-segmentation. Finally, the segmentation output is merged based on topological connectivity into a meaningful geometrical abstraction. The proposed method has been tested on point clouds of two urban scenes obtained by different mobile laser scanners. The results show that the proposed method segments large-scale mobile laser point clouds with good accuracy and computationally effective time cost, and that it segments pole-like objects particularly well.

Adapted head- and eye-movement responses to added-head inertia

NASA Technical Reports Server (NTRS)

Gauthier, G. M.; Martin, B. J.; Stark, L. W.

1986-01-01

Adaptation to inertia added to the head was studied in men by mounting masses on a rigidly attached helmet until two- to ten-fold increases of inertia were produced, while an overhead suspension compensated for the weights. The observed changes in the eye and head movement coordination included increased head movement latencies, as well as changes in the eye movement amplitude, and later stabilizing alternate contractions of the neck muscles. Oscillopsia, or continual displacement or instability of the visual world, which is a symptom of a breakdown of space constancy, was prominent and consistent in the perceptual reports of the subjects. Although adaptation resulting from adding inertia to the head occurred much faster than that induced by adding prisms or lenses, it has similar perceptual and motor components that may be objectively studied in detail.

Moving vehicles segmentation based on Gaussian motion model

NASA Astrophysics Data System (ADS)

Zhang, Wei; Fang, Xiang Z.; Lin, Wei Y.

2005-07-01

Moving objects segmentation is a challenge in computer vision. This paper focuses on the segmentation of moving vehicles in dynamic scene. We analyses the psychology of human vision and present a framework for segmenting moving vehicles in the highway. The proposed framework consists of two parts. Firstly, we propose an adaptive background update method in which the background is updated according to the change of illumination conditions and thus can adapt to the change of illumination sensitively. Secondly, we construct a Gaussian motion model to segment moving vehicles, in which the motion vectors of the moving pixels are modeled as a Gaussian model and an on-line EM algorithm is used to update the model. The Gaussian distribution of the adaptive model is elevated to determine which moving vectors result from moving vehicles and which from other moving objects such as waving trees. Finally, the pixels with motion vector result from the moving vehicles are segmented. Experimental results of several typical scenes show that the proposed model can detect the moving vehicles correctly and is immune from influence of the moving objects caused by the waving trees and the vibration of camera.

[Definition of nodal volumes in breast cancer treatment and segmentation guidelines].

PubMed

Kirova, Y M; Castro Pena, P; Dendale, R; Campana, F; Bollet, M A; Fournier-Bidoz, N; Fourquet, A

2009-06-01

To assist in the determination of breast and nodal volumes in the setting of radiotherapy for breast cancer and establish segmentation guidelines. Materials and methods. Contrast metarial enhanced CT examinations were obtained in the treatment position in 25 patients to clearly define the target volumes. The clinical target volume (CTV) including the breast, internal mammary nodes, supraclavicular and subclavicular regions and axxilary region were segmented along with the brachial plexus and interpectoral nodes. The following critical organs were also segmented: heart, lungs, contralateral breast, thyroid, esophagus and humeral head. A correlation between clinical and imaging findings and meeting between radiation oncologists and breast specialists resulted in a better definition of irradiation volumes for breast and nodes with establishement of segmentation guidelines and creation of an anatomical atlas. A practical approach, based on anatomical criteria, is proposed to assist in the segmentation of breast and node volumes in the setting of breast cancer treatment along with a definition of irradiation volumes.

View-Invariant Gait Recognition Through Genetic Template Segmentation

NASA Astrophysics Data System (ADS)

Isaac, Ebenezer R. H. P.; Elias, Susan; Rajagopalan, Srinivasan; Easwarakumar, K. S.

2017-08-01

Template-based model-free approach provides by far the most successful solution to the gait recognition problem in literature. Recent work discusses how isolating the head and leg portion of the template increase the performance of a gait recognition system making it robust against covariates like clothing and carrying conditions. However, most involve a manual definition of the boundaries. The method we propose, the genetic template segmentation (GTS), employs the genetic algorithm to automate the boundary selection process. This method was tested on the GEI, GEnI and AEI templates. GEI seems to exhibit the best result when segmented with our approach. Experimental results depict that our approach significantly outperforms the existing implementations of view-invariant gait recognition.

Molecular Age-Related Changes in the Anterior Segment of the Eye.

PubMed

Hernandez-Zimbron, Luis Fernando; Gulias-Cañizo, Rosario; Golzarri, María F; Martínez-Báez, Blanca Elizabeth; Quiroz-Mercado, Hugo; Gonzalez-Salinas, Roberto

2017-01-01

To examine the current knowledge about the age-related processes in the anterior segment of the eye at a biological, clinical, and molecular level. We reviewed the available published literature that addresses the aging process of the anterior segment of the eye and its associated molecular and physiological events. We performed a search on PubMed, CINAHL, and Embase using the MeSH terms "eye," "anterior segment," and "age." We generated searches to account for synonyms of these keywords and MESH headings as follows: (1) "Eye" AND "ageing process" OR "anterior segment ageing" and (2) "Anterior segment" AND "ageing process" OR "anterior segment" AND "molecular changes" AND "age." Results . Among the principal causes of age-dependent alterations in the anterior segment of the eye, we found the mutation of the TGF- β gene and loss of autophagy in addition to oxidative stress, which contributes to the pathogenesis of degenerative diseases. In this review, we summarize the current knowledge regarding some of the molecular mechanisms related to aging in the anterior segment of the eye. We also introduce and propose potential roles of autophagy, an important mechanism responsible for maintaining homeostasis and proteostasis under stress conditions in the anterior segment during aging.

Homeotic genes and the arthropod head: Expression patterns of the labial, proboscipedia, and Deformed genes in crustaceans and insects

PubMed Central

Abzhanov, Arhat; Kaufman, Thomas C.

1999-01-01

cDNA fragments of the homologues of the Drosophila head homeotic genes labial (lab), proboscipedia (pb), and Deformed (Dfd) have been isolated from the crustacean Porcellio scaber. Because the accumulation domains of the head homeotic complex (Hox) genes had not been previously reported for crustaceans, we studied the expression patterns of these genes in P. scaber embryos by using in situ hybridization. The P. scaber lab homologue is expressed in the developing second antennal segment and its appendages. This expression domain in crustaceans and in the homologous intercalary segment of insects suggests that the lab gene specified this metamere in the last common ancestor of these two groups. The expression domain of the P. scaber pb gene is in the posterior part of the second antennal segment. This domain, in contrast to that in insects, is colinear with the domains of other head genes in P. scaber, and it differs from the insect pb gene expression domain in the posterior mouthparts, suggesting that the insect and crustacean patterns evolved independently from a broader ancestral domain similar to that found in modern chelicerates. P. scaber Dfd is expressed in the mandibular segment and paragnaths (a pair of ventral mouthpart structures associated with the stomodeum) and differs from insects, where expression is in the mandibular and maxillary segments. Thus, like pb, Dfd shows a divergent Hox gene deployment. We conclude that homologous structures of the mandibulate head display striking differences in their underlying developmental programs related to Hox gene expression. PMID:10468590

Metric Learning to Enhance Hyperspectral Image Segmentation

NASA Technical Reports Server (NTRS)

Thompson, David R.; Castano, Rebecca; Bue, Brian; Gilmore, Martha S.

2013-01-01

Unsupervised hyperspectral image segmentation can reveal spatial trends that show the physical structure of the scene to an analyst. They highlight borders and reveal areas of homogeneity and change. Segmentations are independently helpful for object recognition, and assist with automated production of symbolic maps. Additionally, a good segmentation can dramatically reduce the number of effective spectra in an image, enabling analyses that would otherwise be computationally prohibitive. Specifically, using an over-segmentation of the image instead of individual pixels can reduce noise and potentially improve the results of statistical post-analysis. In this innovation, a metric learning approach is presented to improve the performance of unsupervised hyperspectral image segmentation. The prototype demonstrations attempt a superpixel segmentation in which the image is conservatively over-segmented; that is, the single surface features may be split into multiple segments, but each individual segment, or superpixel, is ensured to have homogenous mineralogy.

Noise destroys feedback enhanced figure-ground segmentation but not feedforward figure-ground segmentation.

PubMed

Romeo, August; Arall, Marina; Supèr, Hans

2012-01-01

Figure-ground (FG) segmentation is the separation of visual information into background and foreground objects. In the visual cortex, FG responses are observed in the late stimulus response period, when neurons fire in tonic mode, and are accompanied by a switch in cortical state. When such a switch does not occur, FG segmentation fails. Currently, it is not known what happens in the brain on such occasions. A biologically plausible feedforward spiking neuron model was previously devised that performed FG segmentation successfully. After incorporating feedback the FG signal was enhanced, which was accompanied by a change in spiking regime. In a feedforward model neurons respond in a bursting mode whereas in the feedback model neurons fired in tonic mode. It is known that bursts can overcome noise, while tonic firing appears to be much more sensitive to noise. In the present study, we try to elucidate how the presence of noise can impair FG segmentation, and to what extent the feedforward and feedback pathways can overcome noise. We show that noise specifically destroys the feedback enhanced FG segmentation and leaves the feedforward FG segmentation largely intact. Our results predict that noise produces failure in FG perception.

Noise destroys feedback enhanced figure-ground segmentation but not feedforward figure-ground segmentation

PubMed Central

Romeo, August; Arall, Marina; Supèr, Hans

2012-01-01

Figure-ground (FG) segmentation is the separation of visual information into background and foreground objects. In the visual cortex, FG responses are observed in the late stimulus response period, when neurons fire in tonic mode, and are accompanied by a switch in cortical state. When such a switch does not occur, FG segmentation fails. Currently, it is not known what happens in the brain on such occasions. A biologically plausible feedforward spiking neuron model was previously devised that performed FG segmentation successfully. After incorporating feedback the FG signal was enhanced, which was accompanied by a change in spiking regime. In a feedforward model neurons respond in a bursting mode whereas in the feedback model neurons fired in tonic mode. It is known that bursts can overcome noise, while tonic firing appears to be much more sensitive to noise. In the present study, we try to elucidate how the presence of noise can impair FG segmentation, and to what extent the feedforward and feedback pathways can overcome noise. We show that noise specifically destroys the feedback enhanced FG segmentation and leaves the feedforward FG segmentation largely intact. Our results predict that noise produces failure in FG perception. PMID:22934028

SU-E-T-605: Performance Evaluation of MLC Leaf-Sequencing Algorithms in Head-And-Neck IMRT

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

Jing, J; Lin, H; Chow, J

2015-06-15

Purpose: To investigate the efficiency of three multileaf collimator (MLC) leaf-sequencing algorithms proposed by Galvin et al, Chen et al and Siochi et al using external beam treatment plans for head-and-neck intensity modulated radiation therapy (IMRT). Methods: IMRT plans for head-and-neck were created using the CORVUS treatment planning system. The plans were optimized and the fluence maps for all photon beams determined. Three different MLC leaf-sequencing algorithms based on Galvin et al, Chen et al and Siochi et al were used to calculate the final photon segmental fields and their monitor units in delivery. For comparison purpose, the maximum intensitymore » of fluence map was kept constant in different plans. The number of beam segments and total number of monitor units were calculated for the three algorithms. Results: From results of number of beam segments and total number of monitor units, we found that algorithm of Galvin et al had the largest number of monitor unit which was about 70% larger than the other two algorithms. Moreover, both algorithms of Galvin et al and Siochi et al have relatively lower number of beam segment compared to Chen et al. Although values of number of beam segment and total number of monitor unit calculated by different algorithms varied with the head-and-neck plans, it can be seen that algorithms of Galvin et al and Siochi et al performed well with a lower number of beam segment, though algorithm of Galvin et al had a larger total number of monitor units than Siochi et al. Conclusion: Although performance of the leaf-sequencing algorithm varied with different IMRT plans having different fluence maps, an evaluation is possible based on the calculated number of beam segment and monitor unit. In this study, algorithm by Siochi et al was found to be more efficient in the head-and-neck IMRT. The Project Sponsored by the Fundamental Research Funds for the Central Universities (J2014HGXJ0094) and the Scientific Research Foundation

Minimizing Head Acceleration in Soccer: A Review of the Literature.

PubMed

Caccese, Jaclyn B; Kaminski, Thomas W

2016-11-01

Physicians and healthcare professionals are often asked for recommendations on how to keep athletes safe during contact sports such as soccer. With an increase in concussion awareness and concern about repetitive subconcussion, many parents and athletes are interested in mitigating head acceleration in soccer, so we conducted a literature review on factors that affect head acceleration in soccer. We searched electronic databases and reference lists to find studies using the keywords 'soccer' OR 'football' AND 'head acceleration'. Because of a lack of current research in soccer heading biomechanics, this review was limited to 18 original research studies. Low head-neck segment mass predisposes athletes to high head acceleration, but head-neck-torso alignment during heading and follow-through after contact can be used to decrease head acceleration. Additionally, improvements in symmetric neck flexor and extensor strength and neuromuscular neck stiffness can decrease head acceleration. Head-to-head impacts and unanticipated ball contacts result in the highest head acceleration. Ball contacts at high velocity may also be dangerous. The risk of concussive impacts may be lessened through the use of headgear, but headgear may also cause athletes to play more recklessly because they feel a sense of increased security. Young, but physically capable, athletes should be taught proper heading technique in a controlled setting, using a carefully planned progression of the skill.

The role of the notochord in amniote vertebral column segmentation.

PubMed

Ward, Lizzy; Pang, Angel S W; Evans, Susan E; Stern, Claudio D

2018-07-01

The vertebral column is segmented, comprising an alternating series of vertebrae and intervertebral discs along the head-tail axis. The vertebrae and outer portion (annulus fibrosus) of the disc are derived from the sclerotome part of the somites, whereas the inner nucleus pulposus of the disc is derived from the notochord. Here we investigate the role of the notochord in vertebral patterning through a series of microsurgical experiments in chick embryos. Ablation of the notochord causes loss of segmentation of vertebral bodies and discs. However, the notochord cannot segment in the absence of the surrounding sclerotome. To test whether the notochord dictates sclerotome segmentation, we grafted an ectopic notochord. We find that the intrinsic segmentation of the sclerotome is dominant over any segmental information the notochord may possess, and no evidence that the chick notochord is intrinsically segmented. We propose that the segmental pattern of vertebral bodies and discs in chick is dictated by the sclerotome, which first signals to the notochord to ensure that the nucleus pulposus develops in register with the somite-derived annulus fibrosus. Later, the notochord is required for maintenance of sclerotome segmentation as the mature vertebral bodies and intervertebral discs form. These results highlight differences in vertebral development between amniotes and teleosts including zebrafish, where the notochord dictates the segmental pattern. The relative importance of the sclerotome and notochord in vertebral patterning has changed significantly during evolution. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

3D surface voxel tracing corrector for accurate bone segmentation.

PubMed

Guo, Haoyan; Song, Sicong; Wang, Jinke; Guo, Maozu; Cheng, Yuanzhi; Wang, Yadong; Tamura, Shinichi

2018-06-18

For extremely close bones, their boundaries are weak and diffused due to strong interaction between adjacent surfaces. These factors prevent the accurate segmentation of bone structure. To alleviate these difficulties, we propose an automatic method for accurate bone segmentation. The method is based on a consideration of the 3D surface normal direction, which is used to detect the bone boundary in 3D CT images. Our segmentation method is divided into three main stages. Firstly, we consider a surface tracing corrector combined with Gaussian standard deviation [Formula: see text] to improve the estimation of normal direction. Secondly, we determine an optimal value of [Formula: see text] for each surface point during this normal direction correction. Thirdly, we construct the 1D signal and refining the rough boundary along the corrected normal direction. The value of [Formula: see text] is used in the first directional derivative of the Gaussian to refine the location of the edge point along accurate normal direction. Because the normal direction is corrected and the value of [Formula: see text] is optimized, our method is robust to noise images and narrow joint space caused by joint degeneration. We applied our method to 15 wrists and 50 hip joints for evaluation. In the wrist segmentation, Dice overlap coefficient (DOC) of [Formula: see text]% was obtained by our method. In the hip segmentation, fivefold cross-validations were performed for two state-of-the-art methods. Forty hip joints were used for training in two state-of-the-art methods, 10 hip joints were used for testing and performing comparisons. The DOCs of [Formula: see text], [Formula: see text]%, and [Formula: see text]% were achieved by our method for the pelvis, the left femoral head and the right femoral head, respectively. Our method was shown to improve segmentation accuracy for several specific challenging cases. The results demonstrate that our approach achieved a superior accuracy over two

Duodenum-preserving total pancreatic head resection for benign cystic neoplastic lesions.

PubMed

Beger, Hans G; Schwarz, Michael; Poch, Bertram

2012-11-01

Cystic neoplasms of the pancreas are diagnosed frequently due to early use of abdominal imaging techniques. Intraductal papillary mucinous neoplasm, mucinous cystic neoplasm, and serous pseudopapillary neoplasia are considered pre-cancerous lesions because of frequent transformation to cancer. Complete surgical resection of the benign lesion is a pancreatic cancer preventive treatment. The application for a limited surgical resection for the benign lesions is increasingly used to reduce the surgical trauma with a short- and long-term benefit compared to major surgical procedures. Duodenum-preserving total pancreatic head resection introduced for inflammatory tumors in the pancreatic head transfers to the patient with a benign cystic lesion located in the pancreatic head, the advantages of a minimalized surgical treatment. Based on the experience of 17 patients treated for cystic neoplastic lesions with duodenum-preserving total pancreatic head resection, the surgical technique of total pancreatic head resection for adenoma, borderline tumors, and carcinoma in situ of cystic neoplasm is presented. A segmental resection of the peripapillary duodenum is recommended in case of suspected tissue ischemia of the peripapillary duodenum. In 305 patients, collected from the literature by PubMed search, in about 40% of the patients a segmental resection of the duodenum and 60% a duodenum and common bile duct-preserving total pancreatic head resection has been performed. Hospital mortality of the 17 patients was 0%. In 305 patients collected, the hospital mortality was 0.65%, 13.2% experienced a delay of gastric emptying and a pancreatic fistula in 18.2%. Recurrence of the disease was 1.5%. Thirty-two of 175 patients had carcinoma in situ. Duodenum-preserving total pancreatic head resection for benign cystic neoplastic lesions is a safe surgical procedure with low post-operative morbidity and mortality.

An Analysis of Image Segmentation Time in Beam’s-Eye-View Treatment Planning

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

Li, Chun; Spelbring, D.R.; Chen, George T.Y.

In this work we tabulate and histogram the image segmentation time for beam’s eye view (BEV) treatment planning in our center. The average time needed to generate contours on CT images delineating normal structures and treatment target volumes is calculated using a data base containing over 500 patients’ BEV plans. The average number of contours and total image segmentation time needed for BEV plans in three common treatment sites, namely, head/neck, lung/chest, and prostate, were estimated.

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.

Survey statistics of automated segmentations applied to optical imaging of mammalian cells.

PubMed

Bajcsy, Peter; Cardone, Antonio; Chalfoun, Joe; Halter, Michael; Juba, Derek; Kociolek, Marcin; Majurski, Michael; Peskin, Adele; Simon, Carl; Simon, Mylene; Vandecreme, Antoine; Brady, Mary

2015-10-15

The goal of this survey paper is to overview cellular measurements using optical microscopy imaging followed by automated image segmentation. The cellular measurements of primary interest are taken from mammalian cells and their components. They are denoted as two- or three-dimensional (2D or 3D) image objects of biological interest. In our applications, such cellular measurements are important for understanding cell phenomena, such as cell counts, cell-scaffold interactions, cell colony growth rates, or cell pluripotency stability, as well as for establishing quality metrics for stem cell therapies. In this context, this survey paper is focused on automated segmentation as a software-based measurement leading to quantitative cellular measurements. We define the scope of this survey and a classification schema first. Next, all found and manually filteredpublications are classified according to the main categories: (1) objects of interests (or objects to be segmented), (2) imaging modalities, (3) digital data axes, (4) segmentation algorithms, (5) segmentation evaluations, (6) computational hardware platforms used for segmentation acceleration, and (7) object (cellular) measurements. Finally, all classified papers are converted programmatically into a set of hyperlinked web pages with occurrence and co-occurrence statistics of assigned categories. The survey paper presents to a reader: (a) the state-of-the-art overview of published papers about automated segmentation applied to optical microscopy imaging of mammalian cells, (b) a classification of segmentation aspects in the context of cell optical imaging, (c) histogram and co-occurrence summary statistics about cellular measurements, segmentations, segmented objects, segmentation evaluations, and the use of computational platforms for accelerating segmentation execution, and (d) open research problems to pursue. The novel contributions of this survey paper are: (1) a new type of classification of cellular

Social discourses of healthy eating. A market segmentation approach.

PubMed

Chrysochou, Polymeros; Askegaard, Søren; Grunert, Klaus G; Kristensen, Dorthe Brogård

2010-10-01

This paper proposes a framework of discourses regarding consumers' healthy eating as a useful conceptual scheme for market segmentation purposes. The objectives are: (a) to identify the appropriate number of health-related segments based on the underlying discursive subject positions of the framework, (b) to validate and further describe the segments based on their socio-demographic characteristics and attitudes towards healthy eating, and (c) to explore differences across segments in types of associations with food and health, as well as perceptions of food healthfulness.316 Danish consumers participated in a survey that included measures of the underlying subject positions of the proposed framework, followed by a word association task that aimed to explore types of associations with food and health, and perceptions of food healthfulness. A latent class clustering approach revealed three consumer segments: the Common, the Idealists and the Pragmatists. Based on the addressed objectives, differences across the segments are described and implications of findings are discussed.

Patterns of Transfer of Adaptation Among Body Segments

NASA Technical Reports Server (NTRS)

Seidler, R. D.; Bloomberg, J. J.; Stelmach, George E.

2000-01-01

Two experiments were conducted in order to determine the patterns of transfer of visuomotor adaptation between arm and head pointing. An altered gain of display of pointing movements was used to induce a conflict between visual and somatosensory representations. Two subject groups participated in Experiment One: group 1 adapted shoulder pointing movements, and group 2 adapted wrist pointing movements to a 0.5 gain of display. Following the adaptation regimen, subjects performed a transfer test in which the shoulder group performed wrist movements and the wrist group performed shoulder movements. The results demonstrated that both groups displayed typical adaptation curves, initially undershooting the target followed by a return to baseline performance. Transfer tests revealed that both groups had high transfer of the acquired adaptation to the other joint. Experiment Two followed a similar design except that group 1 adapted head pointing movements and group 2 adapted arm pointing movements. The arm adaptation had high transfer to head pointing while the head adaptation had very little transfer to arm pointing. These results imply that, while the arm segments may share a common target representation for goal-directed actions, individual but functionally dependent target representations may exist for the control of head and arm movements.

Changes in Head Stability Control in Response to a Lateral Perturbation while Walking in Older Adults

NASA Technical Reports Server (NTRS)

Buccello, Regina R.; Cromwell, Ronita L.; Bloomberg, Jacob J.

2008-01-01

Falling is a main contributor of injury in older adults. The decline in sensory systems associated with aging limits information needed to successfully compensate for unexpected perturbations. Therefore, sensory changes result in older adults having problems maintaining balance stability when experiencing an unexpected lateral perturbation (e.g. slip) in the environment. The goal of this study was to determine head stability movement strategies used by older adults when experiencing an unexpected lateral perturbation during walking. A total of 16 healthy adults, aged 66-81 years, walked across a foam pathway 6 times. One piece of the foam pathway covered a movable platform that translated to the left when the subject stepped on the foam. Three trials were randomized in which the platform shifted. Angular rate sensors were placed on the center of mass for the head and trunk segments to collect head and trunk movement in all three planes of motion. The predominant movement strategies for maintaining head stability were determined from the results of the cross-correlation analyses between the head and trunk segments. The Chi square test of independence was used to evaluate the movement pattern distributions of head-trunk coordination during perturbed and non-perturbed walking. When perturbed, head stabilization was significantly challenged in the yaw and roll planes of motion. Subjects demonstrated a movement pattern of the head leading the trunk in an effort to stabilize the head. The older adult subjects used this head stabilization movement pattern to compensate for sensory changes when experiencing the unexpected lateral perturbation.

SU-E-J-238: Monitoring Lymph Node Volumes During Radiotherapy Using Semi-Automatic Segmentation of MRI Images

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

Veeraraghavan, H; Tyagi, N; Riaz, N

2014-06-01

Purpose: Identification and image-based monitoring of lymph nodes growing due to disease, could be an attractive alternative to prophylactic head and neck irradiation. We evaluated the accuracy of the user-interactive Grow Cut algorithm for volumetric segmentation of radiotherapy relevant lymph nodes from MRI taken weekly during radiotherapy. Method: The algorithm employs user drawn strokes in the image to volumetrically segment multiple structures of interest. We used a 3D T2-wturbo spin echo images with an isotropic resolution of 1 mm3 and FOV of 492×492×300 mm3 of head and neck cancer patients who underwent weekly MR imaging during the course of radiotherapy.more » Various lymph node (LN) levels (N2, N3, N4'5) were individually contoured on the weekly MR images by an expert physician and used as ground truth in our study. The segmentation results were compared with the physician drawn lymph nodes based on DICE similarity score. Results: Three head and neck patients with 6 weekly MR images were evaluated. Two patients had level 2 LN drawn and one patient had level N2, N3 and N4'5 drawn on each MR image. The algorithm took an average of a minute to segment the entire volume (512×512×300 mm3). The algorithm achieved an overall DICE similarity score of 0.78. The time taken for initializing and obtaining the volumetric mask was about 5 mins for cases with only N2 LN and about 15 mins for the case with N2,N3 and N4'5 level nodes. The longer initialization time for the latter case was due to the need for accurate user inputs to separate overlapping portions of the different LN. The standard deviation in segmentation accuracy at different time points was utmost 0.05. Conclusions: Our initial evaluation of the grow cut segmentation shows reasonably accurate and consistent volumetric segmentations of LN with minimal user effort and time.« less

Introduction to the enhanced logistics intratheater support tool (ELIST) mission application and its segments : global data segment version 8.1.0.0, database instance segment version 8.1.0.0, database fill segment version 8.1.0.0, database segment versio

DOT National Transportation Integrated Search

2002-02-26

This document, the Introduction to the Enhanced Logistics Intratheater Support Tool (ELIST) Mission Application and its Segments, satisfies the following objectives: : It identifies the mission application, known in brief as ELIST, and all seven ...

A Manual Segmentation Tool for Three-Dimensional Neuron Datasets.

PubMed

Magliaro, Chiara; Callara, Alejandro L; Vanello, Nicola; Ahluwalia, Arti

2017-01-01

To date, automated or semi-automated software and algorithms for segmentation of neurons from three-dimensional imaging datasets have had limited success. The gold standard for neural segmentation is considered to be the manual isolation performed by an expert. To facilitate the manual isolation of complex objects from image stacks, such as neurons in their native arrangement within the brain, a new Manual Segmentation Tool (ManSegTool) has been developed. ManSegTool allows user to load an image stack, scroll down the images and to manually draw the structures of interest stack-by-stack. Users can eliminate unwanted regions or split structures (i.e., branches from different neurons that are too close each other, but, to the experienced eye, clearly belong to a unique cell), to view the object in 3D and save the results obtained. The tool can be used for testing the performance of a single-neuron segmentation algorithm or to extract complex objects, where the available automated methods still fail. Here we describe the software's main features and then show an example of how ManSegTool can be used to segment neuron images acquired using a confocal microscope. In particular, expert neuroscientists were asked to segment different neurons from which morphometric variables were subsequently extracted as a benchmark for precision. In addition, a literature-defined index for evaluating the goodness of segmentation was used as a benchmark for accuracy. Neocortical layer axons from a DIADEM challenge dataset were also segmented with ManSegTool and compared with the manual "gold-standard" generated for the competition.

Automatic segmentation of bones from digital hand radiographs

NASA Astrophysics Data System (ADS)

Liu, Brent J.; Taira, Ricky K.; Shim, Hyeonjoon; Keaton, Patricia

1995-05-01

The purpose of this paper is to develop a robust and accurate method that automatically segments phalangeal and epiphyseal bones from digital pediatric hand radiographs exhibiting various stages of growth. The algorithm uses an object-oriented approach comprising several stages beginning with the most general objects to be segmented, such as the outline of the hand from background, and proceeding in a succession of stages to the most specific object, such as a specific phalangeal bone from a digit of the hand. Each stage carries custom operators unique to the needs of that specific stage which will aid in more accurate results. The method is further aided by a knowledge base where all model contours and other information such as age, race, and sex, are stored. Shape models, 1-D wrist profiles, as well as an interpretation tree are used to map model and data contour segments. Shape analysis is performed using an arc-length orientation transform. The method is tested on close to 340 phalangeal and epiphyseal objects to be segmented from 17 cases of pediatric hand images obtained from our clinical PACS. Patient age ranges from 2 - 16 years. A pediatric radiologist preliminarily assessed the results of the object contours and were found to be accurate to within 95% for cases with non-fused bones and to within 85% for cases with fused bones. With accurate and robust results, the method can be applied toward areas such as the determination of bone age, the development of a normal hand atlas, and the characterization of many congenital and acquired growth diseases. Furthermore, this method's architecture can be applied to other image segmentation problems.

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.

Semantic Segmentation of Building Elements Using Point Cloud Hashing

NASA Astrophysics Data System (ADS)

Chizhova, M.; Gurianov, A.; Hess, M.; Luhmann, T.; Brunn, A.; Stilla, U.

2018-05-01

For the interpretation of point clouds, the semantic definition of extracted segments from point clouds or images is a common problem. Usually, the semantic of geometrical pre-segmented point cloud elements are determined using probabilistic networks and scene databases. The proposed semantic segmentation method is based on the psychological human interpretation of geometric objects, especially on fundamental rules of primary comprehension. Starting from these rules the buildings could be quite well and simply classified by a human operator (e.g. architect) into different building types and structural elements (dome, nave, transept etc.), including particular building parts which are visually detected. The key part of the procedure is a novel method based on hashing where point cloud projections are transformed into binary pixel representations. A segmentation approach released on the example of classical Orthodox churches is suitable for other buildings and objects characterized through a particular typology in its construction (e.g. industrial objects in standardized enviroments with strict component design allowing clear semantic modelling).

Head Pose Estimation Using Multilinear Subspace Analysis for Robot Human Awareness

NASA Technical Reports Server (NTRS)

Ivanov, Tonislav; Matthies, Larry; Vasilescu, M. Alex O.

2009-01-01

Mobile robots, operating in unconstrained indoor and outdoor environments, would benefit in many ways from perception of the human awareness around them. Knowledge of people's head pose and gaze directions would enable the robot to deduce which people are aware of the its presence, and to predict future motions of the people for better path planning. To make such inferences, requires estimating head pose on facial images that are combination of multiple varying factors, such as identity, appearance, head pose, and illumination. By applying multilinear algebra, the algebra of higher-order tensors, we can separate these factors and estimate head pose regardless of subject's identity or image conditions. Furthermore, we can automatically handle uncertainty in the size of the face and its location. We demonstrate a pipeline of on-the-move detection of pedestrians with a robot stereo vision system, segmentation of the head, and head pose estimation in cluttered urban street scenes.

Internal Lymphedema Correlates with Subjective and Objective Measures of Dysphagia in Head and Neck Cancer Patients.

PubMed

Jackson, Leanne K; Ridner, Sheila H; Deng, Jie; Bartow, Carmin; Mannion, Kyle; Niermann, Ken; Gilbert, Jill; Dietrich, Mary S; Cmelak, Anthony J; Murphy, Barbara A

2016-09-01

Tumor/treatment-related internal lymphedema (IL) and/or external lymphedema (EL) are associated with functional deficits and increased symptom burden in head and neck cancer patients (HNCP). Previously, we noted association between EL/IL and patient-reported dysphagia using the Vanderbilt Head and Neck Symptom Survey (VHNSS) version 1.0. To determine the relationship between IL/EL and subjective and objective measures of swallowing function. Eighty-one HNCP completed: (1) VHNSS version 2.0, including 13 swallowing/nutrition-related questions grouped into three clusters: swallow solids (ss), swallow liquids (sl), and nutrition(nt); (2) physical assessment of EL using Foldi scale; (3) endoscopic assessment of IL using Patterson scale (n = 56); and (4) modified barium swallow study rated by dysphagia outcome and severity scale (DOSS) and in conjunction with a swallow evaluation by National Outcomes Measurement System (NOMS). Examinations were performed at varied time points to assess lymphedema spectrum, from baseline (n = 15, 18.1%) to 18 months post-therapy (n = 20, 24.1%). VHNSS swallow/nutrition items scores correlated with NOMS/DOSS ratings (p < 0.001). Highest correlation was with NOMS: ss (-0.73); sl (-0.61); nt (-0.56). VHNSS swallow/nutrition scores correlated with maximum grade of swelling for any single structure on Patterson scale: ss (0.43; p = 0.001); sl (0.38; p = 0.004); nt (0.41; p = 0.002). IL of aryepiglottic/pharyngoepiglottic folds, epiglottis, and pyriform sinus were most strongly correlated with VHNSS and NOMS ratings. NOMS/DOSS ratings correlated with EL (> = -0.34; p < 0.01). No meaningful correlations exist between VHNSS swallow/nutrition items and EL (< ± 0.15, p > 0.20). IL correlated with subjective and objective measures of swallow dysfunction. Longitudinal analysis of trajectory and impact of IL/EL on dysphagia is ongoing.

Stabilization and mobility of the head and trunk in wild monkeys during terrestrial and flat-surface walks and gallops.

PubMed

Dunbar, Donald C; Badam, Gyani L; Hallgrímsson, Benedikt; Vieilledent, Stéphane

2004-02-01

This study investigated the patterns of rotational mobility (> or =20 degrees ) and stability (< or =20 degrees ) of the head and trunk in wild Indian monkeys during natural locomotion on the ground and on the flat-topped surfaces of walls. Adult hanuman langurs (Semnopithecus entellus) and bonnet macaques (Macaca radiata) of either gender were cine filmed in lateral view. Whole-body horizontal linear displacement, head and trunk pitch displacement relative to space (earth horizontal), and vertical head displacement were measured from the cine films. Head-to-trunk pitch angle was calculated from the head-to-space and trunk-to-space measurements. Locomotor velocities, cycle durations, angular segmental velocities, mean segmental positions and mean peak frequencies of vertical and angular head displacements were then calculated from the displacement data. Yaw rotations were observed qualitatively. During quadrupedal walks by both species, the head was free to rotate in the pitch and yaw planes on a stabilized trunk. By contrast, during quadrupedal gallops by both species, the trunk pitched on a stabilized head. During both gaits in both species, head and trunk pitch rotations were symmetrical about comparable mean positions in both gaits, with mean head position aligning the horizontal semicircular canals near earth horizontal. Head pitch direction countered head vertical displacement direction to varying degrees during walks and only intermittently during gallops, providing evidence that correctional head pitch rotations are not essential for gaze stabilization. Head-to-space pitch velocities were below 350 deg. s(-1), the threshold above which, at least among humans, the vestibulo-ocular reflex (VOR) becomes saturated. Mean peak frequencies of vertical translations and pitch rotations of the head ranged from 1 Hz to 2 Hz, a lower frequency range than that in which inertia is predicted to be the major stabilizer of the head in these species. Some variables, which

Rats' Visual-Spatial Working Memory: New Object Choice Accuracy as a Function of Number of Objects in the Study Array

ERIC Educational Resources Information Center

Cohen, Jerome; Han, Xue; Matei, Anca; Parameswaran, Varakini; Zuniga, Robert; Hlynka, Myron

2010-01-01

When rats had to find new (jackpot) objects for rewards from among previously sampled baited objects, increasing the number of objects in the sample (study) segment of a trial from 3 to 5 and then to 7 (Experiment 1) or from 3 to 6 and 9 (Experiments 2 and 3) or from 6 to 9 and 12 (Experiment 4) did not reduce rats' test segment performance.…

Segmented slant hole collimator for stationary cardiac SPECT: Monte Carlo simulations

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

Mao, Yanfei, E-mail: ymao@ucair.med.utah.edu; Yu, Zhicong; Zeng, Gengsheng L.

2015-09-15

Purpose: This work is a preliminary study of a stationary cardiac SPECT system. The goal of this research is to propose a stationary cardiac SPECT system using segmented slant-hole collimators and to perform computer simulations to test the feasibility. Compared to the rotational SPECT, a stationary system has a benefit of acquiring temporally consistent projections. The most challenging issue in building a stationary system is to provide sufficient projection view-angles. Methods: A GATE (GEANT4 application for tomographic emission) Monte Carlo model was developed to simulate a two-detector stationary cardiac SPECT that uses segmented slant-hole collimators. Each detector contains seven segmentedmore » slant-hole sections that slant to a common volume at the rotation center. Consequently, 14 view-angles over 180° were acquired without any gantry rotation. The NCAT phantom was used for data generation and a tailored maximum-likelihood expectation-maximization algorithm was used for image reconstruction. Effects of limited number of view-angles and data truncation were carefully evaluated in the paper. Results: Simulation results indicated that the proposed segmented slant-hole stationary cardiac SPECT system is able to acquire sufficient data for cardiac imaging without a loss of image quality, even when the uptakes in the liver and kidneys are high. Seven views are acquired simultaneously at each detector, leading to 5-fold sensitivity gain over the conventional dual-head system at the same total acquisition time, which in turn increases the signal-to-noise ratio by 19%. The segmented slant-hole SPECT system also showed a good performance in lesion detection. In our prototype system, a short hole-length was used to reduce the dead zone between neighboring collimator segments. The measured sensitivity gain is about 17-fold over the conventional dual-head system. Conclusions: The GATE Monte Carlo simulations confirm the feasibility of the proposed stationary

Polarization sensitive corneal and anterior segment swept-source optical coherence tomography

NASA Astrophysics Data System (ADS)

Lim, Yiheng; Yamanari, Masahiro; Yasuno, Yoshiaki

2010-02-01

We develop a compact polarization sensitive corneal and anterior segment swept-source optical coherence tomography (PS-CAS- OCT) for evaluating the usefulness of PS-OCT, and enabling large scale studies in the tissue properties of normal and diseased eyes using the benefits of the PS-OCT, which provides better tissue discrimination compared to the conventional OCT by visualizing the fibrous tissues in the anterior eye segment. Our polarization-sensitive interferometer is size reduced into a 19 inch box for the portability and the probe is integrated into a position adjustable scanning head for the usability of our system.

Bilayer segmentation of webcam videos using tree-based classifiers.

PubMed

Yin, Pei; Criminisi, Antonio; Winn, John; Essa, Irfan

2011-01-01

This paper presents an automatic segmentation algorithm for video frames captured by a (monocular) webcam that closely approximates depth segmentation from a stereo camera. The frames are segmented into foreground and background layers that comprise a subject (participant) and other objects and individuals. The algorithm produces correct segmentations even in the presence of large background motion with a nearly stationary foreground. This research makes three key contributions: First, we introduce a novel motion representation, referred to as "motons," inspired by research in object recognition. Second, we propose estimating the segmentation likelihood from the spatial context of motion. The estimation is efficiently learned by random forests. Third, we introduce a general taxonomy of tree-based classifiers that facilitates both theoretical and experimental comparisons of several known classification algorithms and generates new ones. In our bilayer segmentation algorithm, diverse visual cues such as motion, motion context, color, contrast, and spatial priors are fused by means of a conditional random field (CRF) model. Segmentation is then achieved by binary min-cut. Experiments on many sequences of our videochat application demonstrate that our algorithm, which requires no initialization, is effective in a variety of scenes, and the segmentation results are comparable to those obtained by stereo systems.

Electric field theory based approach to search-direction line definition in image segmentation: application to optimal femur-tibia cartilage segmentation in knee-joint 3-D MR

NASA Astrophysics Data System (ADS)

Yin, Y.; Sonka, M.

2010-03-01

A novel method is presented for definition of search lines in a variety of surface segmentation approaches. The method is inspired by properties of electric field direction lines and is applicable to general-purpose n-D shapebased image segmentation tasks. Its utility is demonstrated in graph construction and optimal segmentation of multiple mutually interacting objects. The properties of the electric field-based graph construction guarantee that inter-object graph connecting lines are non-intersecting and inherently covering the entire object-interaction space. When applied to inter-object cross-surface mapping, our approach generates one-to-one and all-to-all vertex correspondent pairs between the regions of mutual interaction. We demonstrate the benefits of the electric field approach in several examples ranging from relatively simple single-surface segmentation to complex multiobject multi-surface segmentation of femur-tibia cartilage. The performance of our approach is demonstrated in 60 MR images from the Osteoarthritis Initiative (OAI), in which our approach achieved a very good performance as judged by surface positioning errors (average of 0.29 and 0.59 mm for signed and unsigned cartilage positioning errors, respectively).

Interactive tele-radiological segmentation systems for treatment and diagnosis.

PubMed

Zimeras, S; Gortzis, L G

2012-01-01

Telehealth is the exchange of health information and the provision of health care services through electronic information and communications technology, where participants are separated by geographic, time, social and cultural barriers. The shift of telemedicine from desktop platforms to wireless and mobile technologies is likely to have a significant impact on healthcare in the future. It is therefore crucial to develop a general information exchange e-medical system to enables its users to perform online and offline medical consultations through diagnosis. During the medical diagnosis, image analysis techniques combined with doctor's opinions could be useful for final medical decisions. Quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest. In medical images, segmentation has traditionally been done by human experts. Even with the aid of image processing software (computer-assisted segmentation tools), manual segmentation of 2D and 3D CT images is tedious, time-consuming, and thus impractical, especially in cases where a large number of objects must be specified. Substantial computational and storage requirements become especially acute when object orientation and scale have to be considered. Therefore automated or semi-automated segmentation techniques are essential if these software applications are ever to gain widespread clinical use. The main purpose of this work is to analyze segmentation techniques for the definition of anatomical structures under telemedical systems.

Video Object Segmentation through Spatially Accurate and Temporally Dense Extraction of Primary Object Regions (Open Access)

DTIC Science & Technology

2013-10-03

fol- low the setup in the literature ([13, 14]), and use 5 (birdfall, cheetah , girl, monkeydog and parachute) of the videos for evaluation (since the...segmentation labeling results of the method, GT is the ground-truth labeling of the video, and F is the (a) Birdfall (b) Cheetah (c) Girl (d) Monkeydog...Video Ours [14] [13] [20] [6] birdfall 155 189 288 252 454 cheetah 633 806 905 1142 1217 girl 1488 1698 1785 1304 1755 monkeydog 365 472 521 563 683

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

Poster - 32: Atlas Selection for Automated Segmentation of Pelvic CT for Prostate Radiotherapy

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

Mallawi, Abrar; Farrell, TomTom; Diamond, Kevin-Ro

2016-08-15

Atlas based-segmentation has recently been evaluated for use in prostate radiotherapy. In a typical approach, the essential step is the selection of an atlas from a database that the best matches of the target image. This work proposes an atlas selection strategy and evaluate it impacts on final segmentation accuracy. Several anatomical parameters were measured to indicate the overall prostate and body shape, all of these measurements obtained on CT images. A brute force procedure was first performed for a training dataset of 20 patients using image registration to pair subject with similar contours; each subject was served as amore » target image to which all reaming 19 images were affinity registered. The overlap between the prostate and femoral heads was quantified for each pair using the Dice Similarity Coefficient (DSC). Finally, an atlas selection procedure was designed; relying on the computation of a similarity score defined as a weighted sum of differences between the target and atlas subject anatomical measurement. The algorithm ability to predict the most similar atlas was excellent, achieving mean DSCs of 0.78 ± 0.07 and 0.90 ± 0.02 for the CTV and either femoral head. The proposed atlas selection yielded 0.72 ± 0.11 and 0.87 ± 0.03 for CTV and either femoral head. The DSC obtained with the proposed selection method were slightly lower than the maximum established using brute force, but this does not include potential improvements expected with deformable registration. The proposed atlas selection method provides reasonable segmentation accuracy.« less

Performance evaluation of object based greenhouse detection from Sentinel-2 MSI and Landsat 8 OLI data: A case study from Almería (Spain)

NASA Astrophysics Data System (ADS)

Novelli, Antonio; Aguilar, Manuel A.; Nemmaoui, Abderrahim; Aguilar, Fernando J.; Tarantino, Eufemia

2016-10-01

This paper shows the first comparison between data from Sentinel-2 (S2) Multi Spectral Instrument (MSI) and Landsat 8 (L8) Operational Land Imager (OLI) headed up to greenhouse detection. Two closely related in time scenes, one for each sensor, were classified by using Object Based Image Analysis and Random Forest (RF). The RF input consisted of several object-based features computed from spectral bands and including mean values, spectral indices and textural features. S2 and L8 data comparisons were also extended using a common segmentation dataset extracted form VHR World-View 2 (WV2) imagery to test differences only due to their specific spectral contribution. The best band combinations to perform segmentation were found through a modified version of the Euclidian Distance 2 index. Four different RF classifications schemes were considered achieving 89.1%, 91.3%, 90.9% and 93.4% as the best overall accuracies respectively, evaluated over the whole study area.

A Bayesian Approach for Image Segmentation with Shape Priors

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

Chang, Hang; Yang, Qing; Parvin, Bahram

2008-06-20

Color and texture have been widely used in image segmentation; however, their performance is often hindered by scene ambiguities, overlapping objects, or missingparts. In this paper, we propose an interactive image segmentation approach with shape prior models within a Bayesian framework. Interactive features, through mouse strokes, reduce ambiguities, and the incorporation of shape priors enhances quality of the segmentation where color and/or texture are not solely adequate. The novelties of our approach are in (i) formulating the segmentation problem in a well-de?ned Bayesian framework with multiple shape priors, (ii) ef?ciently estimating parameters of the Bayesian model, and (iii) multi-object segmentationmore » through user-speci?ed priors. We demonstrate the effectiveness of our method on a set of natural and synthetic images.« less

Comparison of optical see-through head-mounted displays for surgical interventions with object-anchored 2D-display

PubMed Central

Barthel, Alexander; Johnson, Alex; Osgood, Greg; Kazanzides, Peter; Navab, Nassir; Fuerst, Bernhard

2018-01-01

Purpose Optical see-through head-mounted displays (OST-HMD) feature an unhindered and instantaneous view of the surgery site and can enable a mixed reality experience for surgeons during procedures. In this paper, we present a systematic approach to identify the criteria for evaluation of OST-HMD technologies for specific clinical scenarios, which benefit from using an object-anchored 2D-display visualizing medical information. Methods Criteria for evaluating the performance of OST-HMDs for visualization of medical information and its usage are identified and proposed. These include text readability, contrast perception, task load, frame rate, and system lag. We choose to compare three commercially available OST-HMDs, which are representatives of currently available head-mounted display technologies. A multi-user study and an offline experiment are conducted to evaluate their performance. Results Statistical analysis demonstrates that Microsoft HoloLens performs best among the three tested OST-HMDs, in terms of contrast perception, task load, and frame rate, while ODG R-7 offers similar text readability. The integration of indoor localization and fiducial tracking on the HoloLens provides significantly less system lag in a relatively motionless scenario. Conclusions With ever more OST-HMDs appearing on the market, the proposed criteria could be used in the evaluation of their suitability for mixed reality surgical intervention. Currently, Microsoft HoloLens may be more suitable than ODG R-7 and Epson Moverio BT-200 for clinical usability in terms of the evaluated criteria. To the best of our knowledge, this is the first paper that presents a methodology and conducts experiments to evaluate and compare OST-HMDs for their use as object-anchored 2D-display during interventions. PMID:28343301

Comparison of optical see-through head-mounted displays for surgical interventions with object-anchored 2D-display.

PubMed

Qian, Long; Barthel, Alexander; Johnson, Alex; Osgood, Greg; Kazanzides, Peter; Navab, Nassir; Fuerst, Bernhard

2017-06-01

Optical see-through head-mounted displays (OST-HMD) feature an unhindered and instantaneous view of the surgery site and can enable a mixed reality experience for surgeons during procedures. In this paper, we present a systematic approach to identify the criteria for evaluation of OST-HMD technologies for specific clinical scenarios, which benefit from using an object-anchored 2D-display visualizing medical information. Criteria for evaluating the performance of OST-HMDs for visualization of medical information and its usage are identified and proposed. These include text readability, contrast perception, task load, frame rate, and system lag. We choose to compare three commercially available OST-HMDs, which are representatives of currently available head-mounted display technologies. A multi-user study and an offline experiment are conducted to evaluate their performance. Statistical analysis demonstrates that Microsoft HoloLens performs best among the three tested OST-HMDs, in terms of contrast perception, task load, and frame rate, while ODG R-7 offers similar text readability. The integration of indoor localization and fiducial tracking on the HoloLens provides significantly less system lag in a relatively motionless scenario. With ever more OST-HMDs appearing on the market, the proposed criteria could be used in the evaluation of their suitability for mixed reality surgical intervention. Currently, Microsoft HoloLens may be more suitable than ODG R-7 and Epson Moverio BT-200 for clinical usability in terms of the evaluated criteria. To the best of our knowledge, this is the first paper that presents a methodology and conducts experiments to evaluate and compare OST-HMDs for their use as object-anchored 2D-display during interventions.

Head Start of North Dakota, 1999-2000.

ERIC Educational Resources Information Center

North Dakota Dept. of Human Services, Bismark. Div. of Children and Family Services.

The Head Start program, a comprehensive child development program designed to increase the social competence of children in low-income families and children with disabilities and to improve their chances of school success, has been in North Dakota since 1965. This report describes the objectives of the Head Start and Early Head Start programs, the…

The segmentation of the HMD market: optics for smart glasses, smart eyewear, AR and VR headsets

NASA Astrophysics Data System (ADS)

Kress, Bernard; Saeedi, Ehsan; Brac-de-la-Perriere, Vincent

2014-09-01

This paper reviews the various optical technologies that have been developed to implement HMDs (Head Mounted Displays), both as AR (Augmented Reality) devices, VR (Virtual Reality) devices and more recently as smart glasses, smart eyewear or connected glasses. We review the typical requirements and optical performances of such devices and categorize them into distinct groups, which are suited for different (and constantly evolving) market segments, and analyze such market segmentation.

Using EMG to anticipate head motion for virtual-environment applications

NASA Technical Reports Server (NTRS)

Barniv, Yair; Aguilar, Mario; Hasanbelliu, Erion

2005-01-01

In virtual environment (VE) applications, where virtual objects are presented in a see-through head-mounted display, virtual images must be continuously stabilized in space in response to user's head motion. Time delays in head-motion compensation cause virtual objects to "swim" around instead of being stable in space which results in misalignment errors when overlaying virtual and real objects. Visual update delays are a critical technical obstacle for implementing head-mounted displays in applications such as battlefield simulation/training, telerobotics, and telemedicine. Head motion is currently measurable by a head-mounted 6-degrees-of-freedom inertial measurement unit. However, even given this information, overall VE-system latencies cannot be reduced under about 25 ms. We present a novel approach to eliminating latencies, which is premised on the fact that myoelectric signals from a muscle precede its exertion of force, thereby limb or head acceleration. We thus suggest utilizing neck-muscles' myoelectric signals to anticipate head motion. We trained a neural network to map such signals onto equivalent time-advanced inertial outputs. The resulting network can achieve time advances of up to 70 ms.

Using EMG to anticipate head motion for virtual-environment applications.

PubMed

Barniv, Yair; Aguilar, Mario; Hasanbelliu, Erion

2005-06-01

In virtual environment (VE) applications, where virtual objects are presented in a see-through head-mounted display, virtual images must be continuously stabilized in space in response to user's head motion. Time delays in head-motion compensation cause virtual objects to "swim" around instead of being stable in space which results in misalignment errors when overlaying virtual and real objects. Visual update delays are a critical technical obstacle for implementing head-mounted displays in applications such as battlefield simulation/training, telerobotics, and telemedicine. Head motion is currently measurable by a head-mounted 6-degrees-of-freedom inertial measurement unit. However, even given this information, overall VE-system latencies cannot be reduced under about 25 ms. We present a novel approach to eliminating latencies, which is premised on the fact that myoelectric signals from a muscle precede its exertion of force, thereby limb or head acceleration. We thus suggest utilizing neck-muscles' myoelectric signals to anticipate head motion. We trained a neural network to map such signals onto equivalent time-advanced inertial outputs. The resulting network can achieve time advances of up to 70 ms.

Optimal compliant-surface jumping: a multi-segment model of springboard standing jumps.

PubMed

Cheng, Kuangyou B; Hubbard, Mont

2005-09-01

A multi-segment model is used to investigate optimal compliant-surface jumping strategies and is applied to springboard standing jumps. The human model has four segments representing the feet, shanks, thighs, and trunk-head-arms. A rigid bar with a rotational spring on one end and a point mass on the other end (the tip) models the springboard. Board tip mass, length, and stiffness are functions of the fulcrum setting. Body segments and board tip are connected by frictionless hinge joints and are driven by joint torque actuators at the ankle, knee, and hip. One constant (maximum isometric torque) and three variable functions (of instantaneous joint angle, angular velocity, and activation level) determine each joint torque. Movement from a nearly straight motionless initial posture to jump takeoff is simulated. The objective is to find joint torque activation patterns during board contact so that jump height can be maximized. Minimum and maximum joint angles, rates of change of normalized activation levels, and contact duration are constrained. Optimal springboard jumping simulations can reasonably predict jumper vertical velocity and jump height. Qualitatively similar joint torque activation patterns are found over different fulcrum settings. Different from rigid-surface jumping where maximal activation is maintained until takeoff, joint activation decreases near takeoff in compliant-surface jumping. The fulcrum-height relations in experimental data were predicted by the models. However, lack of practice at non-preferred fulcrum settings might have caused less jump height than the models' prediction. Larger fulcrum numbers are beneficial for taller/heavier jumpers because they need more time to extend joints.

3D TEM reconstruction and segmentation process of laminar bio-nanocomposites

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

Iturrondobeitia, M., E-mail: maider.iturrondobeitia@ehu.es; Okariz, A.; Fernandez-Martinez, R.

2015-03-30

The microstructure of laminar bio-nanocomposites (Poly (lactic acid)(PLA)/clay) depends on the amount of clay platelet opening after integration with the polymer matrix and determines the final properties of the material. Transmission electron microscopy (TEM) technique is the only one that can provide a direct observation of the layer dispersion and the degree of exfoliation. However, the orientation of the clay platelets, which affects the final properties, is practically immeasurable from a single 2D TEM image. This issue can be overcome using transmission electron tomography (ET), a technique that allows the complete 3D characterization of the structure, including the measurement ofmore » the orientation of clay platelets, their morphology and their 3D distribution. ET involves a 3D reconstruction of the study volume and a subsequent segmentation of the study object. Currently, accurate segmentation is performed manually, which is inefficient and tedious. The aim of this work is to propose an objective/automated segmentation methodology process of a 3D TEM tomography reconstruction. In this method the segmentation threshold is optimized by minimizing the variation of the dimensions of the segmented objects and matching the segmented V{sub clay} (%) and the actual one. The method is first validated using a fictitious set of objects, and then applied on a nanocomposite.« less

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.

Automatic segmentation and classification of mycobacterium tuberculosis with conventional light microscopy

NASA Astrophysics Data System (ADS)

Xu, Chao; Zhou, Dongxiang; Zhai, Yongping; Liu, Yunhui

2015-12-01

This paper realizes the automatic segmentation and classification of Mycobacterium tuberculosis with conventional light microscopy. First, the candidate bacillus objects are segmented by the marker-based watershed transform. The markers are obtained by an adaptive threshold segmentation based on the adaptive scale Gaussian filter. The scale of the Gaussian filter is determined according to the color model of the bacillus objects. Then the candidate objects are extracted integrally after region merging and contaminations elimination. Second, the shape features of the bacillus objects are characterized by the Hu moments, compactness, eccentricity, and roughness, which are used to classify the single, touching and non-bacillus objects. We evaluated the logistic regression, random forest, and intersection kernel support vector machines classifiers in classifying the bacillus objects respectively. Experimental results demonstrate that the proposed method yields to high robustness and accuracy. The logistic regression classifier performs best with an accuracy of 91.68%.

Heading in football. Part 3: Effect of ball properties on head response

PubMed Central

Shewchenko, N; Withnall, C; Keown, M; Gittens, R; Dvorak, J

2005-01-01

Objectives: Head impacts from footballs are an essential part of the game but have been implicated in mild and acute neuropsychological impairment. Ball characteristics have been noted in literature to affect the impact response of the head; however, the biomechanics are not well understood. The present study determined whether ball mass, pressure, and construction characteristics help reduce head and neck can impact response. Methods: Head responses under ball impact (6–7 m/s) were measured with a biofidelic numerical human model and controlled human subject trials (n = 3). Three ball masses and four ball pressures were investigated for frontal heading. Further, the effect of ball construction in wet/dry conditions was studied with the numerical model. The dynamic ball characteristics were determined experimentally. Head linear and angular accelerations were measured and compared with injury assessment functions comprising peak values and head impact power. Neck responses were assessed with the numerical model. Results: Ball mass reductions up to 35% resulted in decreased head responses up to 23–35% for the numerical and subject trials. Similar decreases in neck axial and shear responses were observed. Ball pressure reductions of 50% resulted in head and neck response reductions up to 10–31% for the subject trials and numerical model. Head response reductions up to 15% were observed between different ball constructions. The wet condition generally resulted in greater head and neck responses of up to 20%. Conclusion: Ball mass, pressure, and construction can reduce the impact severity to the head and neck. It is foreseeable that the benefits can be extended to players of all ages and skill levels. PMID:16046354

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.

A Scalable Framework For Segmenting Magnetic Resonance Images

PubMed Central

Hore, Prodip; Goldgof, Dmitry B.; Gu, Yuhua; Maudsley, Andrew A.; Darkazanli, Ammar

2009-01-01

A fast, accurate and fully automatic method of segmenting magnetic resonance images of the human brain is introduced. The approach scales well allowing fast segmentations of fine resolution images. The approach is based on modifications of the soft clustering algorithm, fuzzy c-means, that enable it to scale to large data sets. Two types of modifications to create incremental versions of fuzzy c-means are discussed. They are much faster when compared to fuzzy c-means for medium to extremely large data sets because they work on successive subsets of the data. They are comparable in quality to application of fuzzy c-means to all of the data. The clustering algorithms coupled with inhomogeneity correction and smoothing are used to create a framework for automatically segmenting magnetic resonance images of the human brain. The framework is applied to a set of normal human brain volumes acquired from different magnetic resonance scanners using different head coils, acquisition parameters and field strengths. Results are compared to those from two widely used magnetic resonance image segmentation programs, Statistical Parametric Mapping and the FMRIB Software Library (FSL). The results are comparable to FSL while providing significant speed-up and better scalability to larger volumes of data. PMID:20046893

Technical Note: PLASTIMATCH MABS, an open source tool for automatic image segmentation

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

Zaffino, Paolo; Spadea, Maria Francesca

Purpose: Multiatlas based segmentation is largely used in many clinical and research applications. Due to its good performances, it has recently been included in some commercial platforms for radiotherapy planning and surgery guidance. Anyway, to date, a software with no restrictions about the anatomical district and image modality is still missing. In this paper we introduce PLASTIMATCH MABS, an open source software that can be used with any image modality for automatic segmentation. Methods: PLASTIMATCH MABS workflow consists of two main parts: (1) an offline phase, where optimal registration and voting parameters are tuned and (2) an online phase, wheremore » a new patient is labeled from scratch by using the same parameters as identified in the former phase. Several registration strategies, as well as different voting criteria can be selected. A flexible atlas selection scheme is also available. To prove the effectiveness of the proposed software across anatomical districts and image modalities, it was tested on two very different scenarios: head and neck (H&N) CT segmentation for radiotherapy application, and magnetic resonance image brain labeling for neuroscience investigation. Results: For the neurological study, minimum dice was equal to 0.76 (investigated structures: left and right caudate, putamen, thalamus, and hippocampus). For head and neck case, minimum dice was 0.42 for the most challenging structures (optic nerves and submandibular glands) and 0.62 for the other ones (mandible, brainstem, and parotid glands). Time required to obtain the labels was compatible with a real clinical workflow (35 and 120 min). Conclusions: The proposed software fills a gap in the multiatlas based segmentation field, since all currently available tools (both for commercial and for research purposes) are restricted to a well specified application. Furthermore, it can be adopted as a platform for exploring MABS parameters and as a reference implementation for comparing

Image segmentation using joint spatial-intensity-shape features: application to CT lung nodule segmentation

NASA Astrophysics Data System (ADS)

Ye, Xujiong; Siddique, Musib; Douiri, Abdel; Beddoe, Gareth; Slabaugh, Greg

2009-02-01

Automatic segmentation of medical images is a challenging problem due to the complexity and variability of human anatomy, poor contrast of the object being segmented, and noise resulting from the image acquisition process. This paper presents a novel feature-guided method for the segmentation of 3D medical lesions. The proposed algorithm combines 1) a volumetric shape feature (shape index) based on high-order partial derivatives; 2) mean shift clustering in a joint spatial-intensity-shape (JSIS) feature space; and 3) a modified expectation-maximization (MEM) algorithm on the mean shift mode map to merge the neighboring regions (modes). In such a scenario, the volumetric shape feature is integrated into the process of the segmentation algorithm. The joint spatial-intensity-shape features provide rich information for the segmentation of the anatomic structures or lesions (tumors). The proposed method has been evaluated on a clinical dataset of thoracic CT scans that contains 68 nodules. A volume overlap ratio between each segmented nodule and the ground truth annotation is calculated. Using the proposed method, the mean overlap ratio over all the nodules is 0.80. On visual inspection and using a quantitative evaluation, the experimental results demonstrate the potential of the proposed method. It can properly segment a variety of nodules including juxta-vascular and juxta-pleural nodules, which are challenging for conventional methods due to the high similarity of intensities between the nodules and their adjacent tissues. This approach could also be applied to lesion segmentation in other anatomies, such as polyps in the colon.

Identifying head-trunk and lower limb contributions to gaze stabilization during locomotion

NASA Technical Reports Server (NTRS)

Mulavara, Ajitkumar P.; Bloomberg, Jacob J.

2002-01-01

The goal of the present study was to determine how the multiple, interdependent full-body sensorimotor subsystems respond to a change in gaze stabilization task constraints during locomotion. Nine subjects performed two gaze stabilization tasks while walking at 6.4 km/hr on a motorized treadmill: 1) focusing on a central point target; 2) reading numeral characters; both presented at 2 m in front at the level of their eyes. While subjects performed the tasks we measured: temporal parameters of gait, full body sagittal plane segmental kinematics of the head, trunk, thigh, tibia and foot, accelerations along the vertical axis at the head and the tibia, and the vertical forces acting on the support surface. We tested the hypothesis that with the increased demands placed on visual acuity during the number recognition task, subjects would modify full-body segmental kinematics in order to reduce perturbations to the head in order to successfully perform the task. We found that while reading numeral characters as compared to the central point target: 1) compensatory head pitch movement was on average 22% greater despite the fact that the trunk pitch and trunk vertical translation movement control were not significantly changed; 2) coordination patterns between head and trunk as reflected by the peak cross correlation between the head pitch and trunk pitch motion as well as the peak cross correlation between the head pitch and vertical trunk translation motion were not significantly changed; 3) knee joint total movement was on average 11% greater during the period from the heel strike event to the peak knee flexion event in stance phase of the gait cycle; 4) peak acceleration measured at the head was significantly reduced by an average of 13% in four of the six subjects. This was so even when the peak acceleration at the tibia and the transmission of the shock wave at heel strike (measured by the peak acceleration ratio of the head/tibia and the time lag between the tibial

Tx/Rx Head Coil Induces Less RF Transmit-Related Heating than Body Coil in Conductive Metallic Objects Outside the Active Area of the Head Coil

PubMed Central

Nagy, Zoltan; Oliver-Taylor, Aaron; Kuehne, Andre; Goluch, Sigrun; Weiskopf, Nikolaus

2017-01-01

The transmit–receive (Tx/Rx) birdcage head coil is often used for excitation instead of the body coil because of the presumably lower risk of heating in and around conductive implants. However, this common practice has not been systematically tested. To investigate whether the Tx/Rx birdcage head coil produces less heating than the body coil when scanning individuals with implants, we used a 3T clinical scanner and made temperature measurements around a straight 15 cm conductor using either the Tx/Rx body or the head coil for excitation. Additionally, the transmitted fields of a Tx/Rx head coil were measured both in air and in gel using a resonant and a non-resonant B field probes as well as a non-resonant E field probe. Simulations using a finite-difference time domain solver were compared with the experimental findings. When the body coil was used for excitation, we observed heating around the 15 cm wire at various anatomical locations (both within and outside of the active volume of the head coil). Outside its active area, no such heating was observed while using the Tx/Rx head coil for excitation. The E and B fields of the Tx/Rx birdcage head coil extended well-beyond the physical dimensions of the coil. In air, the fields were monotonically decreasing, while in gel they were more complex with local maxima at the end of the ASTM phantom. These experimental findings were line with the simulations. While caution must always be exercised when scanning individuals with metallic implants, these findings support the use of the Tx/Rx birdcage head coil in place of the body coil at 3T in order to reduce the risk of heating in and around conductive implants that are remote from the head coil. PMID:28184184

Segmentation precision of abdominal anatomy for MRI-based radiotherapy

PubMed Central

Noel, Camille E.; Zhu, Fan; Lee, Andrew Y.; Yanle, Hu; Parikh, Parag J.

2014-01-01

The limited soft tissue visualization provided by computed tomography, the standard imaging modality for radiotherapy treatment planning and daily localization, has motivated studies on the use of magnetic resonance imaging (MRI) for better characterization of treatment sites, such as the prostate and head and neck. However, no studies have been conducted on MRI-based segmentation for the abdomen, a site that could greatly benefit from enhanced soft tissue targeting. We investigated the interobserver and intraobserver precision in segmentation of abdominal organs on MR images for treatment planning and localization. Manual segmentation of 8 abdominal organs was performed by 3 independent observers on MR images acquired from 14 healthy subjects. Observers repeated segmentation 4 separate times for each image set. Interobserver and intraobserver contouring precision was assessed by computing 3-dimensional overlap (Dice coefficient [DC]) and distance to agreement (Hausdorff distance [HD]) of segmented organs. The mean and standard deviation of intraobserver and interobserver DC and HD values were DCintraobserver = 0.89 ± 0.12, HDintraobserver = 3.6 mm ± 1.5, DCinterobserver = 0.89 ± 0.15, and HDinterobserver = 3.2 mm ± 1.4. Overall, metrics indicated good interobserver/intraobserver precision (mean DC > 0.7, mean HD < 4 mm). Results suggest that MRI offers good segmentation precision for abdominal sites. These findings support the utility of MRI for abdominal planning and localization, as emerging MRI technologies, techniques, and onboard imaging devices are beginning to enable MRI-based radiotherapy. PMID:24726701

Integration of paleoseismic data from multiple sites to develop an objective earthquake chronology: Application to the Weber segment of the Wasatch fault zone, Utah

USGS Publications Warehouse

DuRoss, Christopher B.; Personius, Stephen F.; Crone, Anthony J.; Olig, Susan S.; Lund, William R.

2011-01-01

We present a method to evaluate and integrate paleoseismic data from multiple sites into a single, objective measure of earthquake timing and recurrence on discrete segments of active faults. We apply this method to the Weber segment (WS) of the Wasatch fault zone using data from four fault-trench studies completed between 1981 and 2009. After systematically reevaluating the stratigraphic and chronologic data from each trench site, we constructed time-stratigraphic OxCal models that yield site probability density functions (PDFs) of the times of individual earthquakes. We next qualitatively correlated the site PDFs into a segment-wide earthquake chronology, which is supported by overlapping site PDFs, large per-event displacements, and prominent segment boundaries. For each segment-wide earthquake, we computed the product of the site PDF probabilities in common time bins, which emphasizes the overlap in the site earthquake times, and gives more weight to the narrowest, best-defined PDFs. The product method yields smaller earthquake-timing uncertainties compared to taking the mean of the site PDFs, but is best suited to earthquakes constrained by broad, overlapping site PDFs. We calculated segment-wide earthquake recurrence intervals and uncertainties using a Monte Carlo model. Five surface-faulting earthquakes occurred on the WS at about 5.9, 4.5, 3.1, 1.1, and 0.6 ka. With the exception of the 1.1-ka event, we used the product method to define the earthquake times. The revised WS chronology yields a mean recurrence interval of 1.3 kyr (0.7–1.9-kyr estimated two-sigma [2δ] range based on interevent recurrence). These data help clarify the paleoearthquake history of the WS, including the important question of the timing and rupture extent of the most recent earthquake, and are essential to the improvement of earthquake-probability assessments for the Wasatch Front region.

Integration of paleoseismic data from multiple sites to develop an objective earthquake chronology: Application to the Weber segment of the Wasatch fault zone, Utah

USGS Publications Warehouse

DuRoss, C.B.; Personius, S.F.; Crone, A.J.; Olig, S.S.; Lund, W.R.

2011-01-01

We present a method to evaluate and integrate paleoseismic data from multiple sites into a single, objective measure of earthquake timing and recurrence on discrete segments of active faults. We apply this method to the Weber segment (WS) of the Wasatch fault zone using data from four fault-trench studies completed between 1981 and 2009. After systematically reevaluating the stratigraphic and chronologic data from each trench site, we constructed time-stratigraphic OxCal models that yield site probability density functions (PDFs) of the times of individual earthquakes. We next qualitatively correlated the site PDFs into a segment-wide earthquake chronology, which is supported by overlapping site PDFs, large per-event displacements, and prominent segment boundaries. For each segment-wide earthquake, we computed the product of the site PDF probabilities in common time bins, which emphasizes the overlap in the site earthquake times, and gives more weight to the narrowest, best-defined PDFs. The product method yields smaller earthquake-timing uncertainties compared to taking the mean of the site PDFs, but is best suited to earthquakes constrained by broad, overlapping site PDFs. We calculated segment-wide earthquake recurrence intervals and uncertainties using a Monte Carlo model. Five surface-faulting earthquakes occurred on the WS at about 5.9, 4.5, 3.1, 1.1, and 0.6 ka. With the exception of the 1.1-ka event, we used the product method to define the earthquake times. The revised WS chronology yields a mean recurrence interval of 1.3 kyr (0.7-1.9-kyr estimated two-sigma [2??] range based on interevent recurrence). These data help clarify the paleoearthquake history of the WS, including the important question of the timing and rupture extent of the most recent earthquake, and are essential to the improvement of earthquake-probability assessments for the Wasatch Front region.

Molecular Age-Related Changes in the Anterior Segment of the Eye

PubMed Central

Gulias-Cañizo, Rosario; Martínez-Báez, Blanca Elizabeth; Quiroz-Mercado, Hugo

2017-01-01

Purpose To examine the current knowledge about the age-related processes in the anterior segment of the eye at a biological, clinical, and molecular level. Methods We reviewed the available published literature that addresses the aging process of the anterior segment of the eye and its associated molecular and physiological events. We performed a search on PubMed, CINAHL, and Embase using the MeSH terms “eye,” “anterior segment,” and “age.” We generated searches to account for synonyms of these keywords and MESH headings as follows: (1) “Eye” AND “ageing process” OR “anterior segment ageing” and (2) “Anterior segment” AND “ageing process” OR “anterior segment” AND “molecular changes” AND “age.” Results. Among the principal causes of age-dependent alterations in the anterior segment of the eye, we found the mutation of the TGF-β gene and loss of autophagy in addition to oxidative stress, which contributes to the pathogenesis of degenerative diseases. Conclusions In this review, we summarize the current knowledge regarding some of the molecular mechanisms related to aging in the anterior segment of the eye. We also introduce and propose potential roles of autophagy, an important mechanism responsible for maintaining homeostasis and proteostasis under stress conditions in the anterior segment during aging. PMID:29147580

The control of upper body segment speed and velocity during the golf swing.

PubMed

Horan, Sean A; Kavanagh, Justin J

2012-06-01

Understanding the dynamics of upper body motion during the downswing is an important step in determining the control strategies required for a successful and repeatable golf swing. The purpose of this study was to examine the relationship between head, thorax, and pelvis motion, during the downswing of professional golfers. Three-dimensional data were collected for 14 male professional golfers (age 27 +/- 8 years, golf-playing experience 13.3 +/- 8 years) using an optical motion analysis system. The amplitude and timing of peak speed and peak velocities were calculated for the head, thorax, and pelvis during the downswing. Cross-correlation analysis was used to examine the strength of coupling and phasing between and within segments. The results indicated the thorax segment had the highest peak speeds and peak velocities for the upper body during the downswing. A strong coupling relationship was evident between the thorax and pelvis (average R2 = 0.92 across all directions), while the head and thorax showed a much more variable relationship (average R2 = 0.76 across all directions). The strong coupling between the thorax and pelvis is possibly a method for simplifying the motor control strategy used during the downswing, and a way of ensuring consistent motor patterns.

The effect of input data transformations on object-based image analysis

PubMed Central

LIPPITT, CHRISTOPHER D.; COULTER, LLOYD L.; FREEMAN, MARY; LAMANTIA-BISHOP, JEFFREY; PANG, WYSON; STOW, DOUGLAS A.

2011-01-01

The effect of using spectral transform images as input data on segmentation quality and its potential effect on products generated by object-based image analysis are explored in the context of land cover classification in Accra, Ghana. Five image data transformations are compared to untransformed spectral bands in terms of their effect on segmentation quality and final product accuracy. The relationship between segmentation quality and product accuracy is also briefly explored. Results suggest that input data transformations can aid in the delineation of landscape objects by image segmentation, but the effect is idiosyncratic to the transformation and object of interest. PMID:21673829

Hierarchical combinatorial deep learning architecture for pancreas segmentation of medical computed tomography cancer images.

PubMed

Fu, Min; Wu, Wenming; Hong, Xiafei; Liu, Qiuhua; Jiang, Jialin; Ou, Yaobin; Zhao, Yupei; Gong, Xinqi

2018-04-24

Efficient computational recognition and segmentation of target organ from medical images are foundational in diagnosis and treatment, especially about pancreas cancer. In practice, the diversity in appearance of pancreas and organs in abdomen, makes detailed texture information of objects important in segmentation algorithm. According to our observations, however, the structures of previous networks, such as the Richer Feature Convolutional Network (RCF), are too coarse to segment the object (pancreas) accurately, especially the edge. In this paper, we extend the RCF, proposed to the field of edge detection, for the challenging pancreas segmentation, and put forward a novel pancreas segmentation network. By employing multi-layer up-sampling structure replacing the simple up-sampling operation in all stages, the proposed network fully considers the multi-scale detailed contexture information of object (pancreas) to perform per-pixel segmentation. Additionally, using the CT scans, we supply and train our network, thus get an effective pipeline. Working with our pipeline with multi-layer up-sampling model, we achieve better performance than RCF in the task of single object (pancreas) segmentation. Besides, combining with multi scale input, we achieve the 76.36% DSC (Dice Similarity Coefficient) value in testing data. The results of our experiments show that our advanced model works better than previous networks in our dataset. On the other words, it has better ability in catching detailed contexture information. Therefore, our new single object segmentation model has practical meaning in computational automatic diagnosis.

Improvement and Extension of Shape Evaluation Criteria in Multi-Scale Image Segmentation

NASA Astrophysics Data System (ADS)

Sakamoto, M.; Honda, Y.; Kondo, A.

2016-06-01

From the last decade, the multi-scale image segmentation is getting a particular interest and practically being used for object-based image analysis. In this study, we have addressed the issues on multi-scale image segmentation, especially, in improving the performances for validity of merging and variety of derived region's shape. Firstly, we have introduced constraints on the application of spectral criterion which could suppress excessive merging between dissimilar regions. Secondly, we have extended the evaluation for smoothness criterion by modifying the definition on the extent of the object, which was brought for controlling the shape's diversity. Thirdly, we have developed new shape criterion called aspect ratio. This criterion helps to improve the reproducibility on the shape of object to be matched to the actual objectives of interest. This criterion provides constraint on the aspect ratio in the bounding box of object by keeping properties controlled with conventional shape criteria. These improvements and extensions lead to more accurate, flexible, and diverse segmentation results according to the shape characteristics of the target of interest. Furthermore, we also investigated a technique for quantitative and automatic parameterization in multi-scale image segmentation. This approach is achieved by comparing segmentation result with training area specified in advance by considering the maximization of the average area in derived objects or satisfying the evaluation index called F-measure. Thus, it has been possible to automate the parameterization that suited the objectives especially in the view point of shape's reproducibility.

Head Lice.

PubMed

Meister, Laura; Ochsendorf, Falk

2016-11-11

Conflicting information about the proper treatment of head lice has given rise to uncertainty among patients and treating personnel. For example, the reported efficacy of permethrin fell from 97% in the 1990s to 30% in 2010. Review of the literature based on a selective search of PubMed. In Germany, outbreaks of head lice mainly occur among 5- to 13-year-olds returning to school after the summer vacation. Nymphs hatch from eggs after an average of 8 days and become sexually mature lice over the ensuing 9 days. The main route of transmission is direct head-to-head contact; transmission via inanimate objects is of no relevance. Symptoms arise 4-6 weeks after an initial infestation; many affected persons have no symptoms at all. Wet combing is the most sensitive method of establishing the diagnosis and monitoring treatment. Resistance to neurotoxic pediculocidal drugs is increasing around the world. Dimethicones are the treatment of choice, with 97% efficacy. Outbreaks must be managed with the synchronous treatment of all infested persons to break the chain of infestation. If the agent used is not ovicidal, the treatment must be repeated in 8-10 days and sometimes in a further 7 days as well. Outbreaks of head lice can be successfully terminated by synchronous treatment with ovicidal dimethicones.

Sparse intervertebral fence composition for 3D cervical vertebra segmentation

NASA Astrophysics Data System (ADS)

Liu, Xinxin; Yang, Jian; Song, Shuang; Cong, Weijian; Jiao, Peifeng; Song, Hong; Ai, Danni; Jiang, Yurong; Wang, Yongtian

2018-06-01

Statistical shape models are capable of extracting shape prior information, and are usually utilized to assist the task of segmentation of medical images. However, such models require large training datasets in the case of multi-object structures, and it also is difficult to achieve satisfactory results for complex shapes. This study proposed a novel statistical model for cervical vertebra segmentation, called sparse intervertebral fence composition (SiFC), which can reconstruct the boundary between adjacent vertebrae by modeling intervertebral fences. The complex shape of the cervical spine is replaced by a simple intervertebral fence, which considerably reduces the difficulty of cervical segmentation. The final segmentation results are obtained by using a 3D active contour deformation model without shape constraint, which substantially enhances the recognition capability of the proposed method for objects with complex shapes. The proposed segmentation framework is tested on a dataset with CT images from 20 patients. A quantitative comparison against corresponding reference vertebral segmentation yields an overall mean absolute surface distance of 0.70 mm and a dice similarity index of 95.47% for cervical vertebral segmentation. The experimental results show that the SiFC method achieves competitive cervical vertebral segmentation performances, and completely eliminates inter-process overlap.

Object Scene Flow

NASA Astrophysics Data System (ADS)

Menze, Moritz; Heipke, Christian; Geiger, Andreas

2018-06-01

This work investigates the estimation of dense three-dimensional motion fields, commonly referred to as scene flow. While great progress has been made in recent years, large displacements and adverse imaging conditions as observed in natural outdoor environments are still very challenging for current approaches to reconstruction and motion estimation. In this paper, we propose a unified random field model which reasons jointly about 3D scene flow as well as the location, shape and motion of vehicles in the observed scene. We formulate the problem as the task of decomposing the scene into a small number of rigidly moving objects sharing the same motion parameters. Thus, our formulation effectively introduces long-range spatial dependencies which commonly employed local rigidity priors are lacking. Our inference algorithm then estimates the association of image segments and object hypotheses together with their three-dimensional shape and motion. We demonstrate the potential of the proposed approach by introducing a novel challenging scene flow benchmark which allows for a thorough comparison of the proposed scene flow approach with respect to various baseline models. In contrast to previous benchmarks, our evaluation is the first to provide stereo and optical flow ground truth for dynamic real-world urban scenes at large scale. Our experiments reveal that rigid motion segmentation can be utilized as an effective regularizer for the scene flow problem, improving upon existing two-frame scene flow methods. At the same time, our method yields plausible object segmentations without requiring an explicitly trained recognition model for a specific object class.

3D Clumped Cell Segmentation Using Curvature Based Seeded Watershed.

PubMed

Atta-Fosu, Thomas; Guo, Weihong; Jeter, Dana; Mizutani, Claudia M; Stopczynski, Nathan; Sousa-Neves, Rui

2016-12-01

Image segmentation is an important process that separates objects from the background and also from each other. Applied to cells, the results can be used for cell counting which is very important in medical diagnosis and treatment, and biological research that is often used by scientists and medical practitioners. Segmenting 3D confocal microscopy images containing cells of different shapes and sizes is still challenging as the nuclei are closely packed. The watershed transform provides an efficient tool in segmenting such nuclei provided a reasonable set of markers can be found in the image. In the presence of low-contrast variation or excessive noise in the given image, the watershed transform leads to over-segmentation (a single object is overly split into multiple objects). The traditional watershed uses the local minima of the input image and will characteristically find multiple minima in one object unless they are specified (marker-controlled watershed). An alternative to using the local minima is by a supervised technique called seeded watershed, which supplies single seeds to replace the minima for the objects. Consequently, the accuracy of a seeded watershed algorithm relies on the accuracy of the predefined seeds. In this paper, we present a segmentation approach based on the geometric morphological properties of the 'landscape' using curvatures. The curvatures are computed as the eigenvalues of the Shape matrix, producing accurate seeds that also inherit the original shape of their respective cells. We compare with some popular approaches and show the advantage of the proposed method.

Easy-interactive and quick psoriasis lesion segmentation

NASA Astrophysics Data System (ADS)

Ma, Guoli; He, Bei; Yang, Wenming; Shu, Chang

2013-12-01

This paper proposes an interactive psoriasis lesion segmentation algorithm based on Gaussian Mixture Model (GMM). Psoriasis is an incurable skin disease and affects large population in the world. PASI (Psoriasis Area and Severity Index) is the gold standard utilized by dermatologists to monitor the severity of psoriasis. Computer aid methods of calculating PASI are more objective and accurate than human visual assessment. Psoriasis lesion segmentation is the basis of the whole calculating. This segmentation is different from the common foreground/background segmentation problems. Our algorithm is inspired by GrabCut and consists of three main stages. First, skin area is extracted from the background scene by transforming the RGB values into the YCbCr color space. Second, a rough segmentation of normal skin and psoriasis lesion is given. This is an initial segmentation given by thresholding a single gaussian model and the thresholds are adjustable, which enables user interaction. Third, two GMMs, one for the initial normal skin and one for psoriasis lesion, are built to refine the segmentation. Experimental results demonstrate the effectiveness of the proposed algorithm.

Automatic 3D kidney segmentation based on shape constrained GC-OAAM

NASA Astrophysics Data System (ADS)

Chen, Xinjian; Summers, Ronald M.; Yao, Jianhua

2011-03-01

The kidney can be classified into three main tissue types: renal cortex, renal medulla and renal pelvis (or collecting system). Dysfunction of different renal tissue types may cause different kidney diseases. Therefore, accurate and efficient segmentation of kidney into different tissue types plays a very important role in clinical research. In this paper, we propose an automatic 3D kidney segmentation method which segments the kidney into the three different tissue types: renal cortex, medulla and pelvis. The proposed method synergistically combines active appearance model (AAM), live wire (LW) and graph cut (GC) methods, GC-OAAM for short. Our method consists of two main steps. First, a pseudo 3D segmentation method is employed for kidney initialization in which the segmentation is performed slice-by-slice via a multi-object oriented active appearance model (OAAM) method. An improved iterative model refinement algorithm is proposed for the AAM optimization, which synergistically combines the AAM and LW method. Multi-object strategy is applied to help the object initialization. The 3D model constraints are applied to the initialization result. Second, the object shape information generated from the initialization step is integrated into the GC cost computation. A multi-label GC method is used to segment the kidney into cortex, medulla and pelvis. The proposed method was tested on 19 clinical arterial phase CT data sets. The preliminary results showed the feasibility and efficiency of the proposed method.

Image Segmentation Analysis for NASA Earth Science Applications

NASA Technical Reports Server (NTRS)

Tilton, James C.

2010-01-01

NASA collects large volumes of imagery data from satellite-based Earth remote sensing sensors. Nearly all of the computerized image analysis of this data is performed pixel-by-pixel, in which an algorithm is applied directly to individual image pixels. While this analysis approach is satisfactory in many cases, it is usually not fully effective in extracting the full information content from the high spatial resolution image data that s now becoming increasingly available from these sensors. The field of object-based image analysis (OBIA) has arisen in recent years to address the need to move beyond pixel-based analysis. The Recursive Hierarchical Segmentation (RHSEG) software developed by the author is being used to facilitate moving from pixel-based image analysis to OBIA. The key unique aspect of RHSEG is that it tightly intertwines region growing segmentation, which produces spatially connected region objects, with region object classification, which groups sets of region objects together into region classes. No other practical, operational image segmentation approach has this tight integration of region growing object finding with region classification This integration is made possible by the recursive, divide-and-conquer implementation utilized by RHSEG, in which the input image data is recursively subdivided until the image data sections are small enough to successfully mitigat the combinatorial explosion caused by the need to compute the dissimilarity between each pair of image pixels. RHSEG's tight integration of region growing object finding and region classification is what enables the high spatial fidelity of the image segmentations produced by RHSEG. This presentation will provide an overview of the RHSEG algorithm and describe how it is currently being used to support OBIA or Earth Science applications such as snow/ice mapping and finding archaeological sites from remotely sensed data.

Thermal comfort zone of the hands, feet and head in males and females.

PubMed

Ciuha, Urša; Mekjavic, Igor B

2017-10-01

The present study compared the thermal comfort zones (TCZ) of the hands, feet and head in eight male and eight female participants, assessed with water-perfused segments (WPS). On separate occasions, and separated by a minimum of one day, participants were requested to regulate the temperature of three distal skin regions (hands, feet and head) within their TCZ. On each occasion they donned a specific water-perfused segment (WPS), either gloves, socks or hood for assessing the TCZ of the hands, feet and head, respectively. In the absence of regulation, the temperature of the water perfusing the WPS changed in a saw-tooth manner from 10 to 50°C; by depressing a switch and reversing the direction of the temperature at the limits of the TCZ, each participant defined the TCZ for each skin region investigated. The range of regulated temperatures (upper and lower limits of the TCZ) did not differ between studied skin regions or between genders. Participants however maintained higher head (35.7±0.4°C; p˂0.001) skin temperature (Tsk) compared to hands (34.5±0.8°C) and feet (33.8±1.1°C). When exposed to normothermic conditions, distal skin regions do not differ in ranges of temperatures, perceived as thermally comfortable. Copyright © 2017. Published by Elsevier Inc.

Initialisation of 3D level set for hippocampus segmentation from volumetric brain MR images

NASA Astrophysics Data System (ADS)

Hajiesmaeili, Maryam; Dehmeshki, Jamshid; Bagheri Nakhjavanlo, Bashir; Ellis, Tim

2014-04-01

Shrinkage of the hippocampus is a primary biomarker for Alzheimer's disease and can be measured through accurate segmentation of brain MR images. The paper will describe the problem of initialisation of a 3D level set algorithm for hippocampus segmentation that must cope with the some challenging characteristics, such as small size, wide range of intensities, narrow width, and shape variation. In addition, MR images require bias correction, to account for additional inhomogeneity associated with the scanner technology. Due to these inhomogeneities, using a single initialisation seed region inside the hippocampus is prone to failure. Alternative initialisation strategies are explored, such as using multiple initialisations in different sections (such as the head, body and tail) of the hippocampus. The Dice metric is used to validate our segmentation results with respect to ground truth for a dataset of 25 MR images. Experimental results indicate significant improvement in segmentation performance using the multiple initialisations techniques, yielding more accurate segmentation results for the hippocampus.

Genetics, development and composition of the insect head--a beetle's view.

PubMed

Posnien, Nico; Schinko, Johannes B; Kittelmann, Sebastian; Bucher, Gregor

2010-11-01

Many questions regarding evolution and ontogeny of the insect head remain open. Likewise, the genetic basis of insect head development is poorly understood. Recently, the investigation of gene expression data and the analysis of patterning gene function have revived interest in insect head development. Here, we argue that the red flour beetle Tribolium castaneum is a well suited model organism to spearhead research with respect to the genetic control of insect head development. We review recent molecular data and discuss its bearing on early development and morphogenesis of the head. We present a novel hypothesis on the ontogenetic origin of insect head sutures and review recent insights into the question on the origin of the labrum. Further, we argue that the study of developmental genes may identify the elusive anterior non-segmental region and present some evidence in favor of its existence. With respect to the question of evolution of patterning we show that the head Anlagen of the fruit fly Drosophila melanogaster and Tribolium differ considerably and we review profound differences of their genetic regulation. Finally, we discuss which insect model species might help us to answer the open questions concerning the genetic regulation of head development and its evolution. Copyright © 2010 Elsevier Ltd. All rights reserved.

Evolutionary Origin of Body Axis Segmentation in Annelids and Arthropods

NASA Technical Reports Server (NTRS)

Shankland, S. Martin

2003-01-01

During the period of this report, we have made a number of important discoveries. To date this work has led to 4 peer-reviewed publications in primary research journals plus 1 minireview and 1 chapter in the proceedings of a meeting. Publications resulting from this grant support are enumerated at the end of the report. Two additional, on-going studies also described. 1. Using laser cell ablation, we have obtained evidence that an annelid - the leech Helobdella robusta - patterns the anteroposterior (AP) polarity of its nascent segment primordia independent of cell interactions oriented along the AP axis. 2. We cloned a Helobdella homologue (hro-hh) of the Drosophila segment polarity gene hedgehog, and used in situ hybridization and northern blots to characterize its expression in the embryo. 3. We have used laser cell ablations to examine the possible role of cell interactions during the developmental patterning of the 4 rostralmost "head" segments of the leech Helobdella robusta.

Identifying Head-Trunk and Lower Limb Contributions to Gaze Stabilization During Locomotion

NASA Technical Reports Server (NTRS)

Mulavara, Ajitkumar P.; Bloomberg, Jacob J.

2003-01-01

The goal of the present study was to determine how the multiple, interdependent full-body sensorimotor subsystems respond to a change in gaze stabilization task constraints during locomotion. Nine subjects performed two gaze stabilization tasks while walking at 6.4 km/hr on a motorized treadmill: 1) focusing on a central point target; 2) reading numeral characters; both presented at 2m in front at the level of their eyes. While subjects performed the tasks we measured: temporal parameters of gait, full body sagittal plane segmental kinematics of the head, trunk, thigh, shank and foot, accelerations along the vertical axis at the head and the shank, and the vertical forces acting on the support surface. We tested the hypothesis that with the increased demands placed on visual acuity during the number recognition task, subjects would modify full-body segmental kinematics in order to reduce perturbations to the head in order to successfully perform the task. We found that while reading numeral characters as - compared to the central point target: 1) compensatory head pitch movement was on average 22% greater despite the fact that the trunk pitch and trunk vertical translation movement control were not significantly changed; 2) coordination patterns between head and trunk as reflected by the peak cross correlation between the head pitch and trunk pitch motion as well as the peak cross correlation between the head pitch and vertical trunk translation motion were not significantly changed; 3) knee joint total movement was on average 11% greater during the period from the heel strike event to the peak knee flexion event in stance phase of the gait cycle; 4) peak acceleration measured at the head was significantly reduced by an average of 13% in four of the six subjects. This was so even when the peak acceleration at the shank and the transmissibility of the shock wave at heel strike (measured by the peak acceleration ratio of the head/shank) remained unchanged. Taken

[Segment analysis of the target market of physiotherapeutic services].

PubMed

Babaskin, D V

2010-01-01

The objective of the present study was to demonstrate the possibilities to analyse selected segments of the target market of physiotherapeutic services provided by medical and preventive-facilities of two major types. The main features of a target segment, such as provision of therapeutic massage, are illustrated in terms of two characteristics, namely attractiveness to the users and the ability of a given medical facility to satisfy their requirements. Based on the analysis of portfolio of the available target segments the most promising ones (winner segments) were selected for further marketing studies. This choice does not exclude the possibility of involvement of other segments of medical services in marketing activities.

Head Impact Biomechanics in Women's College Soccer.

PubMed

Lynall, Robert C; Clark, Michael D; Grand, Erin E; Stucker, Jaclyn C; Littleton, Ashley C; Aguilar, Alain J; Petschauer, Meredith A; Teel, Elizabeth F; Mihalik, Jason P

2016-09-01

There are limited nonlaboratory soccer head impact biomechanics data. This is surprising given soccer's global popularity. Epidemiological data suggest that female college soccer players are at a greater concussion injury risk than their male counterparts. Therefore, the purposes of our study were to quantify head impact frequency and magnitude during women's soccer practices and games in the National Collegiate Athletic Association and to characterize these data across event type, playing position, year on the team, and segment of game (first and second halves). Head impact biomechanics were collected from female college soccer players (n = 22; mean ± SD age = 19.1 ± 0.1 yr, height = 168.0 ± 3.5 cm, mass = 63.7 ± 6.0 kg). We employed a helmetless head impact measurement device (X2 Biosystems xPatch) before each competition and practice across a single season. Peak linear and rotational accelerations were categorized based on impact magnitude and subsequently analyzed using appropriate nonparametric analyses. Overall, women's college soccer players experience approximately seven impacts per 90 min of game play. The overwhelming majority (~90%) of all head impacts were categorized into our mildest linear acceleration impact classification (10g-20g). Interestingly, a higher percentage of practice impacts in the 20g-40g range compared with games (11% vs 7%) was observed. Head impact biomechanics studies have provided valuable insights into understanding collision sports and for informing evidence-based rule and policy changes. These have included changing the football kickoff, ice hockey body checking ages, and head-to-head hits in both sports. Given soccer's global popularity, and the growing public concern for the potential long-term neurological implications of collision and contact sports, studying soccer has the potential to impact many athletes and the sports medicine professionals caring for them.

Fuzzy connectedness and object definition

NASA Astrophysics Data System (ADS)

Udupa, Jayaram K.; Samarasekera, Supun

1995-04-01

Approaches to object information extraction from images should attempt to use the fact that images are fuzzy. In past image segmentation research, the notion of `hanging togetherness' of image elements specified by their fuzzy connectedness has been lacking. We present a theory of fuzzy objects for n-dimensional digital spaces based on a notion of fuzzy connectedness of image elements. Although our definitions lead to problems of enormous combinatorial complexity, the theoretical results allow us to reduce this dramatically. We demonstrate the utility of the theory and algorithms in image segmentation based on several practical examples.

Volumetric segmentation of range images for printed circuit board inspection

NASA Astrophysics Data System (ADS)

Van Dop, Erik R.; Regtien, Paul P. L.

1996-10-01

Conventional computer vision approaches towards object recognition and pose estimation employ 2D grey-value or color imaging. As a consequence these images contain information about projections of a 3D scene only. The subsequent image processing will then be difficult, because the object coordinates are represented with just image coordinates. Only complicated low-level vision modules like depth from stereo or depth from shading can recover some of the surface geometry of the scene. Recent advances in fast range imaging have however paved the way towards 3D computer vision, since range data of the scene can now be obtained with sufficient accuracy and speed for object recognition and pose estimation purposes. This article proposes the coded-light range-imaging method together with superquadric segmentation to approach this task. Superquadric segments are volumetric primitives that describe global object properties with 5 parameters, which provide the main features for object recognition. Besides, the principle axes of a superquadric segment determine the phase of an object in the scene. The volumetric segmentation of a range image can be used to detect missing, false or badly placed components on assembled printed circuit boards. Furthermore, this approach will be useful to recognize and extract valuable or toxic electronic components on printed circuit boards scrap that currently burden the environment during electronic waste processing. Results on synthetic range images with errors constructed according to a verified noise model illustrate the capabilities of this approach.

Video-based noncooperative iris image segmentation.

PubMed

Du, Yingzi; Arslanturk, Emrah; Zhou, Zhi; Belcher, Craig

2011-02-01

In this paper, we propose a video-based noncooperative iris image segmentation scheme that incorporates a quality filter to quickly eliminate images without an eye, employs a coarse-to-fine segmentation scheme to improve the overall efficiency, uses a direct least squares fitting of ellipses method to model the deformed pupil and limbic boundaries, and develops a window gradient-based method to remove noise in the iris region. A remote iris acquisition system is set up to collect noncooperative iris video images. An objective method is used to quantitatively evaluate the accuracy of the segmentation results. The experimental results demonstrate the effectiveness of this method. The proposed method would make noncooperative iris recognition or iris surveillance possible.

Retinal layer segmentation of macular OCT images using boundary classification

PubMed Central

Lang, Andrew; Carass, Aaron; Hauser, Matthew; Sotirchos, Elias S.; Calabresi, Peter A.; Ying, Howard S.; Prince, Jerry L.

2013-01-01

Optical coherence tomography (OCT) has proven to be an essential imaging modality for ophthalmology and is proving to be very important in neurology. OCT enables high resolution imaging of the retina, both at the optic nerve head and the macula. Macular retinal layer thicknesses provide useful diagnostic information and have been shown to correlate well with measures of disease severity in several diseases. Since manual segmentation of these layers is time consuming and prone to bias, automatic segmentation methods are critical for full utilization of this technology. In this work, we build a random forest classifier to segment eight retinal layers in macular cube images acquired by OCT. The random forest classifier learns the boundary pixels between layers, producing an accurate probability map for each boundary, which is then processed to finalize the boundaries. Using this algorithm, we can accurately segment the entire retina contained in the macular cube to an accuracy of at least 4.3 microns for any of the nine boundaries. Experiments were carried out on both healthy and multiple sclerosis subjects, with no difference in the accuracy of our algorithm found between the groups. PMID:23847738

Anthropometric and biomechanical characteristics on body segments of Koreans.

PubMed

Park, S J; Kim, C B; Park, S C

1999-05-01

This paper documents the physical measurements of the Korean population in order to construct a data base for ergonomic design. The dimension, volume, density, mass, and center of mass of Koreans whose ages range from 7 to 49 were investigated. Sixty-five male subjects and sixty-nine female subjects participated. Eight body segments (head with neck, trunk, thigh, shank, foot, upper arm, forearm and hand) were directly measured with a Martin-type anthropometer, and the immersion method was adopted to measure the volume of body segments. After this, densities were computed by the density equations in Drillis and Contini (1966). The reaction board method was employed for the measurement of the center of mass. Obtained data were compared with the results in the literature. The results in this paper showed different features on body segment parameters comparing with the results in the literature. The constructed data base can be applied to statistical guideline for product design, workspace design, design of clothing and tools, furniture design and construction of biomechanical models for Korean. Also, they can be extended to the application areas for Mongolian.

Head pain referral during examination of the neck in migraine and tension-type headache.

PubMed

Watson, Dean H; Drummond, Peter D

2012-09-01

To investigate if and to what extent typical head pain can be reproduced in tension-type headache (TTH), migraine without aura sufferers, and controls when sustained pressure was applied to the lateral posterior arch of C1 and the articular pillar of C2, stressing the atlantooccipital and C2-3 segments respectively. Occipital and neck symptoms often accompany primary headache, suggesting involvement of cervical afferents in central pain processing mechanisms in these disorders. Referral of head pain from upper cervical structures is made possible by convergence of cervical and trigeminal nociceptive afferent information in the trigemino-cervical nucleus. Upper cervical segmental and C2-3 zygapophysial joint dysfunction is recognized as a potential source of noxious afferent information and is present in primary headache sufferers. Furthermore, referral of head pain has been demonstrated from symptomatic upper cervical segments and the C2-3 zygapophysial joints, suggesting that head pain referral may be a characteristic of cervical afferent involvement in headache. Thirty-four headache sufferers and 14 controls were examined interictally. Headache patients were diagnosed according the criteria of the International Headache Society and comprised 20 migraine without aura (females n = 18; males n = 2; average age 35.3 years) and 14 TTH sufferers (females n = 11; males n = 3; average age 30.7 years). Two techniques were used specifically to stress the atlantooccipital segments (Technique 1 - C1) and C2-3 zygapophysial joints (Technique 2 - C2). Two techniques were also applied to the arm--the common extensor origin and the mid belly of the biceps brachii. Participants reported reproduction of head pain with "yes" or "no" and rated the intensity of head pain and local pressure of application on a scale of 0 -10, where 0 = no pain and 10 = intolerable pain. None of the subjects reported head pain during application of techniques on the arm. Head pain referral during the

Segmentation precision of abdominal anatomy for MRI-based radiotherapy

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

Noel, Camille E.; Zhu, Fan; Lee, Andrew Y.

2014-10-01

The limited soft tissue visualization provided by computed tomography, the standard imaging modality for radiotherapy treatment planning and daily localization, has motivated studies on the use of magnetic resonance imaging (MRI) for better characterization of treatment sites, such as the prostate and head and neck. However, no studies have been conducted on MRI-based segmentation for the abdomen, a site that could greatly benefit from enhanced soft tissue targeting. We investigated the interobserver and intraobserver precision in segmentation of abdominal organs on MR images for treatment planning and localization. Manual segmentation of 8 abdominal organs was performed by 3 independent observersmore » on MR images acquired from 14 healthy subjects. Observers repeated segmentation 4 separate times for each image set. Interobserver and intraobserver contouring precision was assessed by computing 3-dimensional overlap (Dice coefficient [DC]) and distance to agreement (Hausdorff distance [HD]) of segmented organs. The mean and standard deviation of intraobserver and interobserver DC and HD values were DC{sub intraobserver} = 0.89 ± 0.12, HD{sub intraobserver} = 3.6 mm ± 1.5, DC{sub interobserver} = 0.89 ± 0.15, and HD{sub interobserver} = 3.2 mm ± 1.4. Overall, metrics indicated good interobserver/intraobserver precision (mean DC > 0.7, mean HD < 4 mm). Results suggest that MRI offers good segmentation precision for abdominal sites. These findings support the utility of MRI for abdominal planning and localization, as emerging MRI technologies, techniques, and onboard imaging devices are beginning to enable MRI-based radiotherapy.« less

Boundary segmentation for fluorescence microscopy using steerable filters

NASA Astrophysics Data System (ADS)

Ho, David Joon; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J.

2017-02-01

Fluorescence microscopy is used to image multiple subcellular structures in living cells which are not readily observed using conventional optical microscopy. Moreover, two-photon microscopy is widely used to image structures deeper in tissue. Recent advancement in fluorescence microscopy has enabled the generation of large data sets of images at different depths, times, and spectral channels. Thus, automatic object segmentation is necessary since manual segmentation would be inefficient and biased. However, automatic segmentation is still a challenging problem as regions of interest may not have well defined boundaries as well as non-uniform pixel intensities. This paper describes a method for segmenting tubular structures in fluorescence microscopy images of rat kidney and liver samples using adaptive histogram equalization, foreground/background segmentation, steerable filters to capture directional tendencies, and connected-component analysis. The results from several data sets demonstrate that our method can segment tubular boundaries successfully. Moreover, our method has better performance when compared to other popular image segmentation methods when using ground truth data obtained via manual segmentation.

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%).

Rheoencephalographic (REG) Assessment of Head and Neck Cooling for use with Multiple Sclerosis Patients

NASA Technical Reports Server (NTRS)

Montogomery, Leslie D.; Ku, Yu-Tsuan E.; Webbon, Bruce W. (Technical Monitor)

1995-01-01

We have prepared a computer program (RHEOSYS:RHEOencephalographic impedance trace scanning SyStem) that can be used to automate the analysis of segmental impedance blood flow waveforms. This program was developed to assist in the post test analysis of recorded impedance traces from multiple segments of the body. It incorporates many of the blood flow, segmental volume, and vascular state indices reported in the world literature. As it is currently programmed, seven points are selected from each blood flow pulse and associated ECG waveforrn: 1. peak of the first ECG QRS complex, 2. start of systolic slope on the blood flow trace, 3. maximum amplitude of the impedance pulse, 4. position of the dicrotic notch, 5. maximum amplitude of the postdicrotic segment, 6. peak of the second ECG QRS complex, and 7. start of the next blood flow pulse. These points we used to calculate various geometric, area, and time-related values associated with the impedance pulse morphology. RHEOSYS then calculates a series of 34 impedance and cardiac cycle parameters which include pulse amplitudes; areas; pulse propagation times; cardiac cycle times; and various measures of arterial and various tone, contractility, and pulse volume. We used this program to calculate the scalp and intracranial blood flow responses to head and neck cooling as it may be applied to lower the body temperatures of multiple sclerosis patients. Twelve women and twelve men were tested using a commercially available head and neck cooling system operated at its maximum cooling capacity for a period of 30 minutes. Head and neck cooling produced a transient change in scalp blood flow and a significant, (P<0.05) decrease of approx. 30% in intracranial blood flow. Results of this experiment will illustrate how REG and RHEOSYS can be used in biomedical applications.

BOSS: context-enhanced search for biomedical objects

PubMed Central

2012-01-01

Background There exist many academic search solutions and most of them can be put on either ends of spectrum: general-purpose search and domain-specific "deep" search systems. The general-purpose search systems, such as PubMed, offer flexible query interface, but churn out a list of matching documents that users have to go through the results in order to find the answers to their queries. On the other hand, the "deep" search systems, such as PPI Finder and iHOP, return the precompiled results in a structured way. Their results, however, are often found only within some predefined contexts. In order to alleviate these problems, we introduce a new search engine, BOSS, Biomedical Object Search System. Methods Unlike the conventional search systems, BOSS indexes segments, rather than documents. A segment refers to a Maximal Coherent Semantic Unit (MCSU) such as phrase, clause or sentence that is semantically coherent in the given context (e.g., biomedical objects or their relations). For a user query, BOSS finds all matching segments, identifies the objects appearing in those segments, and aggregates the segments for each object. Finally, it returns the ranked list of the objects along with their matching segments. Results The working prototype of BOSS is available at http://boss.korea.ac.kr. The current version of BOSS has indexed abstracts of more than 20 million articles published during last 16 years from 1996 to 2011 across all science disciplines. Conclusion BOSS fills the gap between either ends of the spectrum by allowing users to pose context-free queries and by returning a structured set of results. Furthermore, BOSS exhibits the characteristic of good scalability, just as with conventional document search engines, because it is designed to use a standard document-indexing model with minimal modifications. Considering the features, BOSS notches up the technological level of traditional solutions for search on biomedical information. PMID:22595092

Unsupervised segmentation of H and E breast images

NASA Astrophysics Data System (ADS)

Hope, Tyna A.; Yaffe, Martin J.

2017-03-01

Heterogeneity of ductal carcinoma in situ (DCIS) continues to be an important topic. Combining biomarker and hematoxylin and eosin (HE) morphology information may provide more insights than either alone. We are working towards a computer-based identification and description system for DCIS. As part of the system we are developing a region of interest finder for further processing, such as identifying DCIS and other HE based measures. The segmentation algorithm is designed to be tolerant of variability in staining and require no user interaction. To achieve stain variation tolerance we use unsupervised learning and iteratively interrogate the image for information. Using simple rules (e.g., "hematoxylin stains nuclei") and iteratively assessing the resultant objects (small hematoxylin stained objects are lymphocytes), the system builds up a knowledge base so that it is not dependent upon manual annotations. The system starts with image resolution-based assumptions but these are replaced by knowledge gained. The algorithm pipeline is designed to find the simplest items first (segment stains), then interesting subclasses and objects (stroma, lymphocytes), and builds information until it is possible to segment blobs that are normal, DCIS, and the range of benign glands. Once the blobs are found, features can be obtained and DCIS detected. In this work we present the early segmentation results with stains where hematoxylin ranges from blue dominant to red dominant in RGB space.

Accuracy of patient-specific organ dose estimates obtained using an automated image segmentation algorithm.

PubMed

Schmidt, Taly Gilat; Wang, Adam S; Coradi, Thomas; Haas, Benjamin; Star-Lack, Josh

2016-10-01

The overall goal of this work is to develop a rapid, accurate, and automated software tool to estimate patient-specific organ doses from computed tomography (CT) scans using simulations to generate dose maps combined with automated segmentation algorithms. This work quantified the accuracy of organ dose estimates obtained by an automated segmentation algorithm. We hypothesized that the autosegmentation algorithm is sufficiently accurate to provide organ dose estimates, since small errors delineating organ boundaries will have minimal effect when computing mean organ dose. A leave-one-out validation study of the automated algorithm was performed with 20 head-neck CT scans expertly segmented into nine regions. Mean organ doses of the automatically and expertly segmented regions were computed from Monte Carlo-generated dose maps and compared. The automated segmentation algorithm estimated the mean organ dose to be within 10% of the expert segmentation for regions other than the spinal canal, with the median error for each organ region below 2%. In the spinal canal region, the median error was [Formula: see text], with a maximum absolute error of 28% for the single-atlas approach and 11% for the multiatlas approach. The results demonstrate that the automated segmentation algorithm can provide accurate organ dose estimates despite some segmentation errors.

Accuracy of patient-specific organ dose estimates obtained using an automated image segmentation algorithm

PubMed Central

Schmidt, Taly Gilat; Wang, Adam S.; Coradi, Thomas; Haas, Benjamin; Star-Lack, Josh

2016-01-01

Abstract. The overall goal of this work is to develop a rapid, accurate, and automated software tool to estimate patient-specific organ doses from computed tomography (CT) scans using simulations to generate dose maps combined with automated segmentation algorithms. This work quantified the accuracy of organ dose estimates obtained by an automated segmentation algorithm. We hypothesized that the autosegmentation algorithm is sufficiently accurate to provide organ dose estimates, since small errors delineating organ boundaries will have minimal effect when computing mean organ dose. A leave-one-out validation study of the automated algorithm was performed with 20 head-neck CT scans expertly segmented into nine regions. Mean organ doses of the automatically and expertly segmented regions were computed from Monte Carlo-generated dose maps and compared. The automated segmentation algorithm estimated the mean organ dose to be within 10% of the expert segmentation for regions other than the spinal canal, with the median error for each organ region below 2%. In the spinal canal region, the median error was −7%, with a maximum absolute error of 28% for the single-atlas approach and 11% for the multiatlas approach. The results demonstrate that the automated segmentation algorithm can provide accurate organ dose estimates despite some segmentation errors. PMID:27921070

A qualitative systematic review of head-to-head randomized controlled trials of oral analgesics in neuropathic pain

PubMed Central

Watson, C Peter N; Gilron, Ian; Sawynok, Jana

2010-01-01

BACKGROUND: Neuropathic pain (NP) encompasses many difficult-to-treat disorders. There are few head-to-head, comparative, randomized controlled trials (RCTs) of drugs for NP in different analgesic categories, or of different drugs within a category, despite many placebo-controlled RCTs for individual agents. Well-designed head-to-head comparative trials are an effective way to determine the relative efficacy and safety of a new drug. OBJECTIVE: To perform a systematic review of head-to-head RCTs of oral analgesics in NP. METHODS: A systematic review of RCTs involving NP patients was performed, of which head-to-head comparative trials were selected. Reference lists from published systematic reviews were searched. These studies were rated according to the Jadad scale for quality. RESULTS AND CONCLUSIONS: Twenty-seven such trials were identified. Seventeen were comparisons of different analgesics, and 10 were of different drugs within an analgesic class. Important information was obtained about the relative efficacy and safety of drugs in different categories and within a category. Some significant differences between active treatments were reported. Trial inadequacies were identified. More and improved head-to-head RCTs are needed to inform clinical choices. PMID:20577657

Relationship between vertebral artery blood flow in different head positions and vertigo.

PubMed

Araz Server, Ela; Edizer, Deniz Tuna; Yiğit, Özgür; Yasak, Ahmet Görkem; Erdim, Çağrı

2018-01-01

To identify the vertebral artery blood flow in different head positions in patients with positional vertigo with no specific diagnosis. Patients with history of vestibular symptoms associated with changes in head position were enrolled into the study. Healthy volunteers were evaluated as control group. Doppler ultrasonography examination of the cervical segment of the vertebral arteries was performed under three different head positions: (i) supine position, (ii) head hyperextended and rotated to the right side and (iii) head hyperextended and rotated to the left side. In the study group, right and left vertebral artery blood flow was significantly lower in the ipsilateral hyperextended position compared to standard supine position (respectively p = .014; p = .001), but did not differ significantly when compared between the standard supine and contralateral hyperextended positions (respectively = .959; p = .669). In the control group, left and right vertebral artery blood flow did not differ significantly when the head was hyperextended to the right or left sides compared to standard supine position (p > .05). Our data demonstrated that the etiology of vestibular complaints in patients with undiagnosed positional vertigo might be related to impairment in vertebral artery blood flow according to head positions.

Comparison of segmentation algorithms for fluorescence microscopy images of cells.

PubMed

Dima, Alden A; Elliott, John T; Filliben, James J; Halter, Michael; Peskin, Adele; Bernal, Javier; Kociolek, Marcin; Brady, Mary C; Tang, Hai C; Plant, Anne L

2011-07-01

The analysis of fluorescence microscopy of cells often requires the determination of cell edges. This is typically done using segmentation techniques that separate the cell objects in an image from the surrounding background. This study compares segmentation results from nine different segmentation techniques applied to two different cell lines and five different sets of imaging conditions. Significant variability in the results of segmentation was observed that was due solely to differences in imaging conditions or applications of different algorithms. We quantified and compared the results with a novel bivariate similarity index metric that evaluates the degree of underestimating or overestimating a cell object. The results show that commonly used threshold-based segmentation techniques are less accurate than k-means clustering with multiple clusters. Segmentation accuracy varies with imaging conditions that determine the sharpness of cell edges and with geometric features of a cell. Based on this observation, we propose a method that quantifies cell edge character to provide an estimate of how accurately an algorithm will perform. The results of this study will assist the development of criteria for evaluating interlaboratory comparability. Published 2011 Wiley-Liss, Inc.

Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery.

PubMed

Belgiu, Mariana; Dr Guţ, Lucian

2014-10-01

Although multiresolution segmentation (MRS) is a powerful technique for dealing with very high resolution imagery, some of the image objects that it generates do not match the geometries of the target objects, which reduces the classification accuracy. MRS can, however, be guided to produce results that approach the desired object geometry using either supervised or unsupervised approaches. Although some studies have suggested that a supervised approach is preferable, there has been no comparative evaluation of these two approaches. Therefore, in this study, we have compared supervised and unsupervised approaches to MRS. One supervised and two unsupervised segmentation methods were tested on three areas using QuickBird and WorldView-2 satellite imagery. The results were assessed using both segmentation evaluation methods and an accuracy assessment of the resulting building classifications. Thus, differences in the geometries of the image objects and in the potential to achieve satisfactory thematic accuracies were evaluated. The two approaches yielded remarkably similar classification results, with overall accuracies ranging from 82% to 86%. The performance of one of the unsupervised methods was unexpectedly similar to that of the supervised method; they identified almost identical scale parameters as being optimal for segmenting buildings, resulting in very similar geometries for the resulting image objects. The second unsupervised method produced very different image objects from the supervised method, but their classification accuracies were still very similar. The latter result was unexpected because, contrary to previously published findings, it suggests a high degree of independence between the segmentation results and classification accuracy. The results of this study have two important implications. The first is that object-based image analysis can be automated without sacrificing classification accuracy, and the second is that the previously accepted idea

The role of oscillatory brain activity in object processing and figure-ground segmentation in human vision.

PubMed

Kinsey, K; Anderson, S J; Hadjipapas, A; Holliday, I E

2011-03-01

The perception of an object as a single entity within a visual scene requires that its features are bound together and segregated from the background and/or other objects. Here, we used magnetoencephalography (MEG) to assess the hypothesis that coherent percepts may arise from the synchronized high frequency (gamma) activity between neurons that code features of the same object. We also assessed the role of low frequency (alpha, beta) activity in object processing. The target stimulus (i.e. object) was a small patch of a concentric grating of 3c/°, viewed eccentrically. The background stimulus was either a blank field or a concentric grating of 3c/° periodicity, viewed centrally. With patterned backgrounds, the target stimulus emerged--through rotation about its own centre--as a circular subsection of the background. Data were acquired using a 275-channel whole-head MEG system and analyzed using Synthetic Aperture Magnetometry (SAM), which allows one to generate images of task-related cortical oscillatory power changes within specific frequency bands. Significant oscillatory activity across a broad range of frequencies was evident at the V1/V2 border, and subsequent analyses were based on a virtual electrode at this location. When the target was presented in isolation, we observed that: (i) contralateral stimulation yielded a sustained power increase in gamma activity; and (ii) both contra- and ipsilateral stimulation yielded near identical transient power changes in alpha (and beta) activity. When the target was presented against a patterned background, we observed that: (i) contralateral stimulation yielded an increase in high-gamma (>55 Hz) power together with a decrease in low-gamma (40-55 Hz) power; and (ii) both contra- and ipsilateral stimulation yielded a transient decrease in alpha (and beta) activity, though the reduction tended to be greatest for contralateral stimulation. The opposing power changes across different regions of the gamma spectrum with

Automatic tissue image segmentation based on 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 multimodality imaging, especially in fusion structural images offered by CT, MRI with functional images collected by optical technologies or other novel imaging technologies. Plus, image segmentation also provides detailed structure description for quantitative visualization of treating light distribution in the human body when incorporated with 3D light transport simulation method. Here we used image enhancement, operators, and 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 a deep learning way. We also introduced parallel computing. Such approaches 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. Our results can be used as a criteria when diagnosing diseases such as cerebral atrophy, which is caused by pathological changes in gray matter or white matter. We demonstrated the great potential of such image processing and deep leaning combined automatic tissue image segmentation in personalized medicine, especially in monitoring, and treatments.

Coregistered FDG PET/CT-based textural characterization of head and neck cancer for radiation treatment planning.

PubMed

Yu, Huan; Caldwell, Curtis; Mah, Katherine; Mozeg, Daniel

2009-03-01

Coregistered fluoro-deoxy-glucose (FDG) positron emission tomography/computed tomography (PET/CT) has shown potential to improve the accuracy of radiation targeting of head and neck cancer (HNC) when compared to the use of CT simulation alone. The objective of this study was to identify textural features useful in distinguishing tumor from normal tissue in head and neck via quantitative texture analysis of coregistered 18F-FDG PET and CT images. Abnormal and typical normal tissues were manually segmented from PET/CT images of 20 patients with HNC and 20 patients with lung cancer. Texture features including some derived from spatial grey-level dependence matrices (SGLDM) and neighborhood gray-tone-difference matrices (NGTDM) were selected for characterization of these segmented regions of interest (ROIs). Both K nearest neighbors (KNNs) and decision tree (DT)-based KNN classifiers were employed to discriminate images of abnormal and normal tissues. The area under the curve (AZ) of receiver operating characteristics (ROC) was used to evaluate the discrimination performance of features in comparison to an expert observer. The leave-one-out and bootstrap techniques were used to validate the results. The AZ of DT-based KNN classifier was 0.95. Sensitivity and specificity for normal and abnormal tissue classification were 89% and 99%, respectively. In summary, NGTDM features such as PET Coarseness, PET Contrast, and CT Coarseness extracted from FDG PET/CT images provided good discrimination performance. The clinical use of such features may lead to improvement in the accuracy of radiation targeting of HNC.

Automated Sperm Head Detection Using Intersecting Cortical Model Optimised by Particle Swarm Optimization.

PubMed

Tan, Weng Chun; Mat Isa, Nor Ashidi

2016-01-01

In human sperm motility analysis, sperm segmentation plays an important role to determine the location of multiple sperms. To ensure an improved segmentation result, the Laplacian of Gaussian filter is implemented as a kernel in a pre-processing step before applying the image segmentation process to automatically segment and detect human spermatozoa. This study proposes an intersecting cortical model (ICM), which was derived from several visual cortex models, to segment the sperm head region. However, the proposed method suffered from parameter selection; thus, the ICM network is optimised using particle swarm optimization where feature mutual information is introduced as the new fitness function. The final results showed that the proposed method is more accurate and robust than four state-of-the-art segmentation methods. The proposed method resulted in rates of 98.14%, 98.82%, 86.46% and 99.81% in accuracy, sensitivity, specificity and precision, respectively, after testing with 1200 sperms. The proposed algorithm is expected to be implemented in analysing sperm motility because of the robustness and capability of this algorithm.

Fuzzy-C-Means Clustering Based Segmentation and CNN-Classification for Accurate Segmentation of Lung Nodules

PubMed

K, Jalal Deen; R, Ganesan; A, Merline

2017-07-27

Objective: Accurate segmentation of abnormal and healthy lungs is very crucial for a steadfast computer-aided disease diagnostics. Methods: For this purpose a stack of chest CT scans are processed. In this paper, novel methods are proposed for segmentation of the multimodal grayscale lung CT scan. In the conventional methods using Markov–Gibbs Random Field (MGRF) model the required regions of interest (ROI) are identified. Result: The results of proposed FCM and CNN based process are compared with the results obtained from the conventional method using MGRF model. The results illustrate that the proposed method can able to segment the various kinds of complex multimodal medical images precisely. Conclusion: However, in this paper, to obtain an exact boundary of the regions, every empirical dispersion of the image is computed by Fuzzy C-Means Clustering segmentation. A classification process based on the Convolutional Neural Network (CNN) classifier is accomplished to distinguish the normal tissue and the abnormal tissue. The experimental evaluation is done using the Interstitial Lung Disease (ILD) database. Creative Commons Attribution License

Fuzzy-C-Means Clustering Based Segmentation and CNN-Classification for Accurate Segmentation of Lung Nodules

PubMed Central

K, Jalal Deen; R, Ganesan; A, Merline

2017-01-01

Objective: Accurate segmentation of abnormal and healthy lungs is very crucial for a steadfast computer-aided disease diagnostics. Methods: For this purpose a stack of chest CT scans are processed. In this paper, novel methods are proposed for segmentation of the multimodal grayscale lung CT scan. In the conventional methods using Markov–Gibbs Random Field (MGRF) model the required regions of interest (ROI) are identified. Result: The results of proposed FCM and CNN based process are compared with the results obtained from the conventional method using MGRF model. The results illustrate that the proposed method can able to segment the various kinds of complex multimodal medical images precisely. Conclusion: However, in this paper, to obtain an exact boundary of the regions, every empirical dispersion of the image is computed by Fuzzy C-Means Clustering segmentation. A classification process based on the Convolutional Neural Network (CNN) classifier is accomplished to distinguish the normal tissue and the abnormal tissue. The experimental evaluation is done using the Interstitial Lung Disease (ILD) database. PMID:28749127

Aging and the segmentation of narrative film.

PubMed

Kurby, Christopher A; Asiala, Lillian K E; Mills, Steven R

2014-01-01

The perception of event structure in continuous activity is important for everyday comprehension. Although the segmentation of experience into events is a normal concomitant of perceptual processing, previous research has shown age differences in the ability to perceive structure in naturalistic activity, such as a movie of someone washing a car. However, past research has also shown that older adults have a preserved ability to comprehend events in narrative text, which suggests that narrative may improve the event processing of older adults. This study tested whether there are age differences in event segmentation at the intersection of continuous activity and narrative: narrative film. Younger and older adults watched and segmented a narrative film, The Red Balloon, into coarse and fine events. Changes in situational features, such as changes in characters, goals, and objects predicted segmentation. Analyses revealed little age-difference in segmentation behavior. This suggests the possibility that narrative structure supports event understanding for older adults.

Objective assessment of swallowing dysfunction and aspiration after radiation concurrent with chemotherapy for head-and-neck cancer.

PubMed

Eisbruch, Avraham; Lyden, Teresa; Bradford, Carol R; Dawson, Laura A; Haxer, Marc J; Miller, Amy E; Teknos, Theodoros N; Chepeha, Douglas B; Hogikyan, Norman D; Terrell, Jeffrey E; Wolf, Gregory T

2002-05-01

To objectively assess swallowing function after an intensive chemoradiation regimen for locally advanced head-and-neck cancer and to assess the clinical implications of swallowing dysfunction. Twenty-nine patients with nonresectable Stage IV head-and-neck cancer participated in a Phase I study of radiation, 70 Gy/7 weeks, concurrent with weekly gemcitabine. Because of a high rate of mucosal toxicity, reduced drug doses were delivered to subsequent patient groups: 300, 150, 50, and 10 mg/m(2)/week. Twenty-six of these patients underwent prospective evaluation of swallowing function with videofluoroscopy and esophagogram. Studies were performed pretherapy, early post-therapy (1-3 months), and late post-therapy (6-12 months). Complete tests were performed pretherapy in 22 patients, early post-therapy in 20, and late post-therapy in 13. Twenty-five patients had at least one post-therapy study. Post-therapy dysfunction was characterized by reduced inversion of the epiglottis, delayed swallow initiation and uncoordinated timing of the propulsion of the bolus, opening of the cricopharyngeal muscle, and closure of the larynx, all of which promoted aspiration during and after the swallow. In addition, reduced base-of-tongue retraction with reduced contact to the posterior pharyngeal wall and incomplete cricopharyngeal relaxation resulted in pooling in the pyriform sinuses and vallecula of residue, which was frequently aspirated after the swallow. Post-therapy aspirations were typically "silent," eliciting no cough reflex, or the cough was delayed and noneffective in expelling the residue. Aspiration was observed in 3 patients (14%) in the pretherapy studies, in 13 (65%) in the early post-therapy studies, and in 8 (62%) in the late post-therapy studies (aspiration rates post-therapy vs. pretherapy: p = 0.0002). Six patients had pneumonia requiring hospitalization 1-14 months after therapy (median: 2.5 months), being the likely cause of death in 2 patients. Five cases of

Contact-free determination of human body segment parameters by means of videometric image processing of an anthropomorphic body model

NASA Astrophysics Data System (ADS)

Hatze, Herbert; Baca, Arnold

1993-01-01

The development of noninvasive techniques for the determination of biomechanical body segment parameters (volumes, masses, the three principal moments of inertia, the three local coordinates of the segmental mass centers, etc.) receives increasing attention from the medical sciences (e,.g., orthopaedic gait analysis), bioengineering, sport biomechanics, and the various space programs. In the present paper, a novel method is presented for determining body segment parameters rapidly and accurately. It is based on the video-image processing of four different body configurations and a finite mass-element human body model. The four video images of the subject in question are recorded against a black background, thus permitting the application of shape recognition procedures incorporating edge detection and calibration algorithms. In this way, a total of 181 object space dimensions of the subject's body segments can be reconstructed and used as anthropometric input data for the mathematical finite mass- element body model. The latter comprises 17 segments (abdomino-thoracic, head-neck, shoulders, upper arms, forearms, hands, abdomino-pelvic, thighs, lower legs, feet) and enables the user to compute all the required segment parameters for each of the 17 segments by means of the associated computer program. The hardware requirements are an IBM- compatible PC (1 MB memory) operating under MS-DOS or PC-DOS (Version 3.1 onwards) and incorporating a VGA-board with a feature connector for connecting it to a super video windows framegrabber board for which there must be available a 16-bit large slot. In addition, a VGA-monitor (50 - 70 Hz, horizontal scan rate at least 31.5 kHz), a common video camera and recorder, and a simple rectangular calibration frame are required. The advantage of the new method lies in its ease of application, its comparatively high accuracy, and in the rapid availability of the body segment parameters, which is particularly useful in clinical practice

Head Rotation Detection in Marmoset Monkeys

NASA Astrophysics Data System (ADS)

Simhadri, Sravanthi

Head movement is known to have the benefit of improving the accuracy of sound localization for humans and animals. Marmoset is a small bodied New World monkey species and it has become an emerging model for studying the auditory functions. This thesis aims to detect the horizontal and vertical rotation of head movement in marmoset monkeys. Experiments were conducted in a sound-attenuated acoustic chamber. Head movement of marmoset monkey was studied under various auditory and visual stimulation conditions. With increasing complexity, these conditions are (1) idle, (2) sound-alone, (3) sound and visual signals, and (4) alert signal by opening and closing of the chamber door. All of these conditions were tested with either house light on or off. Infra-red camera with a frame rate of 90 Hz was used to capture of the head movement of monkeys. To assist the signal detection, two circular markers were attached to the top of monkey head. The data analysis used an image-based marker detection scheme. Images were processed using the Computation Vision Toolbox in Matlab. The markers and their positions were detected using blob detection techniques. Based on the frame-by-frame information of marker positions, the angular position, velocity and acceleration were extracted in horizontal and vertical planes. Adaptive Otsu Thresholding, Kalman filtering and bound setting for marker properties were used to overcome a number of challenges encountered during this analysis, such as finding image segmentation threshold, continuously tracking markers during large head movement, and false alarm detection. The results show that the blob detection method together with Kalman filtering yielded better performances than other image based techniques like optical flow and SURF features .The median of the maximal head turn in the horizontal plane was in the range of 20 to 70 degrees and the median of the maximal velocity in horizontal plane was in the range of a few hundreds of degrees per

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.

A systematic review of military head injuries.

PubMed

Carr, Debra J; Lewis, E; Horsfall, I

2017-02-01

This commissioned review discusses military head injuries caused by non-ballistic impacts, penetrating fragments and bullets (including parts of bullets) and behind helmet blunt trauma (BHBT). A systematic review of the literature was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method. The openly accessible literature was reviewed to investigate military head injuries and their severity. Fifty-four sources were identified that included pertinent openly accessible information relevant to this topic. Limited injury data exist for non-ballistic head injuries for UK forces, although some international data exist for parachutists. The majority of fatal head injuries are due to projectiles penetrating through the face rather than through the area of the head covered by the helmet. Penetrating head injuries are primarily caused by fragments, but helmets are more commonly perforated by high-energy rifle bullets than by fragments. No reports of a BHBT injury have been located in the literature. The description of body segment varies among articles and this makes comparisons among datasets difficult. There is a lack of detail regarding the precise position and severity of injuries, and long-term outcome for casualties. It is demonstrated that wearing military helmets reduces fatalities on and off the battlefield. The risk of BHBT injuries is widely referred to, but evidence of their occurrence is not provided by the authors that describe the risk of BHBT occurring. Further research into the causes and severity of head injuries would be useful for designers of military helmets and other associated personal protective equipment, particularly as advances in materials technology means lighter, thinner and more protective helmets are achievable. 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/.

Moving object detection using dynamic motion modelling from UAV aerial images.

PubMed

Saif, A F M Saifuddin; Prabuwono, Anton Satria; Mahayuddin, Zainal Rasyid

2014-01-01

Motion analysis based moving object detection from UAV aerial image is still an unsolved issue due to inconsideration of proper motion estimation. Existing moving object detection approaches from UAV aerial images did not deal with motion based pixel intensity measurement to detect moving object robustly. Besides current research on moving object detection from UAV aerial images mostly depends on either frame difference or segmentation approach separately. There are two main purposes for this research: firstly to develop a new motion model called DMM (dynamic motion model) and secondly to apply the proposed segmentation approach SUED (segmentation using edge based dilation) using frame difference embedded together with DMM model. The proposed DMM model provides effective search windows based on the highest pixel intensity to segment only specific area for moving object rather than searching the whole area of the frame using SUED. At each stage of the proposed scheme, experimental fusion of the DMM and SUED produces extracted moving objects faithfully. Experimental result reveals that the proposed DMM and SUED have successfully demonstrated the validity of the proposed methodology.

Hierarchical extraction of urban objects from mobile laser scanning data

NASA Astrophysics Data System (ADS)

Yang, Bisheng; Dong, Zhen; Zhao, Gang; Dai, Wenxia

2015-01-01

Point clouds collected in urban scenes contain a huge number of points (e.g., billions), numerous objects with significant size variability, complex and incomplete structures, and variable point densities, raising great challenges for the automated extraction of urban objects in the field of photogrammetry, computer vision, and robotics. This paper addresses these challenges by proposing an automated method to extract urban objects robustly and efficiently. The proposed method generates multi-scale supervoxels from 3D point clouds using the point attributes (e.g., colors, intensities) and spatial distances between points, and then segments the supervoxels rather than individual points by combining graph based segmentation with multiple cues (e.g., principal direction, colors) of the supervoxels. The proposed method defines a set of rules for merging segments into meaningful units according to types of urban objects and forms the semantic knowledge of urban objects for the classification of objects. Finally, the proposed method extracts and classifies urban objects in a hierarchical order ranked by the saliency of the segments. Experiments show that the proposed method is efficient and robust for extracting buildings, streetlamps, trees, telegraph poles, traffic signs, cars, and enclosures from mobile laser scanning (MLS) point clouds, with an overall accuracy of 92.3%.

Head-bobbing behavior in foraging Whooping Cranes

USGS Publications Warehouse

Cronin, T.; Kinloch, M.; Olsen, Glenn H.

2006-01-01

Many species of cursorial birds 'head-bob', that is, they alternately thrust the head forward, then hold it stiII as they walk. Such a motion stabilizes visual fields intermittently and could be critical for visual search; yet the time available for stabilization vs. forward thrust varies with walking speed. Whooping Cranes (Grus americana) are extremely tall birds that visually search the ground for seeds, berries, and small prey. We examined head movements in unrestrained Whooping Cranes using digital video subsequently analyzed with a computer graphical overlay. When foraging, the cranes walk at speeds that allow the head to be held still for at least 50% of the time. This behavior is thought to balance the two needs for covering as much ground as possible and for maximizing the time for visual fixation of the ground in the search for prey. Our results strongly suggest that in cranes, and probably many other bird species, visual fixation of the ground is required for object detection and identification. The thrust phase of the head-bobbing cycle is probably also important for vision. As the head moves forward, the movement generates visual flow and motion parallax, providing visual cues for distances and the relative locations of objects. The eyes commonly change their point of fixation when the head is moving too, suggesting that they remain visually competent throughout the entire cycle of thrust and stabilization.

Interactive 3D segmentation using connected orthogonal contours.

PubMed

de Bruin, P W; Dercksen, V J; Post, F H; Vossepoel, A M; Streekstra, G J; Vos, F M

2005-05-01

This paper describes a new method for interactive segmentation that is based on cross-sectional design and 3D modelling. The method represents a 3D model by a set of connected contours that are planar and orthogonal. Planar contours overlayed on image data are easily manipulated and linked contours reduce the amount of user interaction.1 This method solves the contour-to-contour correspondence problem and can capture extrema of objects in a more flexible way than manual segmentation of a stack of 2D images. The resulting 3D model is guaranteed to be free of geometric and topological errors. We show that manual segmentation using connected orthogonal contours has great advantages over conventional manual segmentation. Furthermore, the method provides effective feedback and control for creating an initial model for, and control and steering of, (semi-)automatic segmentation methods.

Segmentation and learning in the quantitative analysis of microscopy images

NASA Astrophysics Data System (ADS)

Ruggiero, Christy; Ross, Amy; Porter, Reid

2015-02-01

In material science and bio-medical domains the quantity and quality of microscopy images is rapidly increasing and there is a great need to automatically detect, delineate and quantify particles, grains, cells, neurons and other functional "objects" within these images. These are challenging problems for image processing because of the variability in object appearance that inevitably arises in real world image acquisition and analysis. One of the most promising (and practical) ways to address these challenges is interactive image segmentation. These algorithms are designed to incorporate input from a human operator to tailor the segmentation method to the image at hand. Interactive image segmentation is now a key tool in a wide range of applications in microscopy and elsewhere. Historically, interactive image segmentation algorithms have tailored segmentation on an image-by-image basis, and information derived from operator input is not transferred between images. But recently there has been increasing interest to use machine learning in segmentation to provide interactive tools that accumulate and learn from the operator input over longer periods of time. These new learning algorithms reduce the need for operator input over time, and can potentially provide a more dynamic balance between customization and automation for different applications. This paper reviews the state of the art in this area, provides a unified view of these algorithms, and compares the segmentation performance of various design choices.

Simultaneous segmentation of the bone and cartilage surfaces of a knee joint in 3D

NASA Astrophysics Data System (ADS)

Yin, Y.; Zhang, X.; Anderson, D. D.; Brown, T. D.; Hofwegen, C. Van; Sonka, M.

2009-02-01

We present a novel framework for the simultaneous segmentation of multiple interacting surfaces belonging to multiple mutually interacting objects. The method is a non-trivial extension of our previously reported optimal multi-surface segmentation. Considering an example application of knee-cartilage segmentation, the framework consists of the following main steps: 1) Shape model construction: Building a mean shape for each bone of the joint (femur, tibia, patella) from interactively segmented volumetric datasets. Using the resulting mean-shape model - identification of cartilage, non-cartilage, and transition areas on the mean-shape bone model surfaces. 2) Presegmentation: Employment of iterative optimal surface detection method to achieve approximate segmentation of individual bone surfaces. 3) Cross-object surface mapping: Detection of inter-bone equidistant separating sheets to help identify corresponding vertex pairs for all interacting surfaces. 4) Multi-object, multi-surface graph construction and final segmentation: Construction of a single multi-bone, multi-surface graph so that two surfaces (bone and cartilage) with zero and non-zero intervening distances can be detected for each bone of the joint, according to whether or not cartilage can be locally absent or present on the bone. To define inter-object relationships, corresponding vertex pairs identified using the separating sheets were interlinked in the graph. The graph optimization algorithm acted on the entire multiobject, multi-surface graph to yield a globally optimal solution. The segmentation framework was tested on 16 MR-DESS knee-joint datasets from the Osteoarthritis Initiative database. The average signed surface positioning error for the 6 detected surfaces ranged from 0.00 to 0.12 mm. When independently initialized, the signed reproducibility error of bone and cartilage segmentation ranged from 0.00 to 0.26 mm. The results showed that this framework provides robust, accurate, and

A local segmentation parameter optimization approach for mapping heterogeneous urban environments using VHR imagery

NASA Astrophysics Data System (ADS)

Grippa, Tais; Georganos, Stefanos; Lennert, Moritz; Vanhuysse, Sabine; Wolff, Eléonore

2017-10-01

Mapping large heterogeneous urban areas using object-based image analysis (OBIA) remains challenging, especially with respect to the segmentation process. This could be explained both by the complex arrangement of heterogeneous land-cover classes and by the high diversity of urban patterns which can be encountered throughout the scene. In this context, using a single segmentation parameter to obtain satisfying segmentation results for the whole scene can be impossible. Nonetheless, it is possible to subdivide the whole city into smaller local zones, rather homogeneous according to their urban pattern. These zones can then be used to optimize the segmentation parameter locally, instead of using the whole image or a single representative spatial subset. This paper assesses the contribution of a local approach for the optimization of segmentation parameter compared to a global approach. Ouagadougou, located in sub-Saharan Africa, is used as case studies. First, the whole scene is segmented using a single globally optimized segmentation parameter. Second, the city is subdivided into 283 local zones, homogeneous in terms of building size and building density. Each local zone is then segmented using a locally optimized segmentation parameter. Unsupervised segmentation parameter optimization (USPO), relying on an optimization function which tends to maximize both intra-object homogeneity and inter-object heterogeneity, is used to select the segmentation parameter automatically for both approaches. Finally, a land-use/land-cover classification is performed using the Random Forest (RF) classifier. The results reveal that the local approach outperforms the global one, especially by limiting confusions between buildings and their bare-soil neighbors.

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.

Segmentation of blurred objects using wavelet transform: application to x-ray images

NASA Astrophysics Data System (ADS)

Barat, Cecile S.; Ducottet, Christophe; Bilgot, Anne; Desbat, Laurent

2004-02-01

First, we present a wavelet-based algorithm for edge detection and characterization, which is an adaptation of Mallat and Hwang"s method. This algorithm relies on a modelization of contours as smoothed singularities of three particular types (transitions, peaks and lines). On the one hand, it allows to detect and locate edges at an adapted scale. On the other hand, it is able to identify the type of each detected edge point and to measure its amplitude and smoothing size. The latter parameters represent respectively the contrast and the smoothness level of the edge point. Second, we explain that this method has been integrated in a 3D bone surface reconstruction algorithm designed for computer-assisted and minimal invasive orthopaedic surgery. In order to decrease the dose to the patient and to obtain rapidly a 3D image, we propose to identify a bone shape from few X-ray projections by using statistical shape models registered to segmented X-ray projections. We apply this approach to pedicle screw insertion (scoliosis, fractures...) where ten to forty percent of the screws are known to be misplaced. In this context, the proposed edge detection algorithm allows to overcome the major problem of vertebrae segmentation in the X-ray images.

Human recognition based on head-shoulder contour extraction and BP neural network

NASA Astrophysics Data System (ADS)

Kong, Xiao-fang; Wang, Xiu-qin; Gu, Guohua; Chen, Qian; Qian, Wei-xian

2014-11-01

In practical application scenarios like video surveillance and human-computer interaction, human body movements are uncertain because the human body is a non-rigid object. Based on the fact that the head-shoulder part of human body can be less affected by the movement, and will seldom be obscured by other objects, in human detection and recognition, a head-shoulder model with its stable characteristics can be applied as a detection feature to describe the human body. In order to extract the head-shoulder contour accurately, a head-shoulder model establish method with combination of edge detection and the mean-shift algorithm in image clustering has been proposed in this paper. First, an adaptive method of mixture Gaussian background update has been used to extract targets from the video sequence. Second, edge detection has been used to extract the contour of moving objects, and the mean-shift algorithm has been combined to cluster parts of target's contour. Third, the head-shoulder model can be established, according to the width and height ratio of human head-shoulder combined with the projection histogram of the binary image, and the eigenvectors of the head-shoulder contour can be acquired. Finally, the relationship between head-shoulder contour eigenvectors and the moving objects will be formed by the training of back-propagation (BP) neural network classifier, and the human head-shoulder model can be clustered for human detection and recognition. Experiments have shown that the method combined with edge detection and mean-shift algorithm proposed in this paper can extract the complete head-shoulder contour, with low calculating complexity and high efficiency.

Lossy to lossless object-based coding of 3-D MRI data.

PubMed

Menegaz, Gloria; Thiran, Jean-Philippe

2002-01-01

We propose a fully three-dimensional (3-D) object-based coding system exploiting the diagnostic relevance of the different regions of the volumetric data for rate allocation. The data are first decorrelated via a 3-D discrete wavelet transform. The implementation via the lifting steps scheme allows to map integer-to-integer values, enabling lossless coding, and facilitates the definition of the object-based inverse transform. The coding process assigns disjoint segments of the bitstream to the different objects, which can be independently accessed and reconstructed at any up-to-lossless quality. Two fully 3-D coding strategies are considered: embedded zerotree coding (EZW-3D) and multidimensional layered zero coding (MLZC), both generalized for region of interest (ROI)-based processing. In order to avoid artifacts along region boundaries, some extra coefficients must be encoded for each object. This gives rise to an overheading of the bitstream with respect to the case where the volume is encoded as a whole. The amount of such extra information depends on both the filter length and the decomposition depth. The system is characterized on a set of head magnetic resonance images. Results show that MLZC and EZW-3D have competitive performances. In particular, the best MLZC mode outperforms the others state-of-the-art techniques on one of the datasets for which results are available in the literature.

Localized Segment Based Processing for Automatic Building Extraction from LiDAR Data

NASA Astrophysics Data System (ADS)

Parida, G.; Rajan, K. S.

2017-05-01

The current methods of object segmentation and extraction and classification of aerial LiDAR data is manual and tedious task. This work proposes a technique for object segmentation out of LiDAR data. A bottom-up geometric rule based approach was used initially to devise a way to segment buildings out of the LiDAR datasets. For curved wall surfaces, comparison of localized surface normals was done to segment buildings. The algorithm has been applied to both synthetic datasets as well as real world dataset of Vaihingen, Germany. Preliminary results show successful segmentation of the buildings objects from a given scene in case of synthetic datasets and promissory results in case of real world data. The advantages of the proposed work is non-dependence on any other form of data required except LiDAR. It is an unsupervised method of building segmentation, thus requires no model training as seen in supervised techniques. It focuses on extracting the walls of the buildings to construct the footprint, rather than focussing on roof. The focus on extracting the wall to reconstruct the buildings from a LiDAR scene is crux of the method proposed. The current segmentation approach can be used to get 2D footprints of the buildings, with further scope to generate 3D models. Thus, the proposed method can be used as a tool to get footprints of buildings in urban landscapes, helping in urban planning and the smart cities endeavour.

Towards Automatic Image Segmentation Using Optimised Region Growing Technique

NASA Astrophysics Data System (ADS)

Alazab, Mamoun; Islam, Mofakharul; Venkatraman, Sitalakshmi

Image analysis is being adopted extensively in many applications such as digital forensics, medical treatment, industrial inspection, etc. primarily for diagnostic purposes. Hence, there is a growing interest among researches in developing new segmentation techniques to aid the diagnosis process. Manual segmentation of images is labour intensive, extremely time consuming and prone to human errors and hence an automated real-time technique is warranted in such applications. There is no universally applicable automated segmentation technique that will work for all images as the image segmentation is quite complex and unique depending upon the domain application. Hence, to fill the gap, this paper presents an efficient segmentation algorithm that can segment a digital image of interest into a more meaningful arrangement of regions and objects. Our algorithm combines region growing approach with optimised elimination of false boundaries to arrive at more meaningful segments automatically. We demonstrate this using X-ray teeth images that were taken for real-life dental diagnosis.

Segmentation of whole cells and cell nuclei from 3-D optical microscope images using dynamic programming.

PubMed

McCullough, D P; Gudla, P R; Harris, B S; Collins, J A; Meaburn, K J; Nakaya, M A; Yamaguchi, T P; Misteli, T; Lockett, S J

2008-05-01

Communications between cells in large part drive tissue development and function, as well as disease-related processes such as tumorigenesis. Understanding the mechanistic bases of these processes necessitates quantifying specific molecules in adjacent cells or cell nuclei of intact tissue. However, a major restriction on such analyses is the lack of an efficient method that correctly segments each object (cell or nucleus) from 3-D images of an intact tissue specimen. We report a highly reliable and accurate semi-automatic algorithmic method for segmenting fluorescence-labeled cells or nuclei from 3-D tissue images. Segmentation begins with semi-automatic, 2-D object delineation in a user-selected plane, using dynamic programming (DP) to locate the border with an accumulated intensity per unit length greater that any other possible border around the same object. Then the two surfaces of the object in planes above and below the selected plane are found using an algorithm that combines DP and combinatorial searching. Following segmentation, any perceived errors can be interactively corrected. Segmentation accuracy is not significantly affected by intermittent labeling of object surfaces, diffuse surfaces, or spurious signals away from surfaces. The unique strength of the segmentation method was demonstrated on a variety of biological tissue samples where all cells, including irregularly shaped cells, were accurately segmented based on visual inspection.

Integrated segmentation of cellular structures

NASA Astrophysics Data System (ADS)

Ajemba, Peter; Al-Kofahi, Yousef; Scott, Richard; Donovan, Michael; Fernandez, Gerardo

2011-03-01

Automatic segmentation of cellular structures is an essential step in image cytology and histology. Despite substantial progress, better automation and improvements in accuracy and adaptability to novel applications are needed. In applications utilizing multi-channel immuno-fluorescence images, challenges include misclassification of epithelial and stromal nuclei, irregular nuclei and cytoplasm boundaries, and over and under-segmentation of clustered nuclei. Variations in image acquisition conditions and artifacts from nuclei and cytoplasm images often confound existing algorithms in practice. In this paper, we present a robust and accurate algorithm for jointly segmenting cell nuclei and cytoplasm using a combination of ideas to reduce the aforementioned problems. First, an adaptive process that includes top-hat filtering, Eigenvalues-of-Hessian blob detection and distance transforms is used to estimate the inverse illumination field and correct for intensity non-uniformity in the nuclei channel. Next, a minimum-error-thresholding based binarization process and seed-detection combining Laplacian-of-Gaussian filtering constrained by a distance-map-based scale selection is used to identify candidate seeds for nuclei segmentation. The initial segmentation using a local maximum clustering algorithm is refined using a minimum-error-thresholding technique. Final refinements include an artifact removal process specifically targeted at lumens and other problematic structures and a systemic decision process to reclassify nuclei objects near the cytoplasm boundary as epithelial or stromal. Segmentation results were evaluated using 48 realistic phantom images with known ground-truth. The overall segmentation accuracy exceeds 94%. The algorithm was further tested on 981 images of actual prostate cancer tissue. The artifact removal process worked in 90% of cases. The algorithm has now been deployed in a high-volume histology analysis application.

Selecting salient frames for spatiotemporal video modeling and segmentation.

PubMed

Song, Xiaomu; Fan, Guoliang

2007-12-01

We propose a new statistical generative model for spatiotemporal video segmentation. The objective is to partition a video sequence into homogeneous segments that can be used as "building blocks" for semantic video segmentation. The baseline framework is a Gaussian mixture model (GMM)-based video modeling approach that involves a six-dimensional spatiotemporal feature space. Specifically, we introduce the concept of frame saliency to quantify the relevancy of a video frame to the GMM-based spatiotemporal video modeling. This helps us use a small set of salient frames to facilitate the model training by reducing data redundancy and irrelevance. A modified expectation maximization algorithm is developed for simultaneous GMM training and frame saliency estimation, and the frames with the highest saliency values are extracted to refine the GMM estimation for video segmentation. Moreover, it is interesting to find that frame saliency can imply some object behaviors. This makes the proposed method also applicable to other frame-related video analysis tasks, such as key-frame extraction, video skimming, etc. Experiments on real videos demonstrate the effectiveness and efficiency of the proposed method.

Segmentation in reading and film comprehension.

PubMed

Zacks, Jeffrey M; Speer, Nicole K; Reynolds, Jeremy R

2009-05-01

When reading a story or watching a film, comprehenders construct a series of representations in order to understand the events depicted. Discourse comprehension theories and a recent theory of perceptual event segmentation both suggest that comprehenders monitor situational features such as characters' goals, to update these representations at natural boundaries in activity. However, the converging predictions of these theories had previously not been tested directly. Two studies provided evidence that changes in situational features such as characters, their locations, their interactions with objects, and their goals are related to the segmentation of events in both narrative texts and films. A 3rd study indicated that clauses with event boundaries are read more slowly than are other clauses and that changes in situational features partially mediate this relation. A final study suggested that the predictability of incoming information influences reading rate and possibly event segmentation. Taken together, these results suggest that processing situational changes during comprehension is an important determinant of how one segments ongoing activity into events and that this segmentation is related to the control of processing during reading. (c) 2009 APA, all rights reserved.

Head or tail: the orientation of the small bowel capsule endoscope movement in the small bowel.

PubMed

Kopylov, Uri; Papageorgiou, Neofytos P; Nadler, Moshe; Eliakim, Rami; Ben-Horin, Shomron

2012-03-01

The diagnostic accuracy of capsule endoscopy has been suggested to be influenced by the direction of the passage in the intestine. It is currently unknown if a head-first or a tail-first orientation are equally common during the descent through the small bowel. The aim of the study was to identify the orientation of the capsule along the migration through the small bowel. Thirty capsule endoscopies were reviewed by an experienced observer. The direction of the passage through the pylorus and the ileoceccal valve was recorded for all the examinations. In addition, detailed review of the passage of the capsule in different segments of the small bowel was undertaken for all the capsules. The capsule was significantly more likely to pass the pylorus head-first compared to tail-first (25 and 5 out of 30, respectively, OR 5, 95% CI 65-94%, P < 0.001). In 28/30 studies, the capsule exited the ileoceccal valve head-first (OR-14, 95% CI 77-99%, P < 0.001). In an immersion experiment, uneven distribution of weight of the capsule body was demonstrated with the head part (camera tip) being lighter than the tail part. The capsule endoscope usually passes through the pylorus and subsequent segments of the small bowel head-first. This observation suggests that the intestinal peristaltic physiology drives symmetrical bodies with their light part first. The principle of intestinal orientation by weight distribution may bear implications for capsules' design in the future.

MO-F-CAMPUS-J-01: Effect of Iodine Contrast Agent Concentration On Cerebrovascular Dose for Synchrotron Radiation Microangiography Based On a Simple Mouse Head Model and a Voxel Mouse Head Phantom

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

Lin, H; Jing, J; Xie, C

Purpose: To find effective setting methods to mitigate the irradiation injure in synchrotron radiation microangiography(SRA) by Monte Carlo simulation. Methods: A mouse 1-D head model and a segmented voxel mouse head phantom were simulated by EGSnrc/Dosxyznrc code to investigate the dose enhancement effect of the iodine contrast agent irradiated by a monochromatic synchrotron radiation(SR) source. The influence of, like iodine concentration (IC), vessel width and depth, with and without skull layer protection and the various incident X ray energies, were simulated. The dose enhancement effect and the absolute dose based on the segmented voxel mouse head phantom were evaluated. Results:more » The dose enhancement ratio depends little on the irradiation depth, but strongly on the IC, which is linearly increases with IC. The skull layer protection cannot be ignored in SRA, the 700µm thick skull could decrease 10% of the dose. The incident X-ray energy can significantly affact the dose. E.g. compared to the dose of 33.2keV for 50mgI/ml, the 32.7keV dose decreases 38%, whereas the dose of 33.7 keV increases 69.2%, and the variation will strengthen more with enhanced IC. The segmented voxel mouse head phantom also showed that the average dose enhancement effect and the maximal voxel dose per photon depends little on the iodine voxel volume ratio, but strongly on IC. Conclusion: To decrease dose damage in SRA, the high-Z contrast agent should be used as little as possible, and try to avoid radiating locally the injected position immediately after the contrast agent injection. The fragile vessel containing iodine should avoid closely irradiating. Avoiding irradiating through the no or thin skull region, or appending thin equivalent material from outside to protect is also a better method. As long as SRA image quality is ensured, using incident X-ray energy as low as possible.« less

Multi-camera sensor system for 3D segmentation and localization of multiple mobile robots.

PubMed

Losada, Cristina; Mazo, Manuel; Palazuelos, Sira; Pizarro, Daniel; Marrón, Marta

2010-01-01

This paper presents a method for obtaining the motion segmentation and 3D localization of multiple mobile robots in an intelligent space using a multi-camera sensor system. The set of calibrated and synchronized cameras are placed in fixed positions within the environment (intelligent space). The proposed algorithm for motion segmentation and 3D localization is based on the minimization of an objective function. This function includes information from all the cameras, and it does not rely on previous knowledge or invasive landmarks on board the robots. The proposed objective function depends on three groups of variables: the segmentation boundaries, the motion parameters and the depth. For the objective function minimization, we use a greedy iterative algorithm with three steps that, after initialization of segmentation boundaries and depth, are repeated until convergence.

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.

[The clinical impact of artery-first approach combined with vascular resection and reconstruction in the treatment of pancreatic head carcinoma].

PubMed

Huang, J L; Li, W G; Chen, F Z; Su, Z J; Li, F M; Liu, B

2017-03-23

Objective: To evaluate the application of artery first, combined vascular resection and reconstruction in the treatment of pancreatic head carcinoma. Methods: The clinical data of 13 patients with pancreatic head cancer were retrospectively analyzed from February 2014 to March 2016 in the Affiliated Hospital of Xiamen University. Preoperative computed tomography of high resolution layer or magnetic resonance imaging examination demonstrated pancreatic head carcinoma, as well as close adhesion, stenosis, compression or displacement of superior mesenteric vein or portal vein wall. In the operation, the artery first approach was used and the whole arterial blood supply in the head of the pancreas was fully exposed and interdicted. Finally, en block resection and vascular resection and reconstruction was adopted. Results: 12 of 13 patients had pancreatoduodenectomy synchronously with vascular resection and reconstruction; the other patient had these two surgery sequentially. Four patients received blood vessel wedge resection, five had segmental resection combined with end to end suture, and four had segmental resection combined with artificial vascular graft reconstruction. Operation time was (327.2±65.5) minutes, and the amount of blood loss was (472.6±226.4) millilitres. One patient suffered from delayed gastric emptying, and two patients had pancreatic fistula. All patients recovered from postoperative complications by conservative treatment. No patients developed biliary fistula, gastrointestinal fistula, abdominal infection, pulmonary infection, diarrhea, hypoglycemia or other complications, and none died in perioperative period. Postoperative pathological findings confirmed the diagnosis of pancreatic ductal adenocarcinoma. Mean tumor diameter was (4.2±1.5)cm, and (3.8±1.5) metastasis were found in (13.6±2.5) resected lymph nodes. In 11 cases, the tumor cells were found in the outer membrane of blood vessels, 2 cases were found to have tumor invasion in the

Head Start of North Dakota, 1997-1998.

ERIC Educational Resources Information Center

North Dakota Dept. of Human Services, Bismark. Div. of Children and Family Services.

The Head Start program, a comprehensive child development program designed to increase the social competence of children in low-income families and children with disabilities and to improve their chances of school success, has been in North Dakota since 1965. This report describes the objectives of the Head Start program, the North Dakota Head…

Head Start of North Dakota, 1998-99.

ERIC Educational Resources Information Center

North Dakota Dept. of Human Services, Bismark. Div. of Children and Family Services.

The Head Start program, a comprehensive child development program designed to increase the social competence of children in low-income families and children with disabilities and to improve their chances of school success, has been in North Dakota since 1965. This report describes the objectives of the Head Start program, the North Dakota Head…

Automated Tumor Volumetry Using Computer-Aided Image Segmentation

PubMed Central

Bilello, Michel; Sadaghiani, Mohammed Salehi; Akbari, Hamed; Atthiah, Mark A.; Ali, Zarina S.; Da, Xiao; Zhan, Yiqang; O'Rourke, Donald; Grady, Sean M.; Davatzikos, Christos

2015-01-01

Rationale and Objectives Accurate segmentation of brain tumors, and quantification of tumor volume, is important for diagnosis, monitoring, and planning therapeutic intervention. Manual segmentation is not widely used because of time constraints. Previous efforts have mainly produced methods that are tailored to a particular type of tumor or acquisition protocol and have mostly failed to produce a method that functions on different tumor types and is robust to changes in scanning parameters, resolution, and image quality, thereby limiting their clinical value. Herein, we present a semiautomatic method for tumor segmentation that is fast, accurate, and robust to a wide variation in image quality and resolution. Materials and Methods A semiautomatic segmentation method based on the geodesic distance transform was developed and validated by using it to segment 54 brain tumors. Glioblastomas, meningiomas, and brain metastases were segmented. Qualitative validation was based on physician ratings provided by three clinical experts. Quantitative validation was based on comparing semiautomatic and manual segmentations. Results Tumor segmentations obtained using manual and automatic methods were compared quantitatively using the Dice measure of overlap. Subjective evaluation was performed by having human experts rate the computerized segmentations on a 0–5 rating scale where 5 indicated perfect segmentation. Conclusions The proposed method addresses a significant, unmet need in the field of neuro-oncology. Specifically, this method enables clinicians to obtain accurate and reproducible tumor volumes without the need for manual segmentation. PMID:25770633

SpArcFiRe: Scalable automated detection of spiral galaxy arm segments

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

Davis, Darren R.; Hayes, Wayne B., E-mail: drdavis@uci.edu, E-mail: whayes@uci.edu

Given an approximately centered image of a spiral galaxy, we describe an entirely automated method that finds, centers, and sizes the galaxy (possibly masking nearby stars and other objects if necessary in order to isolate the galaxy itself) and then automatically extracts structural information about the spiral arms. For each arm segment found, we list the pixels in that segment, allowing image analysis on a per-arm-segment basis. We also perform a least-squares fit of a logarithmic spiral arc to the pixels in that segment, giving per-arc parameters, such as the pitch angle, arm segment length, location, etc. The algorithm takesmore » about one minute per galaxies, and can easily be scaled using parallelism. We have run it on all ∼644,000 Sloan objects that are larger than 40 pixels across and classified as 'galaxies'. We find a very good correlation between our quantitative description of a spiral structure and the qualitative description provided by Galaxy Zoo humans. Our objective, quantitative measures of structure demonstrate the difficulty in defining exactly what constitutes a spiral 'arm', leading us to prefer the term 'arm segment'. We find that pitch angle often varies significantly segment-to-segment in a single spiral galaxy, making it difficult to define the pitch angle for a single galaxy. We demonstrate how our new database of arm segments can be queried to find galaxies satisfying specific quantitative visual criteria. For example, even though our code does not explicitly find rings, a good surrogate is to look for galaxies having one long, low-pitch-angle arm—which is how our code views ring galaxies. SpArcFiRe is available at http://sparcfire.ics.uci.edu.« less

Segmentation of touching mycobacterium tuberculosis from Ziehl-Neelsen stained sputum smear images

NASA Astrophysics Data System (ADS)

Xu, Chao; Zhou, Dongxiang; Liu, Yunhui

2015-12-01

Touching Mycobacterium tuberculosis objects in the Ziehl-Neelsen stained sputum smear images present different shapes and invisible boundaries in the adhesion areas, which increases the difficulty in objects recognition and counting. In this paper, we present a segmentation method of combining the hierarchy tree analysis with gradient vector flow snake to address this problem. The skeletons of the objects are used for structure analysis based on the hierarchy tree. The gradient vector flow snake is used to estimate the object edge. Experimental results show that the single objects composing the touching objects are successfully segmented by the proposed method. This work will improve the accuracy and practicability of the computer-aided diagnosis of tuberculosis.

Optimal Multiple Surface Segmentation With Shape and Context Priors

PubMed Central

Bai, Junjie; Garvin, Mona K.; Sonka, Milan; Buatti, John M.; Wu, Xiaodong

2014-01-01

Segmentation of multiple surfaces in medical images is a challenging problem, further complicated by the frequent presence of weak boundary evidence, large object deformations, and mutual influence between adjacent objects. This paper reports a novel approach to multi-object segmentation that incorporates both shape and context prior knowledge in a 3-D graph-theoretic framework to help overcome the stated challenges. We employ an arc-based graph representation to incorporate a wide spectrum of prior information through pair-wise energy terms. In particular, a shape-prior term is used to penalize local shape changes and a context-prior term is used to penalize local surface-distance changes from a model of the expected shape and surface distances, respectively. The globally optimal solution for multiple surfaces is obtained by computing a maximum flow in a low-order polynomial time. The proposed method was validated on intraretinal layer segmentation of optical coherence tomography images and demonstrated statistically significant improvement of segmentation accuracy compared to our earlier graph-search method that was not utilizing shape and context priors. The mean unsigned surface positioning errors obtained by the conventional graph-search approach (6.30 ± 1.58 μm) was improved to 5.14 ± 0.99 μm when employing our new method with shape and context priors. PMID:23193309

Head Motion Modeling for Human Behavior Analysis in Dyadic Interaction

PubMed Central

Xiao, Bo; Georgiou, Panayiotis; Baucom, Brian; Narayanan, Shrikanth S.

2015-01-01

This paper presents a computational study of head motion in human interaction, notably of its role in conveying interlocutors’ behavioral characteristics. Head motion is physically complex and carries rich information; current modeling approaches based on visual signals, however, are still limited in their ability to adequately capture these important properties. Guided by the methodology of kinesics, we propose a data driven approach to identify typical head motion patterns. The approach follows the steps of first segmenting motion events, then parametrically representing the motion by linear predictive features, and finally generalizing the motion types using Gaussian mixture models. The proposed approach is experimentally validated using video recordings of communication sessions from real couples involved in a couples therapy study. In particular we use the head motion model to classify binarized expert judgments of the interactants’ specific behavioral characteristics where entrainment in head motion is hypothesized to play a role: Acceptance, Blame, Positive, and Negative behavior. We achieve accuracies in the range of 60% to 70% for the various experimental settings and conditions. In addition, we describe a measure of motion similarity between the interaction partners based on the proposed model. We show that the relative change of head motion similarity during the interaction significantly correlates with the expert judgments of the interactants’ behavioral characteristics. These findings demonstrate the effectiveness of the proposed head motion model, and underscore the promise of analyzing human behavioral characteristics through signal processing methods. PMID:26557047

Effects of Head Rotation and Head Tilt on Pharyngeal Pressure Events Using High Resolution Manometry

PubMed Central

Kim, Cheol Ki; Song, Sun Hong; Koo, Jung Hoi; Lee, Kyung Duck; Park, Hee Sun; Oh, Yoongul; Min, Kyunghoon

2015-01-01

Objective To observe changes in pharyngeal pressure during the swallowing process according to postures in normal individuals using high-resolution manometry (HRM). Methods Ten healthy volunteers drank 5 mL of water twice while sitting in a neutral posture. Thereafter, they drank the same amount of water twice in the head rotation and head tilting postures. The pressure and time during the deglutition process for each posture were measured with HRM. The data obtained for these two postures were compared with those obtained from the neutral posture. Results The maximum pressure, area, rise time, and duration in velopharynx (VP) and tongue base (TB) were not affected by changes in posture. In comparison, the maximum pressure and the pre-upper esophageal sphincter (UES) maximum pressure of the lower pharynx in the counter-catheter head rotation posture were lower than those in the neutral posture. The lower pharynx pressure in the catheter head tilting posture was higher than that in the counter-catheter head tilting. The changes in the VP peak and epiglottis, VP and TB peaks, and the VP onset and post-UES time intervals were significant in head tilting and head rotation toward the catheter postures, as compared with neutral posture. Conclusion The pharyngeal pressure and time parameter analysis using HRM determined the availability of head rotation as a compensatory technique for safe swallowing. Tilting the head smoothes the progress of food by increasing the pressure in the pharynx. PMID:26161349

Severe head lice infestation in an Andean mummy of Arica, Chile.

PubMed

Arriaza, Bernardo; Orellana, Nancy C; Barbosa, Helene S; Menna-Barreto, Rubem F S; Araújo, Adauto; Standen, Vivien

2012-04-01

Pediculus humanus capitis is an ancient human parasite, probably inherited from pre-hominid times. Infestation appears as a recurrent health problem throughout history, including in pre-Columbian populations. Here, we describe and discuss the occurrence of pre-Columbian pediculosis in the Andean region of the Atacama Desert. Using a light microscope and scanning electron microscopy, we studied a highly infested Maitas Chiribaya mummy from Arica in northern Chile dating to 670-990 calibrated years A.D. The scalp and hair of the mummy were almost completely covered by nits and adult head lice. Low- and high-vacuum scanning electron microscopy revealed a well-preserved morphology of the eggs. In addition, the excellent preservation of the nearly 1,000-yr-old adult head lice allowed us to observe and characterize the head, antennae, thorax, abdomen, and legs. Leg segmentation, abdominal spiracles, and sexual dimorphism also were clearly observed. The preservation of the ectoparasites allowed us to examine the micromorphology using scanning electron microscopy; the opercula, aeropyles, and spiracles were clearly visible. This case study provides strong evidence that head lice were a common nuisance for Andean farmers and herders. Head lice are transmitted by direct head-to-head contact; thus, this ancient farmer and herder was potentially infesting other people. The present study contributes to the body of research focusing on lice in ancient populations.

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.

The Analysis of Image Segmentation Hierarchies with a Graph-based Knowledge Discovery System

NASA Technical Reports Server (NTRS)

Tilton, James C.; Cooke, diane J.; Ketkar, Nikhil; Aksoy, Selim

2008-01-01

Currently available pixel-based analysis techniques do not effectively extract the information content from the increasingly available high spatial resolution remotely sensed imagery data. A general consensus is that object-based image analysis (OBIA) is required to effectively analyze this type of data. OBIA is usually a two-stage process; image segmentation followed by an analysis of the segmented objects. We are exploring an approach to OBIA in which hierarchical image segmentations provided by the Recursive Hierarchical Segmentation (RHSEG) software developed at NASA GSFC are analyzed by the Subdue graph-based knowledge discovery system developed by a team at Washington State University. In this paper we discuss out initial approach to representing the RHSEG-produced hierarchical image segmentations in a graphical form understandable by Subdue, and provide results on real and simulated data. We also discuss planned improvements designed to more effectively and completely convey the hierarchical segmentation information to Subdue and to improve processing efficiency.

GPU accelerated fuzzy connected image segmentation by using CUDA.

PubMed

Zhuge, Ying; Cao, Yong; Miller, Robert W

2009-01-01

Image segmentation techniques using fuzzy connectedness principles have shown their effectiveness in segmenting a variety of objects in several large applications in recent years. However, one problem of these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays commodity graphics hardware provides high parallel computing power. In this paper, we present a parallel fuzzy connected image segmentation algorithm on Nvidia's Compute Unified Device Architecture (CUDA) platform for segmenting large medical image data sets. Our experiments based on three data sets with small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 7.2x, 7.3x, and 14.4x, correspondingly, for the three data sets over the sequential implementation of fuzzy connected image segmentation algorithm on CPU.

A patient-specific segmentation framework for longitudinal MR images of traumatic brain injury

NASA Astrophysics Data System (ADS)

Wang, Bo; Prastawa, Marcel; Irimia, Andrei; Chambers, Micah C.; Vespa, Paul M.; Van Horn, John D.; Gerig, Guido

2012-02-01

Traumatic brain injury (TBI) is a major cause of death and disability worldwide. Robust, reproducible segmentations of MR images with TBI are crucial for quantitative analysis of recovery and treatment efficacy. However, this is a significant challenge due to severe anatomy changes caused by edema (swelling), bleeding, tissue deformation, skull fracture, and other effects related to head injury. In this paper, we introduce a multi-modal image segmentation framework for longitudinal TBI images. The framework is initialized through manual input of primary lesion sites at each time point, which are then refined by a joint approach composed of Bayesian segmentation and construction of a personalized atlas. The personalized atlas construction estimates the average of the posteriors of the Bayesian segmentation at each time point and warps the average back to each time point to provide the updated priors for Bayesian segmentation. The difference between our approach and segmenting longitudinal images independently is that we use the information from all time points to improve the segmentations. Given a manual initialization, our framework automatically segments healthy structures (white matter, grey matter, cerebrospinal fluid) as well as different lesions such as hemorrhagic lesions and edema. Our framework can handle different sets of modalities at each time point, which provides flexibility in analyzing clinical scans. We show results on three subjects with acute baseline scans and chronic follow-up scans. The results demonstrate that joint analysis of all the points yields improved segmentation compared to independent analysis of the two time points.