MO-DE-207-04: Imaging educational program on solutions to common pediatric imaging challenges

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

Krishnamurthy, R.

This imaging educational program will focus on solutions to common pediatric imaging challenges. The speakers will present collective knowledge on best practices in pediatric imaging from their experience at dedicated children’s hospitals. The educational program will begin with a detailed discussion of the optimal configuration of fluoroscopes for general pediatric procedures. Following this introduction will be a focused discussion on the utility of Dual Energy CT for imaging children. The third lecture will address the substantial challenge of obtaining consistent image post -processing in pediatric digital radiography. The fourth and final lecture will address best practices in pediatric MRI includingmore » a discussion of ancillary methods to reduce sedation and anesthesia rates. Learning Objectives: To learn techniques for optimizing radiation dose and image quality in pediatric fluoroscopy To become familiar with the unique challenges and applications of Dual Energy CT in pediatric imaging To learn solutions for consistent post-processing quality in pediatric digital radiography To understand the key components of an effective MRI safety and quality program for the pediatric practice.« less

Recent Developments in Vascular Imaging Techniques in Tissue Engineering and Regenerative Medicine

PubMed Central

Upputuri, Paul Kumar; Sivasubramanian, Kathyayini; Mark, Chong Seow Khoon; Pramanik, Manojit

2015-01-01

Adequate vascularisation is key in determining the clinical outcome of stem cells and engineered tissue in regenerative medicine. Numerous imaging modalities have been developed and used for the visualization of vascularisation in tissue engineering. In this review, we briefly discuss the very recent advances aiming at high performance imaging of vasculature. We classify the vascular imaging modalities into three major groups: nonoptical methods (X-ray, magnetic resonance, ultrasound, and positron emission imaging), optical methods (optical coherence, fluorescence, multiphoton, and laser speckle imaging), and hybrid methods (photoacoustic imaging). We then summarize the strengths and challenges of these methods for preclinical and clinical applications. PMID:25821821

Integration of Sparse Multi-modality Representation and Geometrical Constraint for Isointense Infant Brain Segmentation

PubMed Central

Wang, Li; Shi, Feng; Li, Gang; Lin, Weili; Gilmore, John H.; Shen, Dinggang

2014-01-01

Segmentation of infant brain MR images is challenging due to insufficient image quality, severe partial volume effect, and ongoing maturation and myelination process. During the first year of life, the signal contrast between white matter (WM) and gray matter (GM) in MR images undergoes inverse changes. In particular, the inversion of WM/GM signal contrast appears around 6–8 months of age, where brain tissues appear isointense and hence exhibit extremely low tissue contrast, posing significant challenges for automated segmentation. In this paper, we propose a novel segmentation method to address the above-mentioned challenge based on the sparse representation of the complementary tissue distribution information from T1, T2 and diffusion-weighted images. Specifically, we first derive an initial segmentation from a library of aligned multi-modality images with ground-truth segmentations by using sparse representation in a patch-based fashion. The segmentation is further refined by the integration of the geometrical constraint information. The proposed method was evaluated on 22 6-month-old training subjects using leave-one-out cross-validation, as well as 10 additional infant testing subjects, showing superior results in comparison to other state-of-the-art methods. PMID:24505729

Integration of sparse multi-modality representation and geometrical constraint for isointense infant brain segmentation.

PubMed

Wang, Li; Shi, Feng; Li, Gang; Lin, Weili; Gilmore, John H; Shen, Dinggang

2013-01-01

Segmentation of infant brain MR images is challenging due to insufficient image quality, severe partial volume effect, and ongoing maturation and myelination process. During the first year of life, the signal contrast between white matter (WM) and gray matter (GM) in MR images undergoes inverse changes. In particular, the inversion of WM/GM signal contrast appears around 6-8 months of age, where brain tissues appear isointense and hence exhibit extremely low tissue contrast, posing significant challenges for automated segmentation. In this paper, we propose a novel segmentation method to address the above-mentioned challenge based on the sparse representation of the complementary tissue distribution information from T1, T2 and diffusion-weighted images. Specifically, we first derive an initial segmentation from a library of aligned multi-modality images with ground-truth segmentations by using sparse representation in a patch-based fashion. The segmentation is further refined by the integration of the geometrical constraint information. The proposed method was evaluated on 22 6-month-old training subjects using leave-one-out cross-validation, as well as 10 additional infant testing subjects, showing superior results in comparison to other state-of-the-art methods.

MRI Segmentation of the Human Brain: Challenges, Methods, and Applications

PubMed Central

Despotović, Ivana

2015-01-01

Image segmentation is one of the most important tasks in medical image analysis and is often the first and the most critical step in many clinical applications. In brain MRI analysis, image segmentation is commonly used for measuring and visualizing the brain's anatomical structures, for analyzing brain changes, for delineating pathological regions, and for surgical planning and image-guided interventions. In the last few decades, various segmentation techniques of different accuracy and degree of complexity have been developed and reported in the literature. In this paper we review the most popular methods commonly used for brain MRI segmentation. We highlight differences between them and discuss their capabilities, advantages, and limitations. To address the complexity and challenges of the brain MRI segmentation problem, we first introduce the basic concepts of image segmentation. Then, we explain different MRI preprocessing steps including image registration, bias field correction, and removal of nonbrain tissue. Finally, after reviewing different brain MRI segmentation methods, we discuss the validation problem in brain MRI segmentation. PMID:25945121

An object tracking method based on guided filter for night fusion image

NASA Astrophysics Data System (ADS)

Qian, Xiaoyan; Wang, Yuedong; Han, Lei

2016-01-01

Online object tracking is a challenging problem as it entails learning an effective model to account for appearance change caused by intrinsic and extrinsic factors. In this paper, we propose a novel online object tracking with guided image filter for accurate and robust night fusion image tracking. Firstly, frame difference is applied to produce the coarse target, which helps to generate observation models. Under the restriction of these models and local source image, guided filter generates sufficient and accurate foreground target. Then accurate boundaries of the target can be extracted from detection results. Finally timely updating for observation models help to avoid tracking shift. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-art methods.

Machine Learning Approaches in Cardiovascular Imaging.

PubMed

Henglin, Mir; Stein, Gillian; Hushcha, Pavel V; Snoek, Jasper; Wiltschko, Alexander B; Cheng, Susan

2017-10-01

Cardiovascular imaging technologies continue to increase in their capacity to capture and store large quantities of data. Modern computational methods, developed in the field of machine learning, offer new approaches to leveraging the growing volume of imaging data available for analyses. Machine learning methods can now address data-related problems ranging from simple analytic queries of existing measurement data to the more complex challenges involved in analyzing raw images. To date, machine learning has been used in 2 broad and highly interconnected areas: automation of tasks that might otherwise be performed by a human and generation of clinically important new knowledge. Most cardiovascular imaging studies have focused on task-oriented problems, but more studies involving algorithms aimed at generating new clinical insights are emerging. Continued expansion in the size and dimensionality of cardiovascular imaging databases is driving strong interest in applying powerful deep learning methods, in particular, to analyze these data. Overall, the most effective approaches will require an investment in the resources needed to appropriately prepare such large data sets for analyses. Notwithstanding current technical and logistical challenges, machine learning and especially deep learning methods have much to offer and will substantially impact the future practice and science of cardiovascular imaging. © 2017 American Heart Association, Inc.

Building cell models and simulations from microscope images.

PubMed

Murphy, Robert F

2016-03-01

The use of fluorescence microscopy has undergone a major revolution over the past twenty years, both with the development of dramatic new technologies and with the widespread adoption of image analysis and machine learning methods. Many open source software tools provide the ability to use these methods in a wide range of studies, and many molecular and cellular phenotypes can now be automatically distinguished. This article presents the next major challenge in microscopy automation, the creation of accurate models of cell organization directly from images, and reviews the progress that has been made towards this challenge. Copyright © 2015 Elsevier Inc. All rights reserved.

Sparse Reconstruction Techniques in MRI: Methods, Applications, and Challenges to Clinical Adoption

PubMed Central

Yang, Alice Chieh-Yu; Kretzler, Madison; Sudarski, Sonja; Gulani, Vikas; Seiberlich, Nicole

2016-01-01

The family of sparse reconstruction techniques, including the recently introduced compressed sensing framework, has been extensively explored to reduce scan times in Magnetic Resonance Imaging (MRI). While there are many different methods that fall under the general umbrella of sparse reconstructions, they all rely on the idea that a priori information about the sparsity of MR images can be employed to reconstruct full images from undersampled data. This review describes the basic ideas behind sparse reconstruction techniques, how they could be applied to improve MR imaging, and the open challenges to their general adoption in a clinical setting. The fundamental principles underlying different classes of sparse reconstructions techniques are examined, and the requirements that each make on the undersampled data outlined. Applications that could potentially benefit from the accelerations that sparse reconstructions could provide are described, and clinical studies using sparse reconstructions reviewed. Lastly, technical and clinical challenges to widespread implementation of sparse reconstruction techniques, including optimization, reconstruction times, artifact appearance, and comparison with current gold-standards, are discussed. PMID:27003227

Adaptive multiple super fast simulated annealing for stochastic microstructure reconstruction

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

Ryu, Seun; Lin, Guang; Sun, Xin

2013-01-01

Fast image reconstruction from statistical information is critical in image fusion from multimodality chemical imaging instrumentation to create high resolution image with large domain. Stochastic methods have been used widely in image reconstruction from two point correlation function. The main challenge is to increase the efficiency of reconstruction. A novel simulated annealing method is proposed for fast solution of image reconstruction. Combining the advantage of very fast cooling schedules, dynamic adaption and parallelization, the new simulation annealing algorithm increases the efficiencies by several orders of magnitude, making the large domain image fusion feasible.

The challenges of studying visual expertise in medical image diagnosis.

PubMed

Gegenfurtner, Andreas; Kok, Ellen; van Geel, Koos; de Bruin, Anique; Jarodzka, Halszka; Szulewski, Adam; van Merriënboer, Jeroen Jg

2017-01-01

Visual expertise is the superior visual skill shown when executing domain-specific visual tasks. Understanding visual expertise is important in order to understand how the interpretation of medical images may be best learned and taught. In the context of this article, we focus on the visual skill of medical image diagnosis and, more specifically, on the methodological set-ups routinely used in visual expertise research. We offer a critique of commonly used methods and propose three challenges for future research to open up new avenues for studying characteristics of visual expertise in medical image diagnosis. The first challenge addresses theory development. Novel prospects in modelling visual expertise can emerge when we reflect on cognitive and socio-cultural epistemologies in visual expertise research, when we engage in statistical validations of existing theoretical assumptions and when we include social and socio-cultural processes in expertise development. The second challenge addresses the recording and analysis of longitudinal data. If we assume that the development of expertise is a long-term phenomenon, then it follows that future research can engage in advanced statistical modelling of longitudinal expertise data that extends the routine use of cross-sectional material through, for example, animations and dynamic visualisations of developmental data. The third challenge addresses the combination of methods. Alternatives to current practices can integrate qualitative and quantitative approaches in mixed-method designs, embrace relevant yet underused data sources and understand the need for multidisciplinary research teams. Embracing alternative epistemological and methodological approaches for studying visual expertise can lead to a more balanced and robust future for understanding superior visual skills in medical image diagnosis as well as other medical fields. © 2016 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

Application of Palladium-Mediated 18F-Fluorination to PET Radiotracer Development: Overcoming Hurdles to Translation

PubMed Central

Kamlet, Adam S.; Neumann, Constanze N.; Lee, Eunsung; Carlin, Stephen M.; Moseley, Christian K.; Stephenson, Nickeisha; Hooker, Jacob M.; Ritter, Tobias

2013-01-01

New chemistry methods for the synthesis of radiolabeled small molecules have the potential to impact clinical positron emission tomography (PET) imaging, if they can be successfully translated. However, progression of modern reactions from the stage of synthetic chemistry development to the preparation of radiotracer doses ready for use in human PET imaging is challenging and rare. Here we describe the process of and the successful translation of a modern palladium-mediated fluorination reaction to non-human primate (NHP) baboon PET imaging–an important milestone on the path to human PET imaging. The method, which transforms [18F]fluoride into an electrophilic fluorination reagent, provides access to aryl–18F bonds that would be challenging to synthesize via conventional radiochemistry methods. PMID:23554994

Learning normalized inputs for iterative estimation in medical image segmentation.

PubMed

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

2018-02-01

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

Evaluation and comparison of 3D intervertebral disc localization and segmentation methods for 3D T2 MR data: A grand challenge.

PubMed

Zheng, Guoyan; Chu, Chengwen; Belavý, Daniel L; Ibragimov, Bulat; Korez, Robert; Vrtovec, Tomaž; Hutt, Hugo; Everson, Richard; Meakin, Judith; Andrade, Isabel Lŏpez; Glocker, Ben; Chen, Hao; Dou, Qi; Heng, Pheng-Ann; Wang, Chunliang; Forsberg, Daniel; Neubert, Aleš; Fripp, Jurgen; Urschler, Martin; Stern, Darko; Wimmer, Maria; Novikov, Alexey A; Cheng, Hui; Armbrecht, Gabriele; Felsenberg, Dieter; Li, Shuo

2017-01-01

The evaluation of changes in Intervertebral Discs (IVDs) with 3D Magnetic Resonance (MR) Imaging (MRI) can be of interest for many clinical applications. This paper presents the evaluation of both IVD localization and IVD segmentation methods submitted to the Automatic 3D MRI IVD Localization and Segmentation challenge, held at the 2015 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI2015) with an on-site competition. With the construction of a manually annotated reference data set composed of 25 3D T2-weighted MR images acquired from two different studies and the establishment of a standard validation framework, quantitative evaluation was performed to compare the results of methods submitted to the challenge. Experimental results show that overall the best localization method achieves a mean localization distance of 0.8 mm and the best segmentation method achieves a mean Dice of 91.8%, a mean average absolute distance of 1.1 mm and a mean Hausdorff distance of 4.3 mm, respectively. The strengths and drawbacks of each method are discussed, which provides insights into the performance of different IVD localization and segmentation methods. Copyright © 2016 Elsevier B.V. All rights reserved.

Multi-feature machine learning model for automatic segmentation of green fractional vegetation cover for high-throughput field phenotyping.

PubMed

Sadeghi-Tehran, Pouria; Virlet, Nicolas; Sabermanesh, Kasra; Hawkesford, Malcolm J

2017-01-01

Accurately segmenting vegetation from the background within digital images is both a fundamental and a challenging task in phenotyping. The performance of traditional methods is satisfactory in homogeneous environments, however, performance decreases when applied to images acquired in dynamic field environments. In this paper, a multi-feature learning method is proposed to quantify vegetation growth in outdoor field conditions. The introduced technique is compared with the state-of the-art and other learning methods on digital images. All methods are compared and evaluated with different environmental conditions and the following criteria: (1) comparison with ground-truth images, (2) variation along a day with changes in ambient illumination, (3) comparison with manual measurements and (4) an estimation of performance along the full life cycle of a wheat canopy. The method described is capable of coping with the environmental challenges faced in field conditions, with high levels of adaptiveness and without the need for adjusting a threshold for each digital image. The proposed method is also an ideal candidate to process a time series of phenotypic information throughout the crop growth acquired in the field. Moreover, the introduced method has an advantage that it is not limited to growth measurements only but can be applied on other applications such as identifying weeds, diseases, stress, etc.

Application of digital image correlation for long-distance bridge deflection measurement

NASA Astrophysics Data System (ADS)

Tian, Long; Pan, Bing; Cai, Youfa; Liang, Hui; Zhao, Yan

2013-06-01

Due to its advantages of non-contact, full-field and high-resolution measurement, digital image correlation (DIC) method has gained wide acceptance and found numerous applications in the field of experimental mechanics. In this paper, the application of DIC for real-time long-distance bridge deflection detection in outdoor environments is studied. Bridge deflection measurement using DIC in outdoor environments is more challenging than regular DIC measurements performed under laboratory conditions. First, much more image noise due to variations in ambient light will be presented in the images recorded in outdoor environments. Second, how to select the target area becomes a key factor because long-distance imaging results in a large field of view of the test object. Finally, the image acquisition speed of the camera must be high enough (larger than 100 fps) to capture the real-time dynamic motion of a bridge. In this work, the above challenging issues are addressed and several improvements were made to DIC method. The applicability was demonstrated by real experiments. Experimental results indicate that the DIC method has great potentials in motion measurement in various large building structures.

PET/MRI – Technical Review

PubMed Central

Muzic, Raymond F.; DiFilippo, Frank P.

2015-01-01

PET/MR is a hybrid imaging technology with the potential to combine the molecular and functional information of PET with the soft-tissue contrast of MR. Herein we review the technical features and challenges of putting these different technologies together. We emphasize the conceptual to make the material accessible to a wide audience. We begin by reviewing PET/CT, a more mature multi-modality imaging technology, to provide a basis for comparison to the history of PET/MR development. We discuss the motivation and challenges of PET/MR and different approaches that have been used to meet the challenges. We conclude with a speculation about the future of this exciting imaging method. PMID:25497909

Detecting brain tumor in computed tomography images using Markov random fields and fuzzy C-means clustering techniques

NASA Astrophysics Data System (ADS)

Abdulbaqi, Hayder Saad; Jafri, Mohd Zubir Mat; Omar, Ahmad Fairuz; Mustafa, Iskandar Shahrim Bin; Abood, Loay Kadom

2015-04-01

Brain tumors, are an abnormal growth of tissues in the brain. They may arise in people of any age. They must be detected early, diagnosed accurately, monitored carefully, and treated effectively in order to optimize patient outcomes regarding both survival and quality of life. Manual segmentation of brain tumors from CT scan images is a challenging and time consuming task. Size and location accurate detection of brain tumor plays a vital role in the successful diagnosis and treatment of tumors. Brain tumor detection is considered a challenging mission in medical image processing. The aim of this paper is to introduce a scheme for tumor detection in CT scan images using two different techniques Hidden Markov Random Fields (HMRF) and Fuzzy C-means (FCM). The proposed method has been developed in this research in order to construct hybrid method between (HMRF) and threshold. These methods have been applied on 4 different patient data sets. The result of comparison among these methods shows that the proposed method gives good results for brain tissue detection, and is more robust and effective compared with (FCM) techniques.

Evaluation of an automatic brain segmentation method developed for neonates on adult MR brain images

NASA Astrophysics Data System (ADS)

Moeskops, Pim; Viergever, Max A.; Benders, Manon J. N. L.; Išgum, Ivana

2015-03-01

Automatic brain tissue segmentation is of clinical relevance in images acquired at all ages. The literature presents a clear distinction between methods developed for MR images of infants, and methods developed for images of adults. The aim of this work is to evaluate a method developed for neonatal images in the segmentation of adult images. The evaluated method employs supervised voxel classification in subsequent stages, exploiting spatial and intensity information. Evaluation was performed using images available within the MRBrainS13 challenge. The obtained average Dice coefficients were 85.77% for grey matter, 88.66% for white matter, 81.08% for cerebrospinal fluid, 95.65% for cerebrum, and 96.92% for intracranial cavity, currently resulting in the best overall ranking. The possibility of applying the same method to neonatal as well as adult images can be of great value in cross-sectional studies that include a wide age range.

Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles.

PubMed

Barker, Jocelyn; Hoogi, Assaf; Depeursinge, Adrien; Rubin, Daniel L

2016-05-01

Computerized analysis of digital pathology images offers the potential of improving clinical care (e.g. automated diagnosis) and catalyzing research (e.g. discovering disease subtypes). There are two key challenges thwarting computerized analysis of digital pathology images: first, whole slide pathology images are massive, making computerized analysis inefficient, and second, diverse tissue regions in whole slide images that are not directly relevant to the disease may mislead computerized diagnosis algorithms. We propose a method to overcome both of these challenges that utilizes a coarse-to-fine analysis of the localized characteristics in pathology images. An initial surveying stage analyzes the diversity of coarse regions in the whole slide image. This includes extraction of spatially localized features of shape, color and texture from tiled regions covering the slide. Dimensionality reduction of the features assesses the image diversity in the tiled regions and clustering creates representative groups. A second stage provides a detailed analysis of a single representative tile from each group. An Elastic Net classifier produces a diagnostic decision value for each representative tile. A weighted voting scheme aggregates the decision values from these tiles to obtain a diagnosis at the whole slide level. We evaluated our method by automatically classifying 302 brain cancer cases into two possible diagnoses (glioblastoma multiforme (N = 182) versus lower grade glioma (N = 120)) with an accuracy of 93.1% (p < 0.001). We also evaluated our method in the dataset provided for the 2014 MICCAI Pathology Classification Challenge, in which our method, trained and tested using 5-fold cross validation, produced a classification accuracy of 100% (p < 0.001). Our method showed high stability and robustness to parameter variation, with accuracy varying between 95.5% and 100% when evaluated for a wide range of parameters. Our approach may be useful to automatically differentiate between the two cancer subtypes. Copyright © 2015 Elsevier B.V. All rights reserved.

Noise Estimation and Quality Assessment of Gaussian Noise Corrupted Images

NASA Astrophysics Data System (ADS)

Kamble, V. M.; Bhurchandi, K.

2018-03-01

Evaluating the exact quantity of noise present in an image and quality of an image in the absence of reference image is a challenging task. We propose a near perfect noise estimation method and a no reference image quality assessment method for images corrupted by Gaussian noise. The proposed methods obtain initial estimate of noise standard deviation present in an image using the median of wavelet transform coefficients and then obtains a near to exact estimate using curve fitting. The proposed noise estimation method provides the estimate of noise within average error of +/-4%. For quality assessment, this noise estimate is mapped to fit the Differential Mean Opinion Score (DMOS) using a nonlinear function. The proposed methods require minimum training and yields the noise estimate and image quality score. Images from Laboratory for image and Video Processing (LIVE) database and Computational Perception and Image Quality (CSIQ) database are used for validation of the proposed quality assessment method. Experimental results show that the performance of proposed quality assessment method is at par with the existing no reference image quality assessment metric for Gaussian noise corrupted images.

A novel scheme for automatic nonrigid image registration using deformation invariant feature and geometric constraint

NASA Astrophysics Data System (ADS)

Deng, Zhipeng; Lei, Lin; Zhou, Shilin

2015-10-01

Automatic image registration is a vital yet challenging task, particularly for non-rigid deformation images which are more complicated and common in remote sensing images, such as distorted UAV (unmanned aerial vehicle) images or scanning imaging images caused by flutter. Traditional non-rigid image registration methods are based on the correctly matched corresponding landmarks, which usually needs artificial markers. It is a rather challenging task to locate the accurate position of the points and get accurate homonymy point sets. In this paper, we proposed an automatic non-rigid image registration algorithm which mainly consists of three steps: To begin with, we introduce an automatic feature point extraction method based on non-linear scale space and uniform distribution strategy to extract the points which are uniform distributed along the edge of the image. Next, we propose a hybrid point matching algorithm using DaLI (Deformation and Light Invariant) descriptor and local affine invariant geometric constraint based on triangulation which is constructed by K-nearest neighbor algorithm. Based on the accurate homonymy point sets, the two images are registrated by the model of TPS (Thin Plate Spline). Our method is demonstrated by three deliberately designed experiments. The first two experiments are designed to evaluate the distribution of point set and the correctly matching rate on synthetic data and real data respectively. The last experiment is designed on the non-rigid deformation remote sensing images and the three experimental results demonstrate the accuracy, robustness, and efficiency of the proposed algorithm compared with other traditional methods.

Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends

PubMed Central

Mansoor, Awais; Foster, Brent; Xu, Ziyue; Papadakis, Georgios Z.; Folio, Les R.; Udupa, Jayaram K.; Mollura, Daniel J.

2015-01-01

The computer-based process of identifying the boundaries of lung from surrounding thoracic tissue on computed tomographic (CT) images, which is called segmentation, is a vital first step in radiologic pulmonary image analysis. Many algorithms and software platforms provide image segmentation routines for quantification of lung abnormalities; however, nearly all of the current image segmentation approaches apply well only if the lungs exhibit minimal or no pathologic conditions. When moderate to high amounts of disease or abnormalities with a challenging shape or appearance exist in the lungs, computer-aided detection systems may be highly likely to fail to depict those abnormal regions because of inaccurate segmentation methods. In particular, abnormalities such as pleural effusions, consolidations, and masses often cause inaccurate lung segmentation, which greatly limits the use of image processing methods in clinical and research contexts. In this review, a critical summary of the current methods for lung segmentation on CT images is provided, with special emphasis on the accuracy and performance of the methods in cases with abnormalities and cases with exemplary pathologic findings. The currently available segmentation methods can be divided into five major classes: (a) thresholding-based, (b) region-based, (c) shape-based, (d) neighboring anatomy–guided, and (e) machine learning–based methods. The feasibility of each class and its shortcomings are explained and illustrated with the most common lung abnormalities observed on CT images. In an overview, practical applications and evolving technologies combining the presented approaches for the practicing radiologist are detailed. ©RSNA, 2015 PMID:26172351

Advancing RF pulse design using an open-competition format: Report from the 2015 ISMRM challenge.

PubMed

Grissom, William A; Setsompop, Kawin; Hurley, Samuel A; Tsao, Jeffrey; Velikina, Julia V; Samsonov, Alexey A

2017-10-01

To advance the best solutions to two important RF pulse design problems with an open head-to-head competition. Two sub-challenges were formulated in which contestants competed to design the shortest simultaneous multislice (SMS) refocusing pulses and slice-selective parallel transmission (pTx) excitation pulses, subject to realistic hardware and safety constraints. Short refocusing pulses are needed for spin echo SMS imaging at high multiband factors, and short slice-selective pTx pulses are needed for multislice imaging in ultra-high field MRI. Each sub-challenge comprised two phases, in which the first phase posed problems with a low barrier of entry, and the second phase encouraged solutions that performed well in general. The Challenge ran from October 2015 to May 2016. The pTx Challenge winners developed a spokes pulse design method that combined variable-rate selective excitation with an efficient method to enforce SAR constraints, which achieved 10.6 times shorter pulse durations than conventional approaches. The SMS Challenge winners developed a time-optimal control multiband pulse design algorithm that achieved 5.1 times shorter pulse durations than conventional approaches. The Challenge led to rapid step improvements in solutions to significant problems in RF excitation for SMS imaging and ultra-high field MRI. Magn Reson Med 78:1352-1361, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Characterizing challenged Minnesota ballots

NASA Astrophysics Data System (ADS)

Nagy, George; Lopresti, Daniel; Barney Smith, Elisa H.; Wu, Ziyan

2011-01-01

Photocopies of the ballots challenged in the 2008 Minnesota elections, which constitute a public record, were scanned on a high-speed scanner and made available on a public radio website. The PDF files were downloaded, converted to TIF images, and posted on the PERFECT website. Based on a review of relevant image-processing aspects of paper-based election machinery and on additional statistics and observations on the posted sample data, robust tools were developed for determining the underlying grid of the targets on these ballots regardless of skew, clipping, and other degradations caused by high-speed copying and digitization. The accuracy and robustness of a method based on both index-marks and oval targets are demonstrated on 13,435 challenged ballot page images.

Detection of neuron membranes in electron microscopy images using a serial neural network architecture.

PubMed

Jurrus, Elizabeth; Paiva, Antonio R C; Watanabe, Shigeki; Anderson, James R; Jones, Bryan W; Whitaker, Ross T; Jorgensen, Erik M; Marc, Robert E; Tasdizen, Tolga

2010-12-01

Study of nervous systems via the connectome, the map of connectivities of all neurons in that system, is a challenging problem in neuroscience. Towards this goal, neurobiologists are acquiring large electron microscopy datasets. However, the shear volume of these datasets renders manual analysis infeasible. Hence, automated image analysis methods are required for reconstructing the connectome from these very large image collections. Segmentation of neurons in these images, an essential step of the reconstruction pipeline, is challenging because of noise, anisotropic shapes and brightness, and the presence of confounding structures. The method described in this paper uses a series of artificial neural networks (ANNs) in a framework combined with a feature vector that is composed of image intensities sampled over a stencil neighborhood. Several ANNs are applied in series allowing each ANN to use the classification context provided by the previous network to improve detection accuracy. We develop the method of serial ANNs and show that the learned context does improve detection over traditional ANNs. We also demonstrate advantages over previous membrane detection methods. The results are a significant step towards an automated system for the reconstruction of the connectome. Copyright 2010 Elsevier B.V. All rights reserved.

Review methods for image segmentation from computed tomography images

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

Mamat, Nurwahidah; Rahman, Wan Eny Zarina Wan Abdul; Soh, Shaharuddin Cik

Image segmentation is a challenging process in order to get the accuracy of segmentation, automation and robustness especially in medical images. There exist many segmentation methods that can be implemented to medical images but not all methods are suitable. For the medical purposes, the aims of image segmentation are to study the anatomical structure, identify the region of interest, measure tissue volume to measure growth of tumor and help in treatment planning prior to radiation therapy. In this paper, we present a review method for segmentation purposes using Computed Tomography (CT) images. CT images has their own characteristics that affectmore » the ability to visualize anatomic structures and pathologic features such as blurring of the image and visual noise. The details about the methods, the goodness and the problem incurred in the methods will be defined and explained. It is necessary to know the suitable segmentation method in order to get accurate segmentation. This paper can be a guide to researcher to choose the suitable segmentation method especially in segmenting the images from CT scan.« less

Biomedical image classification based on a cascade of an SVM with a reject option and subspace analysis.

PubMed

Lin, Dongyun; Sun, Lei; Toh, Kar-Ann; Zhang, Jing Bo; Lin, Zhiping

2018-05-01

Automated biomedical image classification could confront the challenges of high level noise, image blur, illumination variation and complicated geometric correspondence among various categorical biomedical patterns in practice. To handle these challenges, we propose a cascade method consisting of two stages for biomedical image classification. At stage 1, we propose a confidence score based classification rule with a reject option for a preliminary decision using the support vector machine (SVM). The testing images going through stage 1 are separated into two groups based on their confidence scores. Those testing images with sufficiently high confidence scores are classified at stage 1 while the others with low confidence scores are rejected and fed to stage 2. At stage 2, the rejected images from stage 1 are first processed by a subspace analysis technique called eigenfeature regularization and extraction (ERE), and then classified by another SVM trained in the transformed subspace learned by ERE. At both stages, images are represented based on two types of local features, i.e., SIFT and SURF, respectively. They are encoded using various bag-of-words (BoW) models to handle biomedical patterns with and without geometric correspondence, respectively. Extensive experiments are implemented to evaluate the proposed method on three benchmark real-world biomedical image datasets. The proposed method significantly outperforms several competing state-of-the-art methods in terms of classification accuracy. Copyright © 2018 Elsevier Ltd. All rights reserved.

Spoofing detection on facial images recognition using LBP and GLCM combination

NASA Astrophysics Data System (ADS)

Sthevanie, F.; Ramadhani, K. N.

2018-03-01

The challenge for the facial based security system is how to detect facial image falsification such as facial image spoofing. Spoofing occurs when someone try to pretend as a registered user to obtain illegal access and gain advantage from the protected system. This research implements facial image spoofing detection method by analyzing image texture. The proposed method for texture analysis combines the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) method. The experimental results show that spoofing detection using LBP and GLCM combination achieves high detection rate compared to that of using only LBP feature or GLCM feature.

Spatial dependence of predictions from image segmentation: a methods to determine appropriate scales for producing land-management information

USDA-ARS?s Scientific Manuscript database

A challenge in ecological studies is defining scales of observation that correspond to relevant ecological scales for organisms or processes. Image segmentation has been proposed as an alternative to pixel-based methods for scaling remotely-sensed data into ecologically-meaningful units. However, to...

SET: a pupil detection method using sinusoidal approximation

PubMed Central

Javadi, Amir-Homayoun; Hakimi, Zahra; Barati, Morteza; Walsh, Vincent; Tcheang, Lili

2015-01-01

Mobile eye-tracking in external environments remains challenging, despite recent advances in eye-tracking software and hardware engineering. Many current methods fail to deal with the vast range of outdoor lighting conditions and the speed at which these can change. This confines experiments to artificial environments where conditions must be tightly controlled. Additionally, the emergence of low-cost eye tracking devices calls for the development of analysis tools that enable non-technical researchers to process the output of their images. We have developed a fast and accurate method (known as “SET”) that is suitable even for natural environments with uncontrolled, dynamic and even extreme lighting conditions. We compared the performance of SET with that of two open-source alternatives by processing two collections of eye images: images of natural outdoor scenes with extreme lighting variations (“Natural”); and images of less challenging indoor scenes (“CASIA-Iris-Thousand”). We show that SET excelled in outdoor conditions and was faster, without significant loss of accuracy, indoors. SET offers a low cost eye-tracking solution, delivering high performance even in challenging outdoor environments. It is offered through an open-source MATLAB toolkit as well as a dynamic-link library (“DLL”), which can be imported into many programming languages including C# and Visual Basic in Windows OS (www.eyegoeyetracker.co.uk). PMID:25914641

Automatic single-image-based rain streaks removal via image decomposition.

PubMed

Kang, Li-Wei; Lin, Chia-Wen; Fu, Yu-Hsiang

2012-04-01

Rain removal from a video is a challenging problem and has been recently investigated extensively. Nevertheless, the problem of rain removal from a single image was rarely studied in the literature, where no temporal information among successive images can be exploited, making the problem very challenging. In this paper, we propose a single-image-based rain removal framework via properly formulating rain removal as an image decomposition problem based on morphological component analysis. Instead of directly applying a conventional image decomposition technique, the proposed method first decomposes an image into the low- and high-frequency (HF) parts using a bilateral filter. The HF part is then decomposed into a "rain component" and a "nonrain component" by performing dictionary learning and sparse coding. As a result, the rain component can be successfully removed from the image while preserving most original image details. Experimental results demonstrate the efficacy of the proposed algorithm.

A Dictionary Learning Method with Total Generalized Variation for MRI Reconstruction

PubMed Central

Lu, Hongyang; Wei, Jingbo; Wang, Yuhao; Deng, Xiaohua

2016-01-01

Reconstructing images from their noisy and incomplete measurements is always a challenge especially for medical MR image with important details and features. This work proposes a novel dictionary learning model that integrates two sparse regularization methods: the total generalized variation (TGV) approach and adaptive dictionary learning (DL). In the proposed method, the TGV selectively regularizes different image regions at different levels to avoid oil painting artifacts largely. At the same time, the dictionary learning adaptively represents the image features sparsely and effectively recovers details of images. The proposed model is solved by variable splitting technique and the alternating direction method of multiplier. Extensive simulation experimental results demonstrate that the proposed method consistently recovers MR images efficiently and outperforms the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values. PMID:27110235

A Dictionary Learning Method with Total Generalized Variation for MRI Reconstruction.

PubMed

Lu, Hongyang; Wei, Jingbo; Liu, Qiegen; Wang, Yuhao; Deng, Xiaohua

2016-01-01

Reconstructing images from their noisy and incomplete measurements is always a challenge especially for medical MR image with important details and features. This work proposes a novel dictionary learning model that integrates two sparse regularization methods: the total generalized variation (TGV) approach and adaptive dictionary learning (DL). In the proposed method, the TGV selectively regularizes different image regions at different levels to avoid oil painting artifacts largely. At the same time, the dictionary learning adaptively represents the image features sparsely and effectively recovers details of images. The proposed model is solved by variable splitting technique and the alternating direction method of multiplier. Extensive simulation experimental results demonstrate that the proposed method consistently recovers MR images efficiently and outperforms the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values.

Latent Image Processing Can Bolster the Value of Quizzes.

ERIC Educational Resources Information Center

Singer, David

1985-01-01

Latent image processing is a method which reveals hidden ink when marked with a special pen. Using multiple-choice items with commercially available latent image transfers can provide immediate feedback on take-home quizzes. Students benefitted from formative evaluation and were challenged to search for alternative solutions and explain unexpected…

MO-C-18A-01: Advances in Model-Based 3D Image Reconstruction

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

Chen, G; Pan, X; Stayman, J

2014-06-15

Recent years have seen the emergence of CT image reconstruction techniques that exploit physical models of the imaging system, photon statistics, and even the patient to achieve improved 3D image quality and/or reduction of radiation dose. With numerous advantages in comparison to conventional 3D filtered backprojection, such techniques bring a variety of challenges as well, including: a demanding computational load associated with sophisticated forward models and iterative optimization methods; nonlinearity and nonstationarity in image quality characteristics; a complex dependency on multiple free parameters; and the need to understand how best to incorporate prior information (including patient-specific prior images) within themore » reconstruction process. The advantages, however, are even greater – for example: improved image quality; reduced dose; robustness to noise and artifacts; task-specific reconstruction protocols; suitability to novel CT imaging platforms and noncircular orbits; and incorporation of known characteristics of the imager and patient that are conventionally discarded. This symposium features experts in 3D image reconstruction, image quality assessment, and the translation of such methods to emerging clinical applications. Dr. Chen will address novel methods for the incorporation of prior information in 3D and 4D CT reconstruction techniques. Dr. Pan will show recent advances in optimization-based reconstruction that enable potential reduction of dose and sampling requirements. Dr. Stayman will describe a “task-based imaging” approach that leverages models of the imaging system and patient in combination with a specification of the imaging task to optimize both the acquisition and reconstruction process. Dr. Samei will describe the development of methods for image quality assessment in such nonlinear reconstruction techniques and the use of these methods to characterize and optimize image quality and dose in a spectrum of clinical applications. Learning Objectives: Learn the general methodologies associated with model-based 3D image reconstruction. Learn the potential advantages in image quality and dose associated with model-based image reconstruction. Learn the challenges associated with computational load and image quality assessment for such reconstruction methods. Learn how imaging task can be incorporated as a means to drive optimal image acquisition and reconstruction techniques. Learn how model-based reconstruction methods can incorporate prior information to improve image quality, ease sampling requirements, and reduce dose.« less

Detecting brain tumor in computed tomography images using Markov random fields and fuzzy C-means clustering techniques

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

Abdulbaqi, Hayder Saad; Department of Physics, College of Education, University of Al-Qadisiya, Al-Qadisiya; Jafri, Mohd Zubir Mat

Brain tumors, are an abnormal growth of tissues in the brain. They may arise in people of any age. They must be detected early, diagnosed accurately, monitored carefully, and treated effectively in order to optimize patient outcomes regarding both survival and quality of life. Manual segmentation of brain tumors from CT scan images is a challenging and time consuming task. Size and location accurate detection of brain tumor plays a vital role in the successful diagnosis and treatment of tumors. Brain tumor detection is considered a challenging mission in medical image processing. The aim of this paper is to introducemore » a scheme for tumor detection in CT scan images using two different techniques Hidden Markov Random Fields (HMRF) and Fuzzy C-means (FCM). The proposed method has been developed in this research in order to construct hybrid method between (HMRF) and threshold. These methods have been applied on 4 different patient data sets. The result of comparison among these methods shows that the proposed method gives good results for brain tissue detection, and is more robust and effective compared with (FCM) techniques.« less

Detecting breast microcalcifications using super-resolution ultrasound imaging: a clinical study

NASA Astrophysics Data System (ADS)

Huang, Lianjie; Labyed, Yassin; Hanson, Kenneth; Sandoval, Daniel; Pohl, Jennifer; Williamson, Michael

2013-03-01

Imaging breast microcalcifications is crucial for early detection and diagnosis of breast cancer. It is challenging for current clinical ultrasound to image breast microcalcifications. However, new imaging techniques using data acquired with a synthetic-aperture ultrasound system have the potential to significantly improve ultrasound imaging. We recently developed a super-resolution ultrasound imaging method termed the phase-coherent multiple-signal classification (PC-MUSIC). This signal subspace method accounts for the phase response of transducer elements to improve image resolution. In this paper, we investigate the clinical feasibility of our super-resolution ultrasound imaging method for detecting breast microcalcifications. We use our custom-built, real-time synthetic-aperture ultrasound system to acquire breast ultrasound data for 40 patients whose mammograms show the presence of breast microcalcifications. We apply our super-resolution ultrasound imaging method to the patient data, and produce clear images of breast calcifications. Our super-resolution ultrasound PC-MUSIC imaging with synthetic-aperture ultrasound data can provide a new imaging modality for detecting breast microcalcifications in clinic without using ionizing radiation.

MS lesion segmentation using a multi-channel patch-based approach with spatial consistency

NASA Astrophysics Data System (ADS)

Mechrez, Roey; Goldberger, Jacob; Greenspan, Hayit

2015-03-01

This paper presents an automatic method for segmentation of Multiple Sclerosis (MS) in Magnetic Resonance Images (MRI) of the brain. The approach is based on similarities between multi-channel patches (T1, T2 and FLAIR). An MS lesion patch database is built using training images for which the label maps are known. For each patch in the testing image, k similar patches are retrieved from the database. The matching labels for these k patches are then combined to produce an initial segmentation map for the test case. Finally a novel iterative patch-based label refinement process based on the initial segmentation map is performed to ensure spatial consistency of the detected lesions. A leave-one-out evaluation is done for each testing image in the MS lesion segmentation challenge of MICCAI 2008. Results are shown to compete with the state-of-the-art methods on the MICCAI 2008 challenge.

The role of radiology in diagnosis and management of drug mules: an update with new challenges and new diagnostic tools

PubMed Central

Cengel, Ferhat

2016-01-01

Emergency physicians and radiologists have been increasingly encountering internal concealment of illegal drugs. The packages commonly contain powdered solid drugs such as cocaine, heroin, methamphetamine and hashish, but they may also contain cocaine in the liquid form. The second type of package has recently been more commonly encountered, and poses a greater diagnostic challenge. As clinical evaluation and laboratory tests frequently fail to make the correct diagnosis, imaging examination is typically required. Imaging methods assume a vital role in the diagnosis, follow-up and management. Abdominal X-ray, ultrasonography, CT and MRI are used for the imaging purposes. Among the aforementioned methods, low-dose CT is state-of-the-art in these cases. It is of paramount importance that radiologists have a full knowledge of the imaging characteristics of these packages and accurately guide physicians and security officials. PMID:26867003

Forward Looking Radar Imaging by Truncated Singular Value Decomposition and Its Application for Adverse Weather Aircraft Landing.

PubMed

Huang, Yulin; Zha, Yuebo; Wang, Yue; Yang, Jianyu

2015-06-18

The forward looking radar imaging task is a practical and challenging problem for adverse weather aircraft landing industry. Deconvolution method can realize the forward looking imaging but it often leads to the noise amplification in the radar image. In this paper, a forward looking radar imaging based on deconvolution method is presented for adverse weather aircraft landing. We first present the theoretical background of forward looking radar imaging task and its application for aircraft landing. Then, we convert the forward looking radar imaging task into a corresponding deconvolution problem, which is solved in the framework of algebraic theory using truncated singular decomposition method. The key issue regarding the selecting of the truncated parameter is addressed using generalized cross validation approach. Simulation and experimental results demonstrate that the proposed method is effective in achieving angular resolution enhancement with suppressing the noise amplification in forward looking radar imaging.

Automated tissue segmentation of MR brain images in the presence of white matter lesions.

PubMed

Valverde, Sergi; Oliver, Arnau; Roura, Eloy; González-Villà, Sandra; Pareto, Deborah; Vilanova, Joan C; Ramió-Torrentà, Lluís; Rovira, Àlex; Lladó, Xavier

2017-01-01

Over the last few years, the increasing interest in brain tissue volume measurements on clinical settings has led to the development of a wide number of automated tissue segmentation methods. However, white matter lesions are known to reduce the performance of automated tissue segmentation methods, which requires manual annotation of the lesions and refilling them before segmentation, which is tedious and time-consuming. Here, we propose a new, fully automated T1-w/FLAIR tissue segmentation approach designed to deal with images in the presence of WM lesions. This approach integrates a robust partial volume tissue segmentation with WM outlier rejection and filling, combining intensity and probabilistic and morphological prior maps. We evaluate the performance of this method on the MRBrainS13 tissue segmentation challenge database, which contains images with vascular WM lesions, and also on a set of Multiple Sclerosis (MS) patient images. On both databases, we validate the performance of our method with other state-of-the-art techniques. On the MRBrainS13 data, the presented approach was at the time of submission the best ranked unsupervised intensity model method of the challenge (7th position) and clearly outperformed the other unsupervised pipelines such as FAST and SPM12. On MS data, the differences in tissue segmentation between the images segmented with our method and the same images where manual expert annotations were used to refill lesions on T1-w images before segmentation were lower or similar to the best state-of-the-art pipeline incorporating automated lesion segmentation and filling. Our results show that the proposed pipeline achieved very competitive results on both vascular and MS lesions. A public version of this approach is available to download for the neuro-imaging community. Copyright © 2016 Elsevier B.V. All rights reserved.

EIT Imaging of admittivities with a D-bar method and spatial prior: experimental results for absolute and difference imaging.

PubMed

Hamilton, S J

2017-05-22

Electrical impedance tomography (EIT) is an emerging imaging modality that uses harmless electrical measurements taken on electrodes at a body's surface to recover information about the internal electrical conductivity and or permittivity. The image reconstruction task of EIT is a highly nonlinear inverse problem that is sensitive to noise and modeling errors making the image reconstruction task challenging. D-bar methods solve the nonlinear problem directly, bypassing the need for detailed and time-intensive forward models, to provide absolute (static) as well as time-difference EIT images. Coupling the D-bar methodology with the inclusion of high confidence a priori data results in a noise-robust regularized image reconstruction method. In this work, the a priori D-bar method for complex admittivities is demonstrated effective on experimental tank data for absolute imaging for the first time. Additionally, the method is adjusted for, and tested on, time-difference imaging scenarios. The ability of the method to be used for conductivity, permittivity, absolute as well as time-difference imaging provides the user with great flexibility without a high computational cost.

Misaligned Image Integration With Local Linear Model.

PubMed

Baba, Tatsuya; Matsuoka, Ryo; Shirai, Keiichiro; Okuda, Masahiro

2016-05-01

We present a new image integration technique for a flash and long-exposure image pair to capture a dark scene without incurring blurring or noisy artifacts. Most existing methods require well-aligned images for the integration, which is often a burdensome restriction in practical use. We address this issue by locally transferring the colors of the flash images using a small fraction of the corresponding pixels in the long-exposure images. We formulate the image integration as a convex optimization problem with the local linear model. The proposed method makes it possible to integrate the color of the long-exposure image with the detail of the flash image without causing any harmful effects to its contrast, where we do not need perfect alignment between the images by virtue of our new integration principle. We show that our method successfully outperforms the state of the art in the image integration and reference-based color transfer for challenging misaligned data sets.

Synergistic Instance-Level Subspace Alignment for Fine-Grained Sketch-Based Image Retrieval.

PubMed

Li, Ke; Pang, Kaiyue; Song, Yi-Zhe; Hospedales, Timothy M; Xiang, Tao; Zhang, Honggang

2017-08-25

We study the problem of fine-grained sketch-based image retrieval. By performing instance-level (rather than category-level) retrieval, it embodies a timely and practical application, particularly with the ubiquitous availability of touchscreens. Three factors contribute to the challenging nature of the problem: (i) free-hand sketches are inherently abstract and iconic, making visual comparisons with photos difficult, (ii) sketches and photos are in two different visual domains, i.e. black and white lines vs. color pixels, and (iii) fine-grained distinctions are especially challenging when executed across domain and abstraction-level. To address these challenges, we propose to bridge the image-sketch gap both at the high-level via parts and attributes, as well as at the low-level, via introducing a new domain alignment method. More specifically, (i) we contribute a dataset with 304 photos and 912 sketches, where each sketch and image is annotated with its semantic parts and associated part-level attributes. With the help of this dataset, we investigate (ii) how strongly-supervised deformable part-based models can be learned that subsequently enable automatic detection of part-level attributes, and provide pose-aligned sketch-image comparisons. To reduce the sketch-image gap when comparing low-level features, we also (iii) propose a novel method for instance-level domain-alignment, that exploits both subspace and instance-level cues to better align the domains. Finally (iv) these are combined in a matching framework integrating aligned low-level features, mid-level geometric structure and high-level semantic attributes. Extensive experiments conducted on our new dataset demonstrate effectiveness of the proposed method.

Multi-Wave and Hybrid Imaging Techniques: A New Direction for Nondestructive Testing and Structural Health Monitoring

PubMed Central

Cheng, Yuhua; Deng, Yiming; Cao, Jing; Xiong, Xin; Bai, Libing; Li, Zhaojun

2013-01-01

In this article, the state-of-the-art multi-wave and hybrid imaging techniques in the field of nondestructive evaluation and structural health monitoring were comprehensively reviewed. A new direction for assessment and health monitoring of various structures by capitalizing the advantages of those imaging methods was discussed. Although sharing similar system configurations, the imaging physics and principles of multi-wave phenomena and hybrid imaging methods are inherently different. After a brief introduction of nondestructive evaluation (NDE), structure health monitoring (SHM) and their related challenges, several recent advances that have significantly extended imaging methods from laboratory development into practical applications were summarized, followed by conclusions and discussion on future directions. PMID:24287536

Pose-Invariant Face Recognition via RGB-D Images.

PubMed

Sang, Gaoli; Li, Jing; Zhao, Qijun

2016-01-01

Three-dimensional (3D) face models can intrinsically handle large pose face recognition problem. In this paper, we propose a novel pose-invariant face recognition method via RGB-D images. By employing depth, our method is able to handle self-occlusion and deformation, both of which are challenging problems in two-dimensional (2D) face recognition. Texture images in the gallery can be rendered to the same view as the probe via depth. Meanwhile, depth is also used for similarity measure via frontalization and symmetric filling. Finally, both texture and depth contribute to the final identity estimation. Experiments on Bosphorus, CurtinFaces, Eurecom, and Kiwi databases demonstrate that the additional depth information has improved the performance of face recognition with large pose variations and under even more challenging conditions.

Cascaded deep decision networks for classification of endoscopic images

NASA Astrophysics Data System (ADS)

Murthy, Venkatesh N.; Singh, Vivek; Sun, Shanhui; Bhattacharya, Subhabrata; Chen, Terrence; Comaniciu, Dorin

2017-02-01

Both traditional and wireless capsule endoscopes can generate tens of thousands of images for each patient. It is desirable to have the majority of irrelevant images filtered out by automatic algorithms during an offline review process or to have automatic indication for highly suspicious areas during an online guidance. This also applies to the newly invented endomicroscopy, where online indication of tumor classification plays a significant role. Image classification is a standard pattern recognition problem and is well studied in the literature. However, performance on the challenging endoscopic images still has room for improvement. In this paper, we present a novel Cascaded Deep Decision Network (CDDN) to improve image classification performance over standard Deep neural network based methods. During the learning phase, CDDN automatically builds a network which discards samples that are classified with high confidence scores by a previously trained network and concentrates only on the challenging samples which would be handled by the subsequent expert shallow networks. We validate CDDN using two different types of endoscopic imaging, which includes a polyp classification dataset and a tumor classification dataset. From both datasets we show that CDDN can outperform other methods by about 10%. In addition, CDDN can also be applied to other image classification problems.

Contrast-dependent saturation adjustment for outdoor image enhancement.

PubMed

Wang, Shuhang; Cho, Woon; Jang, Jinbeum; Abidi, Mongi A; Paik, Joonki

2017-01-01

Outdoor images captured in bad-weather conditions usually have poor intensity contrast and color saturation since the light arriving at the camera is severely scattered or attenuated. The task of improving image quality in poor conditions remains a challenge. Existing methods of image quality improvement are usually effective for a small group of images but often fail to produce satisfactory results for a broader variety of images. In this paper, we propose an image enhancement method, which makes it applicable to enhance outdoor images by using content-adaptive contrast improvement as well as contrast-dependent saturation adjustment. The main contribution of this work is twofold: (1) we propose the content-adaptive histogram equalization based on the human visual system to improve the intensity contrast; and (2) we introduce a simple yet effective prior for adjusting the color saturation depending on the intensity contrast. The proposed method is tested with different kinds of images, compared with eight state-of-the-art methods: four enhancement methods and four haze removal methods. Experimental results show the proposed method can more effectively improve the visibility and preserve the naturalness of the images, as opposed to the compared methods.

Adaptive and robust statistical methods for processing near-field scanning microwave microscopy images.

PubMed

Coakley, K J; Imtiaz, A; Wallis, T M; Weber, J C; Berweger, S; Kabos, P

2015-03-01

Near-field scanning microwave microscopy offers great potential to facilitate characterization, development and modeling of materials. By acquiring microwave images at multiple frequencies and amplitudes (along with the other modalities) one can study material and device physics at different lateral and depth scales. Images are typically noisy and contaminated by artifacts that can vary from scan line to scan line and planar-like trends due to sample tilt errors. Here, we level images based on an estimate of a smooth 2-d trend determined with a robust implementation of a local regression method. In this robust approach, features and outliers which are not due to the trend are automatically downweighted. We denoise images with the Adaptive Weights Smoothing method. This method smooths out additive noise while preserving edge-like features in images. We demonstrate the feasibility of our methods on topography images and microwave |S11| images. For one challenging test case, we demonstrate that our method outperforms alternative methods from the scanning probe microscopy data analysis software package Gwyddion. Our methods should be useful for massive image data sets where manual selection of landmarks or image subsets by a user is impractical. Published by Elsevier B.V.

A novel method based on learning automata for automatic lesion detection in breast magnetic resonance imaging.

PubMed

Salehi, Leila; Azmi, Reza

2014-07-01

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. In this way, magnetic resonance imaging (MRI) is emerging as a powerful tool for the detection of breast cancer. Breast MRI presently has two major challenges. First, its specificity is relatively poor, and it detects many false positives (FPs). Second, the method involves acquiring several high-resolution image volumes before, during, and after the injection of a contrast agent. The large volume of data makes the task of interpretation by the radiologist both complex and time-consuming. These challenges have led to the development of the computer-aided detection systems to improve the efficiency and accuracy of the interpretation process. Detection of suspicious regions of interests (ROIs) is a critical preprocessing step in dynamic contrast-enhanced (DCE)-MRI data evaluation. In this regard, this paper introduces a new automatic method to detect the suspicious ROIs for breast DCE-MRI based on region growing. The results indicate that the proposed method is thoroughly able to identify suspicious regions (accuracy of 75.39 ± 3.37 on PIDER breast MRI dataset). Furthermore, the FP per image in this method is averagely 7.92, which shows considerable improvement comparing to other methods like ROI hunter.

Challenges and advantages in wide-field optical coherence tomography angiography imaging of the human retinal and choroidal vasculature at 1.7-MHz A-scan rate

NASA Astrophysics Data System (ADS)

Poddar, Raju; Migacz, Justin V.; Schwartz, Daniel M.; Werner, John S.; Gorczynska, Iwona

2017-10-01

We present noninvasive, three-dimensional, depth-resolved imaging of human retinal and choroidal blood circulation with a swept-source optical coherence tomography (OCT) system at 1065-nm center wavelength. Motion contrast OCT imaging was performed with the phase-variance OCT angiography method. A Fourier-domain mode-locked light source was used to enable an imaging rate of 1.7 MHz. We experimentally demonstrate the challenges and advantages of wide-field OCT angiography (OCTA). In the discussion, we consider acquisition time, scanning area, scanning density, and their influence on visualization of selected features of the retinal and choroidal vascular networks. The OCTA imaging was performed with a field of view of 16 deg (5 mm×5 mm) and 30 deg (9 mm×9 mm). Data were presented in en face projections generated from single volumes and in en face projection mosaics generated from up to 4 datasets. OCTA imaging at 1.7 MHz A-scan rate was compared with results obtained from a commercial OCTA instrument and with conventional ophthalmic diagnostic methods: fundus photography, fluorescein, and indocyanine green angiography. Comparison of images obtained from all methods is demonstrated using the same eye of a healthy volunteer. For example, imaging of retinal pathology is presented in three cases of advanced age-related macular degeneration.

Crack image segmentation based on improved DBC method

NASA Astrophysics Data System (ADS)

Cao, Ting; Yang, Nan; Wang, Fengping; Gao, Ting; Wang, Weixing

2017-11-01

With the development of computer vision technology, crack detection based on digital image segmentation method arouses global attentions among researchers and transportation ministries. Since the crack always exhibits the random shape and complex texture, it is still a challenge to accomplish reliable crack detection results. Therefore, a novel crack image segmentation method based on fractal DBC (differential box counting) is introduced in this paper. The proposed method can estimate every pixel fractal feature based on neighborhood information which can consider the contribution from all possible direction in the related block. The block moves just one pixel every time so that it could cover all the pixels in the crack image. Unlike the classic DBC method which only describes fractal feature for the related region, this novel method can effectively achieve crack image segmentation according to the fractal feature of every pixel. The experiment proves the proposed method can achieve satisfactory results in crack detection.

Lossless Compression of JPEG Coded Photo Collections.

PubMed

Wu, Hao; Sun, Xiaoyan; Yang, Jingyu; Zeng, Wenjun; Wu, Feng

2016-04-06

The explosion of digital photos has posed a significant challenge to photo storage and transmission for both personal devices and cloud platforms. In this paper, we propose a novel lossless compression method to further reduce the size of a set of JPEG coded correlated images without any loss of information. The proposed method jointly removes inter/intra image redundancy in the feature, spatial, and frequency domains. For each collection, we first organize the images into a pseudo video by minimizing the global prediction cost in the feature domain. We then present a hybrid disparity compensation method to better exploit both the global and local correlations among the images in the spatial domain. Furthermore, the redundancy between each compensated signal and the corresponding target image is adaptively reduced in the frequency domain. Experimental results demonstrate the effectiveness of the proposed lossless compression method. Compared to the JPEG coded image collections, our method achieves average bit savings of more than 31%.

Coherent diffractive imaging methods for semiconductor manufacturing

NASA Astrophysics Data System (ADS)

Helfenstein, Patrick; Mochi, Iacopo; Rajeev, Rajendran; Fernandez, Sara; Ekinci, Yasin

2017-12-01

The paradigm shift of the semiconductor industry moving from deep ultraviolet to extreme ultraviolet lithography (EUVL) brought about new challenges in the fabrication of illumination and projection optics, which constitute one of the core sources of cost of ownership for many of the metrology tools needed in the lithography process. For this reason, lensless imaging techniques based on coherent diffractive imaging started to raise interest in the EUVL community. This paper presents an overview of currently on-going research endeavors that use a number of methods based on lensless imaging with coherent light.

Addressing challenges of modulation transfer function measurement with fisheye lens cameras

NASA Astrophysics Data System (ADS)

Deegan, Brian M.; Denny, Patrick E.; Zlokolica, Vladimir; Dever, Barry; Russell, Laura

2015-03-01

Modulation transfer function (MTF) is a well defined and accepted method of measuring image sharpness. The slanted edge test, as defined in ISO12233 is a standard method of calculating MTF, and is widely used for lens alignment and auto-focus algorithm verification. However, there are a number of challenges which should be considered when measuring MTF in cameras with fisheye lenses. Due to trade-offs related Petzval curvature, planarity of the optical plane is difficult to achieve in fisheye lenses. It is therefore critical to have the ability to accurately measure sharpness throughout the entire image, particularly for lens alignment. One challenge for fisheye lenses is that, because of the radial distortion, the slanted edges will have different angles, depending on the location within the image and on the distortion profile of the lens. Previous work in the literature indicates that MTF measurements are robust for angles between 2 and 10 degrees. Outside of this range, MTF measurements become unreliable. Also, the slanted edge itself will be curved by the lens distortion, causing further measurement problems. This study summarises the difficulties in the use of MTF for sharpness measurement in fisheye lens cameras, and proposes mitigations and alternative methods.

Imaging for understanding speech communication: Advances and challenges

NASA Astrophysics Data System (ADS)

Narayanan, Shrikanth

2005-04-01

Research in speech communication has relied on a variety of instrumentation methods to illuminate details of speech production and perception. One longstanding challenge has been the ability to examine real-time changes in the shaping of the vocal tract; a goal that has been furthered by imaging techniques such as ultrasound, movement tracking, and magnetic resonance imaging. The spatial and temporal resolution afforded by these techniques, however, has limited the scope of the investigations that could be carried out. In this talk, we focus on some recent advances in magnetic resonance imaging that allow us to perform near real-time investigations on the dynamics of vocal tract shaping during speech. Examples include Demolin et al. (2000) (4-5 images/second, ultra-fast turbo spin echo) and Mady et al. (2001,2002) (8 images/second, T1 fast gradient echo). A recent study by Narayanan et al. (2004) that used a spiral readout scheme to accelerate image acquisition has allowed for image reconstruction rates of 24 images/second. While these developments offer exciting prospects, a number of challenges lie ahead, including: (1) improving image acquisition protocols, hardware for enhancing signal-to-noise ratio, and optimizing spatial sampling; (2) acquiring quality synchronized audio; and (3) analyzing and modeling image data including cross-modality registration. [Work supported by NIH and NSF.

Robust registration of sparsely sectioned histology to ex-vivo MRI of temporal lobe resections

NASA Astrophysics Data System (ADS)

Goubran, Maged; Khan, Ali R.; Crukley, Cathie; Buchanan, Susan; Santyr, Brendan; deRibaupierre, Sandrine; Peters, Terry M.

2012-02-01

Surgical resection of epileptic foci is a typical treatment for drug-resistant epilepsy, however, accurate preoperative localization is challenging and often requires invasive sub-dural or intra-cranial electrode placement. The presence of cellular abnormalities in the resected tissue can be used to validate the effectiveness of multispectralMagnetic Resonance Imaging (MRI) in pre-operative foci localization and surgical planning. If successful, these techniques can lead to improved surgical outcomes and less invasive procedures. Towards this goal, a novel pipeline is presented here for post-operative imaging of temporal lobe specimens involving MRI and digital histology, and present and evaluate methods for bringing these images into spatial correspondence. The sparsely-sectioned histology images of resected tissue represents a challenge for 3D reconstruction which we address with a combined 3D and 2D rigid registration algorithm that alternates between slice-based and volume-based registration with the ex-vivo MRI. We also evaluate four methods for non-rigid within-plane registration using both images and fiducials, with the top performing method resulting in a target registration error of 0.87 mm. This work allows for the spatially-local comparison of histology with post-operative MRI and paves the way for eventual registration with pre-operative MRI images.

Dynamic PET Image reconstruction for parametric imaging using the HYPR kernel method

NASA Astrophysics Data System (ADS)

Spencer, Benjamin; Qi, Jinyi; Badawi, Ramsey D.; Wang, Guobao

2017-03-01

Dynamic PET image reconstruction is a challenging problem because of the ill-conditioned nature of PET and the lowcounting statistics resulted from short time-frames in dynamic imaging. The kernel method for image reconstruction has been developed to improve image reconstruction of low-count PET data by incorporating prior information derived from high-count composite data. In contrast to most of the existing regularization-based methods, the kernel method embeds image prior information in the forward projection model and does not require an explicit regularization term in the reconstruction formula. Inspired by the existing highly constrained back-projection (HYPR) algorithm for dynamic PET image denoising, we propose in this work a new type of kernel that is simpler to implement and further improves the kernel-based dynamic PET image reconstruction. Our evaluation study using a physical phantom scan with synthetic FDG tracer kinetics has demonstrated that the new HYPR kernel-based reconstruction can achieve a better region-of-interest (ROI) bias versus standard deviation trade-off for dynamic PET parametric imaging than the post-reconstruction HYPR denoising method and the previously used nonlocal-means kernel.

Guided filter-based fusion method for multiexposure images

NASA Astrophysics Data System (ADS)

Hou, Xinglin; Luo, Haibo; Qi, Feng; Zhou, Peipei

2016-11-01

It is challenging to capture a high-dynamic range (HDR) scene using a low-dynamic range camera. A weighted sum-based image fusion (IF) algorithm is proposed so as to express an HDR scene with a high-quality image. This method mainly includes three parts. First, two image features, i.e., gradients and well-exposedness are measured to estimate the initial weight maps. Second, the initial weight maps are refined by a guided filter, in which the source image is considered as the guidance image. This process could reduce the noise in initial weight maps and preserve more texture consistent with the original images. Finally, the fused image is constructed by a weighted sum of source images in the spatial domain. The main contributions of this method are the estimation of the initial weight maps and the appropriate use of the guided filter-based weight maps refinement. It provides accurate weight maps for IF. Compared to traditional IF methods, this algorithm avoids image segmentation, combination, and the camera response curve calibration. Furthermore, experimental results demonstrate the superiority of the proposed method in both subjective and objective evaluations.

Fractional domain varying-order differential denoising method

NASA Astrophysics Data System (ADS)

Zhang, Yan-Shan; Zhang, Feng; Li, Bing-Zhao; Tao, Ran

2014-10-01

Removal of noise is an important step in the image restoration process, and it remains a challenging problem in image processing. Denoising is a process used to remove the noise from the corrupted image, while retaining the edges and other detailed features as much as possible. Recently, denoising in the fractional domain is a hot research topic. The fractional-order anisotropic diffusion method can bring a less blocky effect and preserve edges in image denoising, a method that has received much interest in the literature. Based on this method, we propose a new method for image denoising, in which fractional-varying-order differential, rather than constant-order differential, is used. The theoretical analysis and experimental results show that compared with the state-of-the-art fractional-order anisotropic diffusion method, the proposed fractional-varying-order differential denoising model can preserve structure and texture well, while quickly removing noise, and yields good visual effects and better peak signal-to-noise ratio.

Functional Principal Component Analysis and Randomized Sparse Clustering Algorithm for Medical Image Analysis

PubMed Central

Lin, Nan; Jiang, Junhai; Guo, Shicheng; Xiong, Momiao

2015-01-01

Due to the advancement in sensor technology, the growing large medical image data have the ability to visualize the anatomical changes in biological tissues. As a consequence, the medical images have the potential to enhance the diagnosis of disease, the prediction of clinical outcomes and the characterization of disease progression. But in the meantime, the growing data dimensions pose great methodological and computational challenges for the representation and selection of features in image cluster analysis. To address these challenges, we first extend the functional principal component analysis (FPCA) from one dimension to two dimensions to fully capture the space variation of image the signals. The image signals contain a large number of redundant features which provide no additional information for clustering analysis. The widely used methods for removing the irrelevant features are sparse clustering algorithms using a lasso-type penalty to select the features. However, the accuracy of clustering using a lasso-type penalty depends on the selection of the penalty parameters and the threshold value. In practice, they are difficult to determine. Recently, randomized algorithms have received a great deal of attentions in big data analysis. This paper presents a randomized algorithm for accurate feature selection in image clustering analysis. The proposed method is applied to both the liver and kidney cancer histology image data from the TCGA database. The results demonstrate that the randomized feature selection method coupled with functional principal component analysis substantially outperforms the current sparse clustering algorithms in image cluster analysis. PMID:26196383

Multilinear Graph Embedding: Representation and Regularization for Images.

PubMed

Chen, Yi-Lei; Hsu, Chiou-Ting

2014-02-01

Given a set of images, finding a compact and discriminative representation is still a big challenge especially when multiple latent factors are hidden in the way of data generation. To represent multifactor images, although multilinear models are widely used to parameterize the data, most methods are based on high-order singular value decomposition (HOSVD), which preserves global statistics but interprets local variations inadequately. To this end, we propose a novel method, called multilinear graph embedding (MGE), as well as its kernelization MKGE to leverage the manifold learning techniques into multilinear models. Our method theoretically links the linear, nonlinear, and multilinear dimensionality reduction. We also show that the supervised MGE encodes informative image priors for image regularization, provided that an image is represented as a high-order tensor. From our experiments on face and gait recognition, the superior performance demonstrates that MGE better represents multifactor images than classic methods, including HOSVD and its variants. In addition, the significant improvement in image (or tensor) completion validates the potential of MGE for image regularization.

Joint image restoration and location in visual navigation system

NASA Astrophysics Data System (ADS)

Wu, Yuefeng; Sang, Nong; Lin, Wei; Shao, Yuanjie

2018-02-01

Image location methods are the key technologies of visual navigation, most previous image location methods simply assume the ideal inputs without taking into account the real-world degradations (e.g. low resolution and blur). In view of such degradations, the conventional image location methods first perform image restoration and then match the restored image on the reference image. However, the defective output of the image restoration can affect the result of localization, by dealing with the restoration and location separately. In this paper, we present a joint image restoration and location (JRL) method, which utilizes the sparse representation prior to handle the challenging problem of low-quality image location. The sparse representation prior states that the degraded input image, if correctly restored, will have a good sparse representation in terms of the dictionary constructed from the reference image. By iteratively solving the image restoration in pursuit of the sparest representation, our method can achieve simultaneous restoration and location. Based on such a sparse representation prior, we demonstrate that the image restoration task and the location task can benefit greatly from each other. Extensive experiments on real scene images with Gaussian blur are carried out and our joint model outperforms the conventional methods of treating the two tasks independently.

The compression and storage method of the same kind of medical images: DPCM

NASA Astrophysics Data System (ADS)

Zhao, Xiuying; Wei, Jingyuan; Zhai, Linpei; Liu, Hong

2006-09-01

Medical imaging has started to take advantage of digital technology, opening the way for advanced medical imaging and teleradiology. Medical images, however, require large amounts of memory. At over 1 million bytes per image, a typical hospital needs a staggering amount of memory storage (over one trillion bytes per year), and transmitting an image over a network (even the promised superhighway) could take minutes--too slow for interactive teleradiology. This calls for image compression to reduce significantly the amount of data needed to represent an image. Several compression techniques with different compression ratio have been developed. However, the lossless techniques, which allow for perfect reconstruction of the original images, yield modest compression ratio, while the techniques that yield higher compression ratio are lossy, that is, the original image is reconstructed only approximately. Medical imaging poses the great challenge of having compression algorithms that are lossless (for diagnostic and legal reasons) and yet have high compression ratio for reduced storage and transmission time. To meet this challenge, we are developing and studying some compression schemes, which are either strictly lossless or diagnostically lossless, taking advantage of the peculiarities of medical images and of the medical practice. In order to increase the Signal to Noise Ratio (SNR) by exploitation of correlations within the source signal, a method of combining differential pulse code modulation (DPCM) is presented.

A review of biomechanically informed breast image registration

NASA Astrophysics Data System (ADS)

Hipwell, John H.; Vavourakis, Vasileios; Han, Lianghao; Mertzanidou, Thomy; Eiben, Björn; Hawkes, David J.

2016-01-01

Breast radiology encompasses the full range of imaging modalities from routine imaging via x-ray mammography, magnetic resonance imaging and ultrasound (both two- and three-dimensional), to more recent technologies such as digital breast tomosynthesis, and dedicated breast imaging systems for positron emission mammography and ultrasound tomography. In addition new and experimental modalities, such as Photoacoustics, Near Infrared Spectroscopy and Electrical Impedance Tomography etc, are emerging. The breast is a highly deformable structure however, and this greatly complicates visual comparison of imaging modalities for the purposes of breast screening, cancer diagnosis (including image guided biopsy), tumour staging, treatment monitoring, surgical planning and simulation of the effects of surgery and wound healing etc. Due primarily to the challenges posed by these gross, non-rigid deformations, development of automated methods which enable registration, and hence fusion, of information within and across breast imaging modalities, and between the images and the physical space of the breast during interventions, remains an active research field which has yet to translate suitable methods into clinical practice. This review describes current research in the field of breast biomechanical modelling and identifies relevant publications where the resulting models have been incorporated into breast image registration and simulation algorithms. Despite these developments there remain a number of issues that limit clinical application of biomechanical modelling. These include the accuracy of constitutive modelling, implementation of representative boundary conditions, failure to meet clinically acceptable levels of computational cost, challenges associated with automating patient-specific model generation (i.e. robust image segmentation and mesh generation) and the complexity of applying biomechanical modelling methods in routine clinical practice.

A survey on object detection in optical remote sensing images

NASA Astrophysics Data System (ADS)

Cheng, Gong; Han, Junwei

2016-07-01

Object detection in optical remote sensing images, being a fundamental but challenging problem in the field of aerial and satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. While enormous methods exist, a deep review of the literature concerning generic object detection is still lacking. This paper aims to provide a review of the recent progress in this field. Different from several previously published surveys that focus on a specific object class such as building and road, we concentrate on more generic object categories including, but are not limited to, road, building, tree, vehicle, ship, airport, urban-area. Covering about 270 publications we survey (1) template matching-based object detection methods, (2) knowledge-based object detection methods, (3) object-based image analysis (OBIA)-based object detection methods, (4) machine learning-based object detection methods, and (5) five publicly available datasets and three standard evaluation metrics. We also discuss the challenges of current studies and propose two promising research directions, namely deep learning-based feature representation and weakly supervised learning-based geospatial object detection. It is our hope that this survey will be beneficial for the researchers to have better understanding of this research field.

Time-of-Flight Microwave Camera.

PubMed

Charvat, Gregory; Temme, Andrew; Feigin, Micha; Raskar, Ramesh

2015-10-05

Microwaves can penetrate many obstructions that are opaque at visible wavelengths, however microwave imaging is challenging due to resolution limits associated with relatively small apertures and unrecoverable "stealth" regions due to the specularity of most objects at microwave frequencies. We demonstrate a multispectral time-of-flight microwave imaging system which overcomes these challenges with a large passive aperture to improve lateral resolution, multiple illumination points with a data fusion method to reduce stealth regions, and a frequency modulated continuous wave (FMCW) receiver to achieve depth resolution. The camera captures images with a resolution of 1.5 degrees, multispectral images across the X frequency band (8 GHz-12 GHz), and a time resolution of 200 ps (6 cm optical path in free space). Images are taken of objects in free space as well as behind drywall and plywood. This architecture allows "camera-like" behavior from a microwave imaging system and is practical for imaging everyday objects in the microwave spectrum.

Spinal cord grey matter segmentation challenge.

PubMed

Prados, Ferran; Ashburner, John; Blaiotta, Claudia; Brosch, Tom; Carballido-Gamio, Julio; Cardoso, Manuel Jorge; Conrad, Benjamin N; Datta, Esha; Dávid, Gergely; Leener, Benjamin De; Dupont, Sara M; Freund, Patrick; Wheeler-Kingshott, Claudia A M Gandini; Grussu, Francesco; Henry, Roland; Landman, Bennett A; Ljungberg, Emil; Lyttle, Bailey; Ourselin, Sebastien; Papinutto, Nico; Saporito, Salvatore; Schlaeger, Regina; Smith, Seth A; Summers, Paul; Tam, Roger; Yiannakas, Marios C; Zhu, Alyssa; Cohen-Adad, Julien

2017-05-15

An important image processing step in spinal cord magnetic resonance imaging is the ability to reliably and accurately segment grey and white matter for tissue specific analysis. There are several semi- or fully-automated segmentation methods for cervical cord cross-sectional area measurement with an excellent performance close or equal to the manual segmentation. However, grey matter segmentation is still challenging due to small cross-sectional size and shape, and active research is being conducted by several groups around the world in this field. Therefore a grey matter spinal cord segmentation challenge was organised to test different capabilities of various methods using the same multi-centre and multi-vendor dataset acquired with distinct 3D gradient-echo sequences. This challenge aimed to characterize the state-of-the-art in the field as well as identifying new opportunities for future improvements. Six different spinal cord grey matter segmentation methods developed independently by various research groups across the world and their performance were compared to manual segmentation outcomes, the present gold-standard. All algorithms provided good overall results for detecting the grey matter butterfly, albeit with variable performance in certain quality-of-segmentation metrics. The data have been made publicly available and the challenge web site remains open to new submissions. No modifications were introduced to any of the presented methods as a result of this challenge for the purposes of this publication. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Improved image alignment method in application to X-ray images and biological images.

PubMed

Wang, Ching-Wei; Chen, Hsiang-Chou

2013-08-01

Alignment of medical images is a vital component of a large number of applications throughout the clinical track of events; not only within clinical diagnostic settings, but prominently so in the area of planning, consummation and evaluation of surgical and radiotherapeutical procedures. However, image registration of medical images is challenging because of variations on data appearance, imaging artifacts and complex data deformation problems. Hence, the aim of this study is to develop a robust image alignment method for medical images. An improved image registration method is proposed, and the method is evaluated with two types of medical data, including biological microscopic tissue images and dental X-ray images and compared with five state-of-the-art image registration techniques. The experimental results show that the presented method consistently performs well on both types of medical images, achieving 88.44 and 88.93% averaged registration accuracies for biological tissue images and X-ray images, respectively, and outperforms the benchmark methods. Based on the Tukey's honestly significant difference test and Fisher's least square difference test tests, the presented method performs significantly better than all existing methods (P ≤ 0.001) for tissue image alignment, and for the X-ray image registration, the proposed method performs significantly better than the two benchmark b-spline approaches (P < 0.001). The software implementation of the presented method and the data used in this study are made publicly available for scientific communities to use (http://www-o.ntust.edu.tw/∼cweiwang/ImprovedImageRegistration/). cweiwang@mail.ntust.edu.tw.

Blind technique using blocking artifacts and entropy of histograms for image tampering detection

NASA Astrophysics Data System (ADS)

Manu, V. T.; Mehtre, B. M.

2017-06-01

The tremendous technological advancements in recent times has enabled people to create, edit and circulate images easily than ever before. As a result of this, ensuring the integrity and authenticity of the images has become challenging. Malicious editing of images to deceive the viewer is referred to as image tampering. A widely used image tampering technique is image splicing or compositing, in which regions from different images are copied and pasted. In this paper, we propose a tamper detection method utilizing the blocking and blur artifacts which are the footprints of splicing. The classification of images as tampered or not, is done based on the standard deviations of the entropy histograms and block discrete cosine transformations. We can detect the exact boundaries of the tampered area in the image, if the image is classified as tampered. Experimental results on publicly available image tampering datasets show that the proposed method outperforms the existing methods in terms of accuracy.

WE-A-BRD-01: MR Imaging for Treatment Planning: What Every Physicist Should Know

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

McGee, K.

2015-06-15

Ever since its introduction as a diagnostic imaging modality over 30 years ago, the radiation therapy community has acknowledged the utility of MR imaging as a tool for not only improved visualization of the target volume but also for demarcation of adjacent organs at risk. However, the adaptation of MR imaging in radiation oncology has, until recently been slow due in large part to the inability to image radiation therapy patients in their treatment position. With the introduction of so-called wide bore high field MR scanners, multi element flexible receive only RF coils, high performance imaging gradients and a rangemore » of volumetric imaging sequences it is now possible to obtain both high resolution and high signal-to-noise ratio images of in-treatment radiation therapy patients within clinically feasible imaging times. As a Result, there is renewed interest in the use of MR imaging for radiation oncology treatment planning that is being translated into physical siting and integration of these systems into radiation oncology departments. As MR imaging expands into the radiation oncology domain there is a significant and unmet need for radiation therapy physicists to become educated regarding the strengths, limitations and technical challenges associated with MR imaging. The purpose of this presentation is to address this need by providing an educational overview of the techniques and challenges associated with MR imaging of patients for radiation therapy treatment planning. As such this presentation will: 1) describe the fundamental differences between imaging of patients for diagnostic and therapeutic purposes (i.e. radiation therapy planning), 2) describe most commonly used imaging sequences and contrasts for identification of disease for radiation planning, 3) identify the most common sources of image distortion and techniques to reduce their effect on spatial fidelity of the MR data, 4) describe the effects of motion and methods to quantify/correct it, and 5) identify emergent techniques for performing MR only treatment simulation. Upon completion attendees will have a working understanding of the basic methodologies associated with MR imaging in radiation oncology, the unique technical challenges imposed by MR imaging in the treatment position and techniques to address these. Learning Objectives: 1. Understand the differences between MR imaging for diagnostic imaging and for radiation therapy planning. 2. Identify the most common sources of distortion and artifacts and simple methods to correct them. 3. Understand the challenges with MR imaging in the therapy treatment position and appropriate techniques to address them.« less

Deep learning methods for CT image-domain metal artifact reduction

NASA Astrophysics Data System (ADS)

Gjesteby, Lars; Yang, Qingsong; Xi, Yan; Shan, Hongming; Claus, Bernhard; Jin, Yannan; De Man, Bruno; Wang, Ge

2017-09-01

Artifacts resulting from metal objects have been a persistent problem in CT images over the last four decades. A common approach to overcome their effects is to replace corrupt projection data with values synthesized from an interpolation scheme or by reprojection of a prior image. State-of-the-art correction methods, such as the interpolation- and normalization-based algorithm NMAR, often do not produce clinically satisfactory results. Residual image artifacts remain in challenging cases and even new artifacts can be introduced by the interpolation scheme. Metal artifacts continue to be a major impediment, particularly in radiation and proton therapy planning as well as orthopedic imaging. A new solution to the long-standing metal artifact reduction (MAR) problem is deep learning, which has been successfully applied to medical image processing and analysis tasks. In this study, we combine a convolutional neural network (CNN) with the state-of-the-art NMAR algorithm to reduce metal streaks in critical image regions. Training data was synthesized from CT simulation scans of a phantom derived from real patient images. The CNN is able to map metal-corrupted images to artifact-free monoenergetic images to achieve additional correction on top of NMAR for improved image quality. Our results indicate that deep learning is a novel tool to address CT reconstruction challenges, and may enable more accurate tumor volume estimation for radiation therapy planning.

Phase retrieval by coherent modulation imaging.

PubMed

Zhang, Fucai; Chen, Bo; Morrison, Graeme R; Vila-Comamala, Joan; Guizar-Sicairos, Manuel; Robinson, Ian K

2016-11-18

Phase retrieval is a long-standing problem in imaging when only the intensity of the wavefield can be recorded. Coherent diffraction imaging is a lensless technique that uses iterative algorithms to recover amplitude and phase contrast images from diffraction intensity data. For general samples, phase retrieval from a single-diffraction pattern has been an algorithmic and experimental challenge. Here we report a method of phase retrieval that uses a known modulation of the sample exit wave. This coherent modulation imaging method removes inherent ambiguities of coherent diffraction imaging and uses a reliable, rapidly converging iterative algorithm involving three planes. It works for extended samples, does not require tight support for convergence and relaxes dynamic range requirements on the detector. Coherent modulation imaging provides a robust method for imaging in materials and biological science, while its single-shot capability will benefit the investigation of dynamical processes with pulsed sources, such as X-ray free-electron lasers.

A transversal approach for patch-based label fusion via matrix completion

PubMed Central

Sanroma, Gerard; Wu, Guorong; Gao, Yaozong; Thung, Kim-Han; Guo, Yanrong; Shen, Dinggang

2015-01-01

Recently, multi-atlas patch-based label fusion has received an increasing interest in the medical image segmentation field. After warping the anatomical labels from the atlas images to the target image by registration, label fusion is the key step to determine the latent label for each target image point. Two popular types of patch-based label fusion approaches are (1) reconstruction-based approaches that compute the target labels as a weighted average of atlas labels, where the weights are derived by reconstructing the target image patch using the atlas image patches; and (2) classification-based approaches that determine the target label as a mapping of the target image patch, where the mapping function is often learned using the atlas image patches and their corresponding labels. Both approaches have their advantages and limitations. In this paper, we propose a novel patch-based label fusion method to combine the above two types of approaches via matrix completion (and hence, we call it transversal). As we will show, our method overcomes the individual limitations of both reconstruction-based and classification-based approaches. Since the labeling confidences may vary across the target image points, we further propose a sequential labeling framework that first labels the highly confident points and then gradually labels more challenging points in an iterative manner, guided by the label information determined in the previous iterations. We demonstrate the performance of our novel label fusion method in segmenting the hippocampus in the ADNI dataset, subcortical and limbic structures in the LONI dataset, and mid-brain structures in the SATA dataset. We achieve more accurate segmentation results than both reconstruction-based and classification-based approaches. Our label fusion method is also ranked 1st in the online SATA Multi-Atlas Segmentation Challenge. PMID:26160394

Phase unwrapping using region-based markov random field model.

PubMed

Dong, Ying; Ji, Jim

2010-01-01

Phase unwrapping is a classical problem in Magnetic Resonance Imaging (MRI), Interferometric Synthetic Aperture Radar and Sonar (InSAR/InSAS), fringe pattern analysis, and spectroscopy. Although many methods have been proposed to address this problem, robust and effective phase unwrapping remains a challenge. This paper presents a novel phase unwrapping method using a region-based Markov Random Field (MRF) model. Specifically, the phase image is segmented into regions within which the phase is not wrapped. Then, the phase image is unwrapped between different regions using an improved Highest Confidence First (HCF) algorithm to optimize the MRF model. The proposed method has desirable theoretical properties as well as an efficient implementation. Simulations and experimental results on MRI images show that the proposed method provides similar or improved phase unwrapping than Phase Unwrapping MAx-flow/min-cut (PUMA) method and ZpM method.

Artificial intelligence in radiology.

PubMed

Hosny, Ahmed; Parmar, Chintan; Quackenbush, John; Schwartz, Lawrence H; Aerts, Hugo J W L

2018-05-17

Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.

A fluorescent imaging technique for quantifying spray deposits on plant leaves

USDA-ARS?s Scientific Manuscript database

Because of the unique characteristics of electrostatically-charged sprays, use of traditional methods to quantify deposition from these sprays has been challenging. A new fluorescent imaging technique was developed to quantify spray deposits from electrostatically-charged sprays on natural plant lea...

Discriminative Structured Dictionary Learning on Grassmann Manifolds and Its Application on Image Restoration.

PubMed

Pan, Han; Jing, Zhongliang; Qiao, Lingfeng; Li, Minzhe

2017-09-25

Image restoration is a difficult and challenging problem in various imaging applications. However, despite of the benefits of a single overcomplete dictionary, there are still several challenges for capturing the geometric structure of image of interest. To more accurately represent the local structures of the underlying signals, we propose a new problem formulation for sparse representation with block-orthogonal constraint. There are three contributions. First, a framework for discriminative structured dictionary learning is proposed, which leads to a smooth manifold structure and quotient search spaces. Second, an alternating minimization scheme is proposed after taking both the cost function and the constraints into account. This is achieved by iteratively alternating between updating the block structure of the dictionary defined on Grassmann manifold and sparsifying the dictionary atoms automatically. Third, Riemannian conjugate gradient is considered to track local subspaces efficiently with a convergence guarantee. Extensive experiments on various datasets demonstrate that the proposed method outperforms the state-of-the-art methods on the removal of mixed Gaussian-impulse noise.

Evaluation of the morphology structure of meibomian glands based on mask dodging method

NASA Astrophysics Data System (ADS)

Yan, Huangping; Zuo, Yingbo; Chen, Yisha; Chen, Yanping

2016-10-01

Low contrast and non-uniform illumination of infrared (IR) meibography images make the detection of meibomian glands challengeable. An improved Mask dodging algorithm is proposed. To overcome the shortage of low contrast using traditional Mask dodging method, a scale factor is used to enhance the image after subtracting background image from an original one. Meibomian glands are detected and the ratio of the meibomian gland area to the measurement area is calculated. The results show that the improved Mask algorithm has ideal dodging effect, which can eliminate non-uniform illumination and improve contrast of meibography images effectively.

Tackling the x-ray cargo inspection challenge using machine learning

NASA Astrophysics Data System (ADS)

Jaccard, Nicolas; Rogers, Thomas W.; Morton, Edward J.; Griffin, Lewis D.

2016-05-01

The current infrastructure for non-intrusive inspection of cargo containers cannot accommodate exploding com-merce volumes and increasingly stringent regulations. There is a pressing need to develop methods to automate parts of the inspection workflow, enabling expert operators to focus on a manageable number of high-risk images. To tackle this challenge, we developed a modular framework for automated X-ray cargo image inspection. Employing state-of-the-art machine learning approaches, including deep learning, we demonstrate high performance for empty container verification and specific threat detection. This work constitutes a significant step towards the partial automation of X-ray cargo image inspection.

Propagation phasor approach for holographic image reconstruction

PubMed Central

Luo, Wei; Zhang, Yibo; Göröcs, Zoltán; Feizi, Alborz; Ozcan, Aydogan

2016-01-01

To achieve high-resolution and wide field-of-view, digital holographic imaging techniques need to tackle two major challenges: phase recovery and spatial undersampling. Previously, these challenges were separately addressed using phase retrieval and pixel super-resolution algorithms, which utilize the diversity of different imaging parameters. Although existing holographic imaging methods can achieve large space-bandwidth-products by performing pixel super-resolution and phase retrieval sequentially, they require large amounts of data, which might be a limitation in high-speed or cost-effective imaging applications. Here we report a propagation phasor approach, which for the first time combines phase retrieval and pixel super-resolution into a unified mathematical framework and enables the synthesis of new holographic image reconstruction methods with significantly improved data efficiency. In this approach, twin image and spatial aliasing signals, along with other digital artifacts, are interpreted as noise terms that are modulated by phasors that analytically depend on the lateral displacement between hologram and sensor planes, sample-to-sensor distance, wavelength, and the illumination angle. Compared to previous holographic reconstruction techniques, this new framework results in five- to seven-fold reduced number of raw measurements, while still achieving a competitive resolution and space-bandwidth-product. We also demonstrated the success of this approach by imaging biological specimens including Papanicolaou and blood smears. PMID:26964671

Histopathological Image Classification using Discriminative Feature-oriented Dictionary Learning

PubMed Central

Vu, Tiep Huu; Mousavi, Hojjat Seyed; Monga, Vishal; Rao, Ganesh; Rao, UK Arvind

2016-01-01

In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structures. In this paper, we propose an automatic feature discovery framework via learning class-specific dictionaries and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific dictionaries such that under a sparsity constraint, the learned dictionaries allow representing a new image sample parsimoniously via the dictionary corresponding to the class identity of the sample. At the same time, the dictionary is designed to be poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian kidney, lung and spleen images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, reveal the merits of our proposal over state-of-the-art alternatives. Moreover, we demonstrate that DFDL exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training is often not available. PMID:26513781

Extended morphological processing: a practical method for automatic spot detection of biological markers from microscopic images.

PubMed

Kimori, Yoshitaka; Baba, Norio; Morone, Nobuhiro

2010-07-08

A reliable extraction technique for resolving multiple spots in light or electron microscopic images is essential in investigations of the spatial distribution and dynamics of specific proteins inside cells and tissues. Currently, automatic spot extraction and characterization in complex microscopic images poses many challenges to conventional image processing methods. A new method to extract closely located, small target spots from biological images is proposed. This method starts with a simple but practical operation based on the extended morphological top-hat transformation to subtract an uneven background. The core of our novel approach is the following: first, the original image is rotated in an arbitrary direction and each rotated image is opened with a single straight line-segment structuring element. Second, the opened images are unified and then subtracted from the original image. To evaluate these procedures, model images of simulated spots with closely located targets were created and the efficacy of our method was compared to that of conventional morphological filtering methods. The results showed the better performance of our method. The spots of real microscope images can be quantified to confirm that the method is applicable in a given practice. Our method achieved effective spot extraction under various image conditions, including aggregated target spots, poor signal-to-noise ratio, and large variations in the background intensity. Furthermore, it has no restrictions with respect to the shape of the extracted spots. The features of our method allow its broad application in biological and biomedical image information analysis.

CT Image Sequence Restoration Based on Sparse and Low-Rank Decomposition

PubMed Central

Gou, Shuiping; Wang, Yueyue; Wang, Zhilong; Peng, Yong; Zhang, Xiaopeng; Jiao, Licheng; Wu, Jianshe

2013-01-01

Blurry organ boundaries and soft tissue structures present a major challenge in biomedical image restoration. In this paper, we propose a low-rank decomposition-based method for computed tomography (CT) image sequence restoration, where the CT image sequence is decomposed into a sparse component and a low-rank component. A new point spread function of Weiner filter is employed to efficiently remove blur in the sparse component; a wiener filtering with the Gaussian PSF is used to recover the average image of the low-rank component. And then we get the recovered CT image sequence by combining the recovery low-rank image with all recovery sparse image sequence. Our method achieves restoration results with higher contrast, sharper organ boundaries and richer soft tissue structure information, compared with existing CT image restoration methods. The robustness of our method was assessed with numerical experiments using three different low-rank models: Robust Principle Component Analysis (RPCA), Linearized Alternating Direction Method with Adaptive Penalty (LADMAP) and Go Decomposition (GoDec). Experimental results demonstrated that the RPCA model was the most suitable for the small noise CT images whereas the GoDec model was the best for the large noisy CT images. PMID:24023764

Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images.

PubMed

Pereira, Sergio; Pinto, Adriano; Alves, Victor; Silva, Carlos A

2016-05-01

Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of oncological patients. Magnetic resonance imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice. So, automatic and reliable segmentation methods are required; however, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem. In this paper, we propose an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3 ×3 kernels. The use of small kernels allows designing a deeper architecture, besides having a positive effect against overfitting, given the fewer number of weights in the network. We also investigated the use of intensity normalization as a pre-processing step, which though not common in CNN-based segmentation methods, proved together with data augmentation to be very effective for brain tumor segmentation in MRI images. Our proposal was validated in the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013), obtaining simultaneously the first position for the complete, core, and enhancing regions in Dice Similarity Coefficient metric (0.88, 0.83, 0.77) for the Challenge data set. Also, it obtained the overall first position by the online evaluation platform. We also participated in the on-site BRATS 2015 Challenge using the same model, obtaining the second place, with Dice Similarity Coefficient metric of 0.78, 0.65, and 0.75 for the complete, core, and enhancing regions, respectively.

Correlated Imaging – A Grand Challenge in Chemical Analysis

PubMed Central

Masyuko, Rachel; Lanni, Eric; Sweedler, Jonathan V.; Bohn, Paul W.

2013-01-01

Correlated chemical imaging is an emerging strategy for acquisition of images by combining information from multiplexed measurement platforms to track, visualize, and interpret in situ changes in the structure, organization, and activities of interesting chemical systems, frequently spanning multiple decades in space and time. Acquiring and correlating information from complementary imaging experiments has the potential to expose complex chemical behavior in ways that are simply not available from single methods applied in isolation, thereby greatly amplifying the information gathering power of imaging experiments. However, in order to correlate image information across platforms, a number of issues must be addressed. First, signals are obtained from disparate experiments with fundamentally different figures of merit, including pixel size, spatial resolution, dynamic range, and acquisition rates. In addition, images are often acquired on different instruments in different locations, so the sample must be registered spatially so that the same area of the sample landscape is addressed. The signals acquired must be correlated in both spatial and temporal domains, and the resulting information has to be presented in a way that is readily understood. These requirements pose special challenges for image cross-correlation that go well beyond those posed in single technique imaging approaches. The special opportunities and challenges that attend correlated imaging are explored by specific reference to correlated mass spectrometric and Raman imaging, a topic of substantial and growing interest. PMID:23431559

Region specific optimization of continuous linear attenuation coefficients based on UTE (RESOLUTE): application to PET/MR brain imaging

NASA Astrophysics Data System (ADS)

Ladefoged, Claes N.; Benoit, Didier; Law, Ian; Holm, Søren; Kjær, Andreas; Højgaard, Liselotte; Hansen, Adam E.; Andersen, Flemming L.

2015-10-01

The reconstruction of PET brain data in a PET/MR hybrid scanner is challenging in the absence of transmission sources, where MR images are used for MR-based attenuation correction (MR-AC). The main challenge of MR-AC is to separate bone and air, as neither have a signal in traditional MR images, and to assign the correct linear attenuation coefficient to bone. The ultra-short echo time (UTE) MR sequence was proposed as a basis for MR-AC as this sequence shows a small signal in bone. The purpose of this study was to develop a new clinically feasible MR-AC method with patient specific continuous-valued linear attenuation coefficients in bone that provides accurate reconstructed PET image data. A total of 164 [18F]FDG PET/MR patients were included in this study, of which 10 were used for training. MR-AC was based on either standard CT (reference), UTE or our method (RESOLUTE). The reconstructed PET images were evaluated in the whole brain, as well as regionally in the brain using a ROI-based analysis. Our method segments air, brain, cerebral spinal fluid, and soft tissue voxels on the unprocessed UTE TE images, and uses a mapping of R2* values to CT Hounsfield Units (HU) to measure the density in bone voxels. The average error of our method in the brain was 0.1% and less than 1.2% in any region of the brain. On average 95% of the brain was within  ±10% of PETCT, compared to 72% when using UTE. The proposed method is clinically feasible, reducing both the global and local errors on the reconstructed PET images, as well as limiting the number and extent of the outliers.

Deterministic object tracking using Gaussian ringlet and directional edge features

NASA Astrophysics Data System (ADS)

Krieger, Evan W.; Sidike, Paheding; Aspiras, Theus; Asari, Vijayan K.

2017-10-01

Challenges currently existing for intensity-based histogram feature tracking methods in wide area motion imagery (WAMI) data include object structural information distortions, background variations, and object scale change. These issues are caused by different pavement or ground types and from changing the sensor or altitude. All of these challenges need to be overcome in order to have a robust object tracker, while attaining a computation time appropriate for real-time processing. To achieve this, we present a novel method, Directional Ringlet Intensity Feature Transform (DRIFT), which employs Kirsch kernel filtering for edge features and a ringlet feature mapping for rotational invariance. The method also includes an automatic scale change component to obtain accurate object boundaries and improvements for lowering computation times. We evaluated the DRIFT algorithm on two challenging WAMI datasets, namely Columbus Large Image Format (CLIF) and Large Area Image Recorder (LAIR), to evaluate its robustness and efficiency. Additional evaluations on general tracking video sequences are performed using the Visual Tracker Benchmark and Visual Object Tracking 2014 databases to demonstrate the algorithms ability with additional challenges in long complex sequences including scale change. Experimental results show that the proposed approach yields competitive results compared to state-of-the-art object tracking methods on the testing datasets.

A singular K-space model for fast reconstruction of magnetic resonance images from undersampled data.

PubMed

Luo, Jianhua; Mou, Zhiying; Qin, Binjie; Li, Wanqing; Ogunbona, Philip; Robini, Marc C; Zhu, Yuemin

2018-07-01

Reconstructing magnetic resonance images from undersampled k-space data is a challenging problem. This paper introduces a novel method of image reconstruction from undersampled k-space data based on the concept of singularizing operators and a novel singular k-space model. Exploring the sparsity of an image in the k-space, the singular k-space model (SKM) is proposed in terms of the k-space functions of a singularizing operator. The singularizing operator is constructed by combining basic difference operators. An algorithm is developed to reliably estimate the model parameters from undersampled k-space data. The estimated parameters are then used to recover the missing k-space data through the model, subsequently achieving high-quality reconstruction of the image using inverse Fourier transform. Experiments on physical phantom and real brain MR images have shown that the proposed SKM method constantly outperforms the popular total variation (TV) and the classical zero-filling (ZF) methods regardless of the undersampling rates, the noise levels, and the image structures. For the same objective quality of the reconstructed images, the proposed method requires much less k-space data than the TV method. The SKM method is an effective method for fast MRI reconstruction from the undersampled k-space data. Graphical abstract Two Real Images and their sparsified images by singularizing operator.

Automatic registration of panoramic image sequence and mobile laser scanning data using semantic features

NASA Astrophysics Data System (ADS)

Li, Jianping; Yang, Bisheng; Chen, Chi; Huang, Ronggang; Dong, Zhen; Xiao, Wen

2018-02-01

Inaccurate exterior orientation parameters (EoPs) between sensors obtained by pre-calibration leads to failure of registration between panoramic image sequence and mobile laser scanning data. To address this challenge, this paper proposes an automatic registration method based on semantic features extracted from panoramic images and point clouds. Firstly, accurate rotation parameters between the panoramic camera and the laser scanner are estimated using GPS and IMU aided structure from motion (SfM). The initial EoPs of panoramic images are obtained at the same time. Secondly, vehicles in panoramic images are extracted by the Faster-RCNN as candidate primitives to be matched with potential corresponding primitives in point clouds according to the initial EoPs. Finally, translation between the panoramic camera and the laser scanner is refined by maximizing the overlapping area of corresponding primitive pairs based on the Particle Swarm Optimization (PSO), resulting in a finer registration between panoramic image sequences and point clouds. Two challenging urban scenes were experimented to assess the proposed method, and the final registration errors of these two scenes were both less than three pixels, which demonstrates a high level of automation, robustness and accuracy.

An automated method for accurate vessel segmentation.

PubMed

Yang, Xin; Liu, Chaoyue; Le Minh, Hung; Wang, Zhiwei; Chien, Aichi; Cheng, Kwang-Ting Tim

2017-05-07

Vessel segmentation is a critical task for various medical applications, such as diagnosis assistance of diabetic retinopathy, quantification of cerebral aneurysm's growth, and guiding surgery in neurosurgical procedures. Despite technology advances in image segmentation, existing methods still suffer from low accuracy for vessel segmentation in the two challenging while common scenarios in clinical usage: (1) regions with a low signal-to-noise-ratio (SNR), and (2) at vessel boundaries disturbed by adjacent non-vessel pixels. In this paper, we present an automated system which can achieve highly accurate vessel segmentation for both 2D and 3D images even under these challenging scenarios. Three key contributions achieved by our system are: (1) a progressive contrast enhancement method to adaptively enhance contrast of challenging pixels that were otherwise indistinguishable, (2) a boundary refinement method to effectively improve segmentation accuracy at vessel borders based on Canny edge detection, and (3) a content-aware region-of-interests (ROI) adjustment method to automatically determine the locations and sizes of ROIs which contain ambiguous pixels and demand further verification. Extensive evaluation of our method is conducted on both 2D and 3D datasets. On a public 2D retinal dataset (named DRIVE (Staal 2004 IEEE Trans. Med. Imaging 23 501-9)) and our 2D clinical cerebral dataset, our approach achieves superior performance to the state-of-the-art methods including a vesselness based method (Frangi 1998 Int. Conf. on Medical Image Computing and Computer-Assisted Intervention) and an optimally oriented flux (OOF) based method (Law and Chung 2008 European Conf. on Computer Vision). An evaluation on 11 clinical 3D CTA cerebral datasets shows that our method can achieve 94% average accuracy with respect to the manual segmentation reference, which is 23% to 33% better than the five baseline methods (Yushkevich 2006 Neuroimage 31 1116-28; Law and Chung 2008 European Conf. on Computer Vision; Law and Chung 2009 IEEE Trans. Image Process. 18 596-612; Wang 2015 J. Neurosci. Methods 241 30-6) with manually optimized parameters. Our system has also been applied clinically for cerebral aneurysm development analysis. Experimental results on 10 patients' data, with two 3D CT scans per patient, show that our system's automatic diagnosis outcomes are consistent with clinicians' manual measurements.

An automated method for accurate vessel segmentation

NASA Astrophysics Data System (ADS)

Yang, Xin; Liu, Chaoyue; Le Minh, Hung; Wang, Zhiwei; Chien, Aichi; (Tim Cheng, Kwang-Ting

2017-05-01

Vessel segmentation is a critical task for various medical applications, such as diagnosis assistance of diabetic retinopathy, quantification of cerebral aneurysm’s growth, and guiding surgery in neurosurgical procedures. Despite technology advances in image segmentation, existing methods still suffer from low accuracy for vessel segmentation in the two challenging while common scenarios in clinical usage: (1) regions with a low signal-to-noise-ratio (SNR), and (2) at vessel boundaries disturbed by adjacent non-vessel pixels. In this paper, we present an automated system which can achieve highly accurate vessel segmentation for both 2D and 3D images even under these challenging scenarios. Three key contributions achieved by our system are: (1) a progressive contrast enhancement method to adaptively enhance contrast of challenging pixels that were otherwise indistinguishable, (2) a boundary refinement method to effectively improve segmentation accuracy at vessel borders based on Canny edge detection, and (3) a content-aware region-of-interests (ROI) adjustment method to automatically determine the locations and sizes of ROIs which contain ambiguous pixels and demand further verification. Extensive evaluation of our method is conducted on both 2D and 3D datasets. On a public 2D retinal dataset (named DRIVE (Staal 2004 IEEE Trans. Med. Imaging 23 501-9)) and our 2D clinical cerebral dataset, our approach achieves superior performance to the state-of-the-art methods including a vesselness based method (Frangi 1998 Int. Conf. on Medical Image Computing and Computer-Assisted Intervention) and an optimally oriented flux (OOF) based method (Law and Chung 2008 European Conf. on Computer Vision). An evaluation on 11 clinical 3D CTA cerebral datasets shows that our method can achieve 94% average accuracy with respect to the manual segmentation reference, which is 23% to 33% better than the five baseline methods (Yushkevich 2006 Neuroimage 31 1116-28; Law and Chung 2008 European Conf. on Computer Vision; Law and Chung 2009 IEEE Trans. Image Process. 18 596-612; Wang 2015 J. Neurosci. Methods 241 30-6) with manually optimized parameters. Our system has also been applied clinically for cerebral aneurysm development analysis. Experimental results on 10 patients’ data, with two 3D CT scans per patient, show that our system’s automatic diagnosis outcomes are consistent with clinicians’ manual measurements.

A state-of-the-art review on segmentation algorithms in intravascular ultrasound (IVUS) images.

PubMed

Katouzian, Amin; Angelini, Elsa D; Carlier, Stéphane G; Suri, Jasjit S; Navab, Nassir; Laine, Andrew F

2012-09-01

Over the past two decades, intravascular ultrasound (IVUS) image segmentation has remained a challenge for researchers while the use of this imaging modality is rapidly growing in catheterization procedures and in research studies. IVUS provides cross-sectional grayscale images of the arterial wall and the extent of atherosclerotic plaques with high spatial resolution in real time. In this paper, we review recently developed image processing methods for the detection of media-adventitia and luminal borders in IVUS images acquired with different transducers operating at frequencies ranging from 20 to 45 MHz. We discuss methodological challenges, lack of diversity in reported datasets, and weaknesses of quantification metrics that make IVUS segmentation still an open problem despite all efforts. In conclusion, we call for a common reference database, validation metrics, and ground-truth definition with which new and existing algorithms could be benchmarked.

Morphology supporting function: attenuation correction for SPECT/CT, PET/CT, and PET/MR imaging

PubMed Central

Lee, Tzu C.; Alessio, Adam M.; Miyaoka, Robert M.; Kinahan, Paul E.

2017-01-01

Both SPECT, and in particular PET, are unique in medical imaging for their high sensitivity and direct link to a physical quantity, i.e. radiotracer concentration. This gives PET and SPECT imaging unique capabilities for accurately monitoring disease activity for the purposes of clinical management or therapy development. However, to achieve a direct quantitative connection between the underlying radiotracer concentration and the reconstructed image values several confounding physical effects have to be estimated, notably photon attenuation and scatter. With the advent of dual-modality SPECT/CT, PET/CT, and PET/MR scanners, the complementary CT or MR image data can enable these corrections, although there are unique challenges for each combination. This review covers the basic physics underlying photon attenuation and scatter and summarizes technical considerations for multimodal imaging with regard to PET and SPECT quantification and methods to address the challenges for each multimodal combination. PMID:26576737

Automatic annotation of histopathological images using a latent topic model based on non-negative matrix factorization

PubMed Central

Cruz-Roa, Angel; Díaz, Gloria; Romero, Eduardo; González, Fabio A.

2011-01-01

Histopathological images are an important resource for clinical diagnosis and biomedical research. From an image understanding point of view, the automatic annotation of these images is a challenging problem. This paper presents a new method for automatic histopathological image annotation based on three complementary strategies, first, a part-based image representation, called the bag of features, which takes advantage of the natural redundancy of histopathological images for capturing the fundamental patterns of biological structures, second, a latent topic model, based on non-negative matrix factorization, which captures the high-level visual patterns hidden in the image, and, third, a probabilistic annotation model that links visual appearance of morphological and architectural features associated to 10 histopathological image annotations. The method was evaluated using 1,604 annotated images of skin tissues, which included normal and pathological architectural and morphological features, obtaining a recall of 74% and a precision of 50%, which improved a baseline annotation method based on support vector machines in a 64% and 24%, respectively. PMID:22811960

Connectivity inference from neural recording data: Challenges, mathematical bases and research directions.

PubMed

Magrans de Abril, Ildefons; Yoshimoto, Junichiro; Doya, Kenji

2018-06-01

This article presents a review of computational methods for connectivity inference from neural activity data derived from multi-electrode recordings or fluorescence imaging. We first identify biophysical and technical challenges in connectivity inference along the data processing pipeline. We then review connectivity inference methods based on two major mathematical foundations, namely, descriptive model-free approaches and generative model-based approaches. We investigate representative studies in both categories and clarify which challenges have been addressed by which method. We further identify critical open issues and possible research directions. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Statistical iterative reconstruction to improve image quality for digital breast tomosynthesis

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

Xu, Shiyu, E-mail: shiyu.xu@gmail.com; Chen, Ying, E-mail: adachen@siu.edu; Lu, Jianping

2015-09-15

Purpose: Digital breast tomosynthesis (DBT) is a novel modality with the potential to improve early detection of breast cancer by providing three-dimensional (3D) imaging with a low radiation dose. 3D image reconstruction presents some challenges: cone-beam and flat-panel geometry, and highly incomplete sampling. A promising means to overcome these challenges is statistical iterative reconstruction (IR), since it provides the flexibility of accurate physics modeling and a general description of system geometry. The authors’ goal was to develop techniques for applying statistical IR to tomosynthesis imaging data. Methods: These techniques include the following: a physics model with a local voxel-pair basedmore » prior with flexible parameters to fine-tune image quality; a precomputed parameter λ in the prior, to remove data dependence and to achieve a uniform resolution property; an effective ray-driven technique to compute the forward and backprojection; and an oversampled, ray-driven method to perform high resolution reconstruction with a practical region-of-interest technique. To assess the performance of these techniques, the authors acquired phantom data on the stationary DBT prototype system. To solve the estimation problem, the authors proposed an optimization-transfer based algorithm framework that potentially allows fewer iterations to achieve an acceptably converged reconstruction. Results: IR improved the detectability of low-contrast and small microcalcifications, reduced cross-plane artifacts, improved spatial resolution, and lowered noise in reconstructed images. Conclusions: Although the computational load remains a significant challenge for practical development, the superior image quality provided by statistical IR, combined with advancing computational techniques, may bring benefits to screening, diagnostics, and intraoperative imaging in clinical applications.« less

Dense deconvolution net: Multi path fusion and dense deconvolution for high resolution skin lesion segmentation.

PubMed

He, Xinzi; Yu, Zhen; Wang, Tianfu; Lei, Baiying; Shi, Yiyan

2018-01-01

Dermoscopy imaging has been a routine examination approach for skin lesion diagnosis. Accurate segmentation is the first step for automatic dermoscopy image assessment. The main challenges for skin lesion segmentation are numerous variations in viewpoint and scale of skin lesion region. To handle these challenges, we propose a novel skin lesion segmentation network via a very deep dense deconvolution network based on dermoscopic images. Specifically, the deep dense layer and generic multi-path Deep RefineNet are combined to improve the segmentation performance. The deep representation of all available layers is aggregated to form the global feature maps using skip connection. Also, the dense deconvolution layer is leveraged to capture diverse appearance features via the contextual information. Finally, we apply the dense deconvolution layer to smooth segmentation maps and obtain final high-resolution output. Our proposed method shows the superiority over the state-of-the-art approaches based on the public available 2016 and 2017 skin lesion challenge dataset and achieves the accuracy of 96.0% and 93.9%, which obtained a 6.0% and 1.2% increase over the traditional method, respectively. By utilizing Dense Deconvolution Net, the average time for processing one testing images with our proposed framework was 0.253 s.

Harnessing the potential of noninvasive in vivo preclinical imaging of the immune system: challenges and prospects.

PubMed

Diken, Mustafa; Pektor, Stefanie; Miederer, Matthias

2016-10-01

Preclinical imaging has become a powerful method for investigation of in vivo processes such as pharmacokinetics of therapeutic substances and visualization of physiologic and pathophysiological mechanisms. These are important aspects to understand diseases and develop strategies to modify their progression with pharmacologic interventions. One promising intervention is the application of specifically tailored nanoscale particles that modulate the immune system to generate a tumor targeting immune response. In this complex interaction between immunomodulatory therapies, the immune system and malignant disease, imaging methods are expected to play a key role on the way to generate new therapeutic strategies. Here, we summarize examples which demonstrate the current potential of imaging methods and develop a perspective on the future value of preclinical imaging of the immune system.

Infrared thermal imaging as a method to evaluate heat loss in newborn lambs.

PubMed

Labeur, L; Villiers, G; Small, A H; Hinch, G N; Schmoelzl, S

2017-12-01

Thermal imaging technology has been identified as a potential method for non-invasive study of thermogenesis in the neonatal lamb. In comparison to measurement of the core body temperature, infrared thermography may observe thermal loss and thermogenesis linked to subcutaneous brown fat depots. This study aimed to identify a suitable method to measure heat loss in the neonatal lamb under a cold challenge. During late pregnancy (day 125), ewes were subjected to either shearing (n=15) or mock handling (sham-shorn for 2min mimicking the shearing movements) (n=15). Previous studies have shown an increase in brown adipose tissue deposition in lambs born to ewes shorn during pregnancy and we hypothesized that the shearing treatment would impact thermoregulatory capacities in newborn lambs. Lambs born to control ewes (n=14; CONTROL) and shorn ewes (n=13; SHORN) were subjected to a cold challenge of 1h duration at 4h after birth. During the cold challenge, thermography images were taken every 10min, from above, at a fixed distance from the dorsal midline. On each image, four fixed-size areas were identified (shoulder, mid loin, hips and rump) and the average and maximum temperatures of each recorded. In all lambs, body surface temperature decreased over time. Overall the SHORN lambs appeared to maintain body surface temperature better than CONTROL lambs, while CONTROL lambs appeared to have higher core temperature. At 30min post cold challenge SHORN lambs tended to have higher body surface temperatures than lambs (P=0.0474). Both average and maximum temperatures were highest at the hips. Average temperature was lowest at the shoulder (P<0.05), while maximum temperatures were lowest at both shoulder and rump (P<0.005). These results indicate that lambs born to shorn ewes maintained their radiated body surface temperature better than CONTROL lambs. In conjunction with core temperature changes under cold challenge, this insight will allow us to understand whether increased body surface temperature contributes to increased overall heat loss or whether increased body surface temperature is indeed a mechanism contributing to maintenance of core body temperature under cold challenge conditions. This study has confirmed the utility of infrared thermography images to capture and identify different levels of thermoregulatory capacity in newborn lambs. Copyright © 2017 Elsevier Ltd. All rights reserved.

Mass Spectrometry Imaging Can Distinguish on a Proteomic Level Between Proliferative Nodules Within a Benign Congenital Nevus and Malignant Melanoma.

PubMed

Lazova, Rossitza; Yang, Zhe; El Habr, Constantin; Lim, Young; Choate, Keith Adam; Seeley, Erin H; Caprioli, Richard M; Yangqun, Li

2017-09-01

Histopathological interpretation of proliferative nodules occurring in association with congenital melanocytic nevi can be very challenging due to their similarities with congenital malignant melanoma and malignant melanoma arising in association with congenital nevi. We hereby report a diagnostically challenging case of congenital melanocytic nevus with proliferative nodules and ulcerations, which was originally misdiagnosed as congenital malignant melanoma. Subsequent histopathological examination in consultation by one of the authors (R.L.) and mass spectrometry imaging analysis rendered a diagnosis of congenital melanocytic nevus with proliferative nodules. In this case, mass spectrometry imaging, a novel method capable of distinguishing benign from malignant melanocytic lesions on a proteomic level, was instrumental in making the diagnosis of a benign nevus. We emphasize the importance of this method as an ancillary tool in the diagnosis of difficult melanocytic lesions.

A reference estimator based on composite sensor pattern noise for source device identification

NASA Astrophysics Data System (ADS)

Li, Ruizhe; Li, Chang-Tsun; Guan, Yu

2014-02-01

It has been proved that Sensor Pattern Noise (SPN) can serve as an imaging device fingerprint for source camera identification. Reference SPN estimation is a very important procedure within the framework of this application. Most previous works built reference SPN by averaging the SPNs extracted from 50 images of blue sky. However, this method can be problematic. Firstly, in practice we may face the problem of source camera identification in the absence of the imaging cameras and reference SPNs, which means only natural images with scene details are available for reference SPN estimation rather than blue sky images. It is challenging because the reference SPN can be severely contaminated by image content. Secondly, the number of available reference images sometimes is too few for existing methods to estimate a reliable reference SPN. In fact, existing methods lack consideration of the number of available reference images as they were designed for the datasets with abundant images to estimate the reference SPN. In order to deal with the aforementioned problem, in this work, a novel reference estimator is proposed. Experimental results show that our proposed method achieves better performance than the methods based on the averaged reference SPN, especially when few reference images used.

Review of 3D image data calibration for heterogeneity correction in proton therapy treatment planning.

PubMed

Zhu, Jiahua; Penfold, Scott N

2016-06-01

Correct modelling of the interaction parameters of patient tissues is of vital importance in proton therapy treatment planning because of the large dose gradients associated with the Bragg peak. Different 3D imaging techniques yield different information regarding these interaction parameters. Given the rapidly expanding interest in proton therapy, this review is written to make readers aware of the current challenges in accounting for tissue heterogeneities and the imaging systems that are proposed to tackle these challenges. A summary of the interaction parameters of interest in proton therapy and the current and developmental 3D imaging techniques used in proton therapy treatment planning is given. The different methods to translate the imaging data to the interaction parameters of interest are reviewed and a summary of the implementations in several commercial treatment planning systems is presented.

Applying quantitative benefit-risk analysis to aid regulatory decision making in diagnostic imaging: methods, challenges, and opportunities.

PubMed

Agapova, Maria; Devine, Emily Beth; Bresnahan, Brian W; Higashi, Mitchell K; Garrison, Louis P

2014-09-01

Health agencies making regulatory marketing-authorization decisions use qualitative and quantitative approaches to assess expected benefits and expected risks associated with medical interventions. There is, however, no universal standard approach that regulatory agencies consistently use to conduct benefit-risk assessment (BRA) for pharmaceuticals or medical devices, including for imaging technologies. Economics, health services research, and health outcomes research use quantitative approaches to elicit preferences of stakeholders, identify priorities, and model health conditions and health intervention effects. Challenges to BRA in medical devices are outlined, highlighting additional barriers in radiology. Three quantitative methods--multi-criteria decision analysis, health outcomes modeling and stated-choice survey--are assessed using criteria that are important in balancing benefits and risks of medical devices and imaging technologies. To be useful in regulatory BRA, quantitative methods need to: aggregate multiple benefits and risks, incorporate qualitative considerations, account for uncertainty, and make clear whose preferences/priorities are being used. Each quantitative method performs differently across these criteria and little is known about how BRA estimates and conclusions vary by approach. While no specific quantitative method is likely to be the strongest in all of the important areas, quantitative methods may have a place in BRA of medical devices and radiology. Quantitative BRA approaches have been more widely applied in medicines, with fewer BRAs in devices. Despite substantial differences in characteristics of pharmaceuticals and devices, BRA methods may be as applicable to medical devices and imaging technologies as they are to pharmaceuticals. Further research to guide the development and selection of quantitative BRA methods for medical devices and imaging technologies is needed. Copyright © 2014 AUR. Published by Elsevier Inc. All rights reserved.

Mapping detailed 3D information onto high resolution SAR signatures

NASA Astrophysics Data System (ADS)

Anglberger, H.; Speck, R.

2017-05-01

Due to challenges in the visual interpretation of radar signatures or in the subsequent information extraction, a fusion with other data sources can be beneficial. The most accurate basis for a fusion of any kind of remote sensing data is the mapping of the acquired 2D image space onto the true 3D geometry of the scenery. In the case of radar images this is a challenging task because the coordinate system is based on the measured range which causes ambiguous regions due to layover effects. This paper describes a method that accurately maps the detailed 3D information of a scene to the slantrange-based coordinate system of imaging radars. Due to this mapping all the contributing geometrical parts of one resolution cell can be determined in 3D space. The proposed method is highly efficient, because computationally expensive operations can be directly performed on graphics card hardware. The described approach builds a perfect basis for sophisticated methods to extract data from multiple complimentary sensors like from radar and optical images, especially because true 3D information from whole cities will be available in the near future. The performance of the developed methods will be demonstrated with high resolution radar data acquired by the space-borne SAR-sensor TerraSAR-X.

Noisy Ocular Recognition Based on Three Convolutional Neural Networks.

PubMed

Lee, Min Beom; Hong, Hyung Gil; Park, Kang Ryoung

2017-12-17

In recent years, the iris recognition system has been gaining increasing acceptance for applications such as access control and smartphone security. When the images of the iris are obtained under unconstrained conditions, an issue of undermined quality is caused by optical and motion blur, off-angle view (the user's eyes looking somewhere else, not into the front of the camera), specular reflection (SR) and other factors. Such noisy iris images increase intra-individual variations and, as a result, reduce the accuracy of iris recognition. A typical iris recognition system requires a near-infrared (NIR) illuminator along with an NIR camera, which are larger and more expensive than fingerprint recognition equipment. Hence, many studies have proposed methods of using iris images captured by a visible light camera without the need for an additional illuminator. In this research, we propose a new recognition method for noisy iris and ocular images by using one iris and two periocular regions, based on three convolutional neural networks (CNNs). Experiments were conducted by using the noisy iris challenge evaluation-part II (NICE.II) training dataset (selected from the university of Beira iris (UBIRIS).v2 database), mobile iris challenge evaluation (MICHE) database, and institute of automation of Chinese academy of sciences (CASIA)-Iris-Distance database. As a result, the method proposed by this study outperformed previous methods.

Multimodal inspection in power engineering and building industries: new challenges and solutions

NASA Astrophysics Data System (ADS)

Kujawińska, Małgorzata; Malesa, Marcin; Malowany, Krzysztof

2013-09-01

Recently the demand and number of applications of full-field, optical measurement methods based on noncoherent light sources increased significantly. They include traditional image processing, thermovision, digital image correlation (DIC) and structured light methods. However, there are still numerous challenges connected with implementation of these methods to in-situ, long-term monitoring in industrial, civil engineering and cultural heritage applications, multimodal measurements of a variety of object features or simply adopting instruments to work in hard environmental conditions. In this paper we focus on 3D DIC method and present its enhancements concerning software modifications (new visualization methods and a method for automatic merging of data distributed in time) and hardware improvements. The modified 3D DIC system combined with infrared camera system is applied in many interesting cases: measurements of boiler drum during annealing and of pipelines in heat power stations and monitoring of different building steel struts at construction site and validation of numerical models of large building structures constructed of graded metal plate arches.

Comparison of computer systems and ranking criteria for automatic melanoma detection in dermoscopic images.

PubMed

Møllersen, Kajsa; Zortea, Maciel; Schopf, Thomas R; Kirchesch, Herbert; Godtliebsen, Fred

2017-01-01

Melanoma is the deadliest form of skin cancer, and early detection is crucial for patient survival. Computer systems can assist in melanoma detection, but are not widespread in clinical practice. In 2016, an open challenge in classification of dermoscopic images of skin lesions was announced. A training set of 900 images with corresponding class labels and semi-automatic/manual segmentation masks was released for the challenge. An independent test set of 379 images, of which 75 were of melanomas, was used to rank the participants. This article demonstrates the impact of ranking criteria, segmentation method and classifier, and highlights the clinical perspective. We compare five different measures for diagnostic accuracy by analysing the resulting ranking of the computer systems in the challenge. Choice of performance measure had great impact on the ranking. Systems that were ranked among the top three for one measure, dropped to the bottom half when changing performance measure. Nevus Doctor, a computer system previously developed by the authors, was used to participate in the challenge, and investigate the impact of segmentation and classifier. The diagnostic accuracy when using an automatic versus the semi-automatic/manual segmentation is investigated. The unexpected small impact of segmentation method suggests that improvements of the automatic segmentation method w.r.t. resemblance to semi-automatic/manual segmentation will not improve diagnostic accuracy substantially. A small set of similar classification algorithms are used to investigate the impact of classifier on the diagnostic accuracy. The variability in diagnostic accuracy for different classifier algorithms was larger than the variability for segmentation methods, and suggests a focus for future investigations. From a clinical perspective, the misclassification of a melanoma as benign has far greater cost than the misclassification of a benign lesion. For computer systems to have clinical impact, their performance should be ranked by a high-sensitivity measure.

Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing

PubMed Central

Zhang, Fan; Li, Guojun; Li, Wei; Hu, Wei; Hu, Yuxin

2016-01-01

With the development of synthetic aperture radar (SAR) technologies in recent years, the huge amount of remote sensing data brings challenges for real-time imaging processing. Therefore, high performance computing (HPC) methods have been presented to accelerate SAR imaging, especially the GPU based methods. In the classical GPU based imaging algorithm, GPU is employed to accelerate image processing by massive parallel computing, and CPU is only used to perform the auxiliary work such as data input/output (IO). However, the computing capability of CPU is ignored and underestimated. In this work, a new deep collaborative SAR imaging method based on multiple CPU/GPU is proposed to achieve real-time SAR imaging. Through the proposed tasks partitioning and scheduling strategy, the whole image can be generated with deep collaborative multiple CPU/GPU computing. In the part of CPU parallel imaging, the advanced vector extension (AVX) method is firstly introduced into the multi-core CPU parallel method for higher efficiency. As for the GPU parallel imaging, not only the bottlenecks of memory limitation and frequent data transferring are broken, but also kinds of optimized strategies are applied, such as streaming, parallel pipeline and so on. Experimental results demonstrate that the deep CPU/GPU collaborative imaging method enhances the efficiency of SAR imaging on single-core CPU by 270 times and realizes the real-time imaging in that the imaging rate outperforms the raw data generation rate. PMID:27070606

Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing.

PubMed

Zhang, Fan; Li, Guojun; Li, Wei; Hu, Wei; Hu, Yuxin

2016-04-07

With the development of synthetic aperture radar (SAR) technologies in recent years, the huge amount of remote sensing data brings challenges for real-time imaging processing. Therefore, high performance computing (HPC) methods have been presented to accelerate SAR imaging, especially the GPU based methods. In the classical GPU based imaging algorithm, GPU is employed to accelerate image processing by massive parallel computing, and CPU is only used to perform the auxiliary work such as data input/output (IO). However, the computing capability of CPU is ignored and underestimated. In this work, a new deep collaborative SAR imaging method based on multiple CPU/GPU is proposed to achieve real-time SAR imaging. Through the proposed tasks partitioning and scheduling strategy, the whole image can be generated with deep collaborative multiple CPU/GPU computing. In the part of CPU parallel imaging, the advanced vector extension (AVX) method is firstly introduced into the multi-core CPU parallel method for higher efficiency. As for the GPU parallel imaging, not only the bottlenecks of memory limitation and frequent data transferring are broken, but also kinds of optimized strategies are applied, such as streaming, parallel pipeline and so on. Experimental results demonstrate that the deep CPU/GPU collaborative imaging method enhances the efficiency of SAR imaging on single-core CPU by 270 times and realizes the real-time imaging in that the imaging rate outperforms the raw data generation rate.

Automated processing of zebrafish imaging data: a survey.

PubMed

Mikut, Ralf; Dickmeis, Thomas; Driever, Wolfgang; Geurts, Pierre; Hamprecht, Fred A; Kausler, Bernhard X; Ledesma-Carbayo, María J; Marée, Raphaël; Mikula, Karol; Pantazis, Periklis; Ronneberger, Olaf; Santos, Andres; Stotzka, Rainer; Strähle, Uwe; Peyriéras, Nadine

2013-09-01

Due to the relative transparency of its embryos and larvae, the zebrafish is an ideal model organism for bioimaging approaches in vertebrates. Novel microscope technologies allow the imaging of developmental processes in unprecedented detail, and they enable the use of complex image-based read-outs for high-throughput/high-content screening. Such applications can easily generate Terabytes of image data, the handling and analysis of which becomes a major bottleneck in extracting the targeted information. Here, we describe the current state of the art in computational image analysis in the zebrafish system. We discuss the challenges encountered when handling high-content image data, especially with regard to data quality, annotation, and storage. We survey methods for preprocessing image data for further analysis, and describe selected examples of automated image analysis, including the tracking of cells during embryogenesis, heartbeat detection, identification of dead embryos, recognition of tissues and anatomical landmarks, and quantification of behavioral patterns of adult fish. We review recent examples for applications using such methods, such as the comprehensive analysis of cell lineages during early development, the generation of a three-dimensional brain atlas of zebrafish larvae, and high-throughput drug screens based on movement patterns. Finally, we identify future challenges for the zebrafish image analysis community, notably those concerning the compatibility of algorithms and data formats for the assembly of modular analysis pipelines.

Automated Processing of Zebrafish Imaging Data: A Survey

PubMed Central

Dickmeis, Thomas; Driever, Wolfgang; Geurts, Pierre; Hamprecht, Fred A.; Kausler, Bernhard X.; Ledesma-Carbayo, María J.; Marée, Raphaël; Mikula, Karol; Pantazis, Periklis; Ronneberger, Olaf; Santos, Andres; Stotzka, Rainer; Strähle, Uwe; Peyriéras, Nadine

2013-01-01

Abstract Due to the relative transparency of its embryos and larvae, the zebrafish is an ideal model organism for bioimaging approaches in vertebrates. Novel microscope technologies allow the imaging of developmental processes in unprecedented detail, and they enable the use of complex image-based read-outs for high-throughput/high-content screening. Such applications can easily generate Terabytes of image data, the handling and analysis of which becomes a major bottleneck in extracting the targeted information. Here, we describe the current state of the art in computational image analysis in the zebrafish system. We discuss the challenges encountered when handling high-content image data, especially with regard to data quality, annotation, and storage. We survey methods for preprocessing image data for further analysis, and describe selected examples of automated image analysis, including the tracking of cells during embryogenesis, heartbeat detection, identification of dead embryos, recognition of tissues and anatomical landmarks, and quantification of behavioral patterns of adult fish. We review recent examples for applications using such methods, such as the comprehensive analysis of cell lineages during early development, the generation of a three-dimensional brain atlas of zebrafish larvae, and high-throughput drug screens based on movement patterns. Finally, we identify future challenges for the zebrafish image analysis community, notably those concerning the compatibility of algorithms and data formats for the assembly of modular analysis pipelines. PMID:23758125

Level set method with automatic selective local statistics for brain tumor segmentation in MR images.

PubMed

Thapaliya, Kiran; Pyun, Jae-Young; Park, Chun-Su; Kwon, Goo-Rak

2013-01-01

The level set approach is a powerful tool for segmenting images. This paper proposes a method for segmenting brain tumor images from MR images. A new signed pressure function (SPF) that can efficiently stop the contours at weak or blurred edges is introduced. The local statistics of the different objects present in the MR images were calculated. Using local statistics, the tumor objects were identified among different objects. In this level set method, the calculation of the parameters is a challenging task. The calculations of different parameters for different types of images were automatic. The basic thresholding value was updated and adjusted automatically for different MR images. This thresholding value was used to calculate the different parameters in the proposed algorithm. The proposed algorithm was tested on the magnetic resonance images of the brain for tumor segmentation and its performance was evaluated visually and quantitatively. Numerical experiments on some brain tumor images highlighted the efficiency and robustness of this method. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Imaging for lung physiology: What do we wish we could measure?

PubMed Central

Buxton, Richard B.

2012-01-01

The role of imaging as a tool for investigating lung physiology is growing at an accelerating pace. Looking forward, we wished to identify unresolved issues in lung physiology that might realistically be addressed by imaging methods in development or imaging approaches that could be considered. The role of imaging is framed in terms of the importance of good spatial and temporal resolution and the types of questions that could be addressed as these technical capabilities improve. Recognizing that physiology is fundamentally a quantitative science, a recurring emphasis is on the need for imaging methods that provide reliable measurements of specific physiological parameters. The topics included necessarily reflect our perspective on what are interesting questions and are not meant to be a comprehensive review. Nevertheless, we hope that this essay will be a spur to physiologists to think about how imaging could usefully be applied in their research and to physical scientists developing new imaging methods to attack challenging questions imaging could potentially answer. PMID:22582217

The analysis of image feature robustness using cometcloud

PubMed Central

Qi, Xin; Kim, Hyunjoo; Xing, Fuyong; Parashar, Manish; Foran, David J.; Yang, Lin

2012-01-01

The robustness of image features is a very important consideration in quantitative image analysis. The objective of this paper is to investigate the robustness of a range of image texture features using hematoxylin stained breast tissue microarray slides which are assessed while simulating different imaging challenges including out of focus, changes in magnification and variations in illumination, noise, compression, distortion, and rotation. We employed five texture analysis methods and tested them while introducing all of the challenges listed above. The texture features that were evaluated include co-occurrence matrix, center-symmetric auto-correlation, texture feature coding method, local binary pattern, and texton. Due to the independence of each transformation and texture descriptor, a network structured combination was proposed and deployed on the Rutgers private cloud. The experiments utilized 20 randomly selected tissue microarray cores. All the combinations of the image transformations and deformations are calculated, and the whole feature extraction procedure was completed in 70 minutes using a cloud equipped with 20 nodes. Center-symmetric auto-correlation outperforms all the other four texture descriptors but also requires the longest computational time. It is roughly 10 times slower than local binary pattern and texton. From a speed perspective, both the local binary pattern and texton features provided excellent performance for classification and content-based image retrieval. PMID:23248759

Hyperspectral image classification based on local binary patterns and PCANet

NASA Astrophysics Data System (ADS)

Yang, Huizhen; Gao, Feng; Dong, Junyu; Yang, Yang

2018-04-01

Hyperspectral image classification has been well acknowledged as one of the challenging tasks of hyperspectral data processing. In this paper, we propose a novel hyperspectral image classification framework based on local binary pattern (LBP) features and PCANet. In the proposed method, linear prediction error (LPE) is first employed to select a subset of informative bands, and LBP is utilized to extract texture features. Then, spectral and texture features are stacked into a high dimensional vectors. Next, the extracted features of a specified position are transformed to a 2-D image. The obtained images of all pixels are fed into PCANet for classification. Experimental results on real hyperspectral dataset demonstrate the effectiveness of the proposed method.

Globally optimal tumor segmentation in PET-CT images: a graph-based co-segmentation method.

PubMed

Han, Dongfeng; Bayouth, John; Song, Qi; Taurani, Aakant; Sonka, Milan; Buatti, John; Wu, Xiaodong

2011-01-01

Tumor segmentation in PET and CT images is notoriously challenging due to the low spatial resolution in PET and low contrast in CT images. In this paper, we have proposed a general framework to use both PET and CT images simultaneously for tumor segmentation. Our method utilizes the strength of each imaging modality: the superior contrast of PET and the superior spatial resolution of CT. We formulate this problem as a Markov Random Field (MRF) based segmentation of the image pair with a regularized term that penalizes the segmentation difference between PET and CT. Our method simulates the clinical practice of delineating tumor simultaneously using both PET and CT, and is able to concurrently segment tumor from both modalities, achieving globally optimal solutions in low-order polynomial time by a single maximum flow computation. The method was evaluated on clinically relevant tumor segmentation problems. The results showed that our method can effectively make use of both PET and CT image information, yielding segmentation accuracy of 0.85 in Dice similarity coefficient and the average median hausdorff distance (HD) of 6.4 mm, which is 10% (resp., 16%) improvement compared to the graph cuts method solely using the PET (resp., CT) images.

Denoising imaging polarimetry by adapted BM3D method.

PubMed

Tibbs, Alexander B; Daly, Ilse M; Roberts, Nicholas W; Bull, David R

2018-04-01

In addition to the visual information contained in intensity and color, imaging polarimetry allows visual information to be extracted from the polarization of light. However, a major challenge of imaging polarimetry is image degradation due to noise. This paper investigates the mitigation of noise through denoising algorithms and compares existing denoising algorithms with a new method, based on BM3D (Block Matching 3D). This algorithm, Polarization-BM3D (PBM3D), gives visual quality superior to the state of the art across all images and noise standard deviations tested. We show that denoising polarization images using PBM3D allows the degree of polarization to be more accurately calculated by comparing it with spectral polarimetry measurements.

Enhancing hyperspectral spatial resolution using multispectral image fusion: A wavelet approach

NASA Astrophysics Data System (ADS)

Jazaeri, Amin

High spectral and spatial resolution images have a significant impact in remote sensing applications. Because both spatial and spectral resolutions of spaceborne sensors are fixed by design and it is not possible to further increase the spatial or spectral resolution, techniques such as image fusion must be applied to achieve such goals. This dissertation introduces the concept of wavelet fusion between hyperspectral and multispectral sensors in order to enhance the spectral and spatial resolution of a hyperspectral image. To test the robustness of this concept, images from Hyperion (hyperspectral sensor) and Advanced Land Imager (multispectral sensor) were first co-registered and then fused using different wavelet algorithms. A regression-based fusion algorithm was also implemented for comparison purposes. The results show that the fused images using a combined bi-linear wavelet-regression algorithm have less error than other methods when compared to the ground truth. In addition, a combined regression-wavelet algorithm shows more immunity to misalignment of the pixels due to the lack of proper registration. The quantitative measures of average mean square error show that the performance of wavelet-based methods degrades when the spatial resolution of hyperspectral images becomes eight times less than its corresponding multispectral image. Regardless of what method of fusion is utilized, the main challenge in image fusion is image registration, which is also a very time intensive process. Because the combined regression wavelet technique is computationally expensive, a hybrid technique based on regression and wavelet methods was also implemented to decrease computational overhead. However, the gain in faster computation was offset by the introduction of more error in the outcome. The secondary objective of this dissertation is to examine the feasibility and sensor requirements for image fusion for future NASA missions in order to be able to perform onboard image fusion. In this process, the main challenge of image registration was resolved by registering the input images using transformation matrices of previously acquired data. The composite image resulted from the fusion process remarkably matched the ground truth, indicating the possibility of real time onboard fusion processing.

Magnetic resonance imaging with hyperpolarized agents: methods and applications

NASA Astrophysics Data System (ADS)

Adamson, Erin B.; Ludwig, Kai D.; Mummy, David G.; Fain, Sean B.

2017-07-01

In the past decade, hyperpolarized (HP) contrast agents have been under active development for MRI applications to address the twin challenges of functional and quantitative imaging. Both HP helium (3He) and xenon (129Xe) gases have reached the stage where they are under study in clinical research. HP 129Xe, in particular, is poised for larger scale clinical research to investigate asthma, chronic obstructive pulmonary disease, and fibrotic lung diseases. With advances in polarizer technology and unique capabilities for imaging of 129Xe gas exchange into lung tissue and blood, HP 129Xe MRI is attracting new attention. In parallel, HP 13C and 15N MRI methods have steadily advanced in a wide range of pre-clinical research applications for imaging metabolism in various cancers and cardiac disease. The HP [1-13C] pyruvate MRI technique, in particular, has undergone phase I trials in prostate cancer and is poised for investigational new drug trials at multiple institutions in cancer and cardiac applications. This review treats the methodology behind both HP gases and HP 13C and 15N liquid state agents. Gas and liquid phase HP agents share similar technologies for achieving non-equilibrium polarization outside the field of the MRI scanner, strategies for image data acquisition, and translational challenges in moving from pre-clinical to clinical research. To cover the wide array of methods and applications, this review is organized by numerical section into (1) a brief introduction, (2) the physical and biological properties of the most common polarized agents with a brief summary of applications and methods of polarization, (3) methods for image acquisition and reconstruction specific to improving data acquisition efficiency for HP MRI, (4) the main physical properties that enable unique measures of physiology or metabolic pathways, followed by a more detailed review of the literature describing the use of HP agents to study: (5) metabolic pathways in cancer and cardiac disease and (6) lung function in both pre-clinical and clinical research studies, concluding with (7) some future directions and challenges, and (8) an overall summary.

Crowdsourcing scoring of immunohistochemistry images: Evaluating Performance of the Crowd and an Automated Computational Method

NASA Astrophysics Data System (ADS)

Irshad, Humayun; Oh, Eun-Yeong; Schmolze, Daniel; Quintana, Liza M.; Collins, Laura; Tamimi, Rulla M.; Beck, Andrew H.

2017-02-01

The assessment of protein expression in immunohistochemistry (IHC) images provides important diagnostic, prognostic and predictive information for guiding cancer diagnosis and therapy. Manual scoring of IHC images represents a logistical challenge, as the process is labor intensive and time consuming. Since the last decade, computational methods have been developed to enable the application of quantitative methods for the analysis and interpretation of protein expression in IHC images. These methods have not yet replaced manual scoring for the assessment of IHC in the majority of diagnostic laboratories and in many large-scale research studies. An alternative approach is crowdsourcing the quantification of IHC images to an undefined crowd. The aim of this study is to quantify IHC images for labeling of ER status with two different crowdsourcing approaches, image-labeling and nuclei-labeling, and compare their performance with automated methods. Crowdsourcing- derived scores obtained greater concordance with the pathologist interpretations for both image-labeling and nuclei-labeling tasks (83% and 87%), as compared to the pathologist concordance achieved by the automated method (81%) on 5,338 TMA images from 1,853 breast cancer patients. This analysis shows that crowdsourcing the scoring of protein expression in IHC images is a promising new approach for large scale cancer molecular pathology studies.

Multiscale Medical Image Fusion in Wavelet Domain

PubMed Central

Khare, Ashish

2013-01-01

Wavelet transforms have emerged as a powerful tool in image fusion. However, the study and analysis of medical image fusion is still a challenging area of research. Therefore, in this paper, we propose a multiscale fusion of multimodal medical images in wavelet domain. Fusion of medical images has been performed at multiple scales varying from minimum to maximum level using maximum selection rule which provides more flexibility and choice to select the relevant fused images. The experimental analysis of the proposed method has been performed with several sets of medical images. Fusion results have been evaluated subjectively and objectively with existing state-of-the-art fusion methods which include several pyramid- and wavelet-transform-based fusion methods and principal component analysis (PCA) fusion method. The comparative analysis of the fusion results has been performed with edge strength (Q), mutual information (MI), entropy (E), standard deviation (SD), blind structural similarity index metric (BSSIM), spatial frequency (SF), and average gradient (AG) metrics. The combined subjective and objective evaluations of the proposed fusion method at multiple scales showed the effectiveness and goodness of the proposed approach. PMID:24453868

Optical imaging through dynamic turbid media using the Fourier-domain shower-curtain effect

PubMed Central

Edrei, Eitan; Scarcelli, Giuliano

2016-01-01

Several phenomena have been recently exploited to circumvent scattering and have succeeded in imaging or focusing light through turbid layers. However, the requirement for the turbid medium to be steady during the imaging process remains a fundamental limitation of these methods. Here we introduce an optical imaging modality that overcomes this challenge by taking advantage of the so-called shower-curtain effect, adapted to the spatial-frequency domain via speckle correlography. We present high resolution imaging of objects hidden behind millimeter-thick tissue or dense lens cataracts. We demonstrate our imaging technique to be insensitive to rapid medium movements (> 5 m/s) beyond any biologically-relevant motion. Furthermore, we show this method can be extended to several contrast mechanisms and imaging configurations. PMID:27347498

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.

ARCOCT: Automatic detection of lumen border in intravascular OCT images.

PubMed

Cheimariotis, Grigorios-Aris; Chatzizisis, Yiannis S; Koutkias, Vassilis G; Toutouzas, Konstantinos; Giannopoulos, Andreas; Riga, Maria; Chouvarda, Ioanna; Antoniadis, Antonios P; Doulaverakis, Charalambos; Tsamboulatidis, Ioannis; Kompatsiaris, Ioannis; Giannoglou, George D; Maglaveras, Nicos

2017-11-01

Intravascular optical coherence tomography (OCT) is an invaluable tool for the detection of pathological features on the arterial wall and the investigation of post-stenting complications. Computational lumen border detection in OCT images is highly advantageous, since it may support rapid morphometric analysis. However, automatic detection is very challenging, since OCT images typically include various artifacts that impact image clarity, including features such as side branches and intraluminal blood presence. This paper presents ARCOCT, a segmentation method for fully-automatic detection of lumen border in OCT images. ARCOCT relies on multiple, consecutive processing steps, accounting for image preparation, contour extraction and refinement. In particular, for contour extraction ARCOCT employs the transformation of OCT images based on physical characteristics such as reflectivity and absorption of the tissue and, for contour refinement, local regression using weighted linear least squares and a 2nd degree polynomial model is employed to achieve artifact and small-branch correction as well as smoothness of the artery mesh. Our major focus was to achieve accurate contour delineation in the various types of OCT images, i.e., even in challenging cases with branches and artifacts. ARCOCT has been assessed in a dataset of 1812 images (308 from stented and 1504 from native segments) obtained from 20 patients. ARCOCT was compared against ground-truth manual segmentation performed by experts on the basis of various geometric features (e.g. area, perimeter, radius, diameter, centroid, etc.) and closed contour matching indicators (the Dice index, the Hausdorff distance and the undirected average distance), using standard statistical analysis methods. The proposed method was proven very efficient and close to the ground-truth, exhibiting non statistically-significant differences for most of the examined metrics. ARCOCT allows accurate and fully-automated lumen border detection in OCT images. Copyright © 2017 Elsevier B.V. All rights reserved.

Times have changed! Forensic radiology--a new challenge for radiology and forensic pathology.

PubMed

Flach, Patricia M; Thali, Michael J; Germerott, Tanja

2014-04-01

The ongoing development of imaging and the recent integration of cross-sectional imaging methods into the medicolegal workflow have resulted in an increasing number of forensic institutes acquiring dedicated CT and MRI scanners. The purpose of this article is to evaluate the different aspects of postmortem imaging and to detail the necessary cooperation between radiologists and forensic pathologists for mutual learning and accurate science to form a new subspecialty: forensic radiology. CONCLUSION; Forensic radiology must integrate the expertise of forensic pathologists and radiologists. The challenge is to unite these two disciplines first by direct and intense communications and second by a basic understanding of forensic pathology by radiologists as well as a foundational knowledge of postmortem imaging by forensic pathologists, in combination with the establishment of educational and reporting guidelines.

Multi-modal Registration for Correlative Microscopy using Image Analogies

PubMed Central

Cao, Tian; Zach, Christopher; Modla, Shannon; Powell, Debbie; Czymmek, Kirk; Niethammer, Marc

2014-01-01

Correlative microscopy is a methodology combining the functionality of light microscopy with the high resolution of electron microscopy and other microscopy technologies for the same biological specimen. In this paper, we propose an image registration method for correlative microscopy, which is challenging due to the distinct appearance of biological structures when imaged with different modalities. Our method is based on image analogies and allows to transform images of a given modality into the appearance-space of another modality. Hence, the registration between two different types of microscopy images can be transformed to a mono-modality image registration. We use a sparse representation model to obtain image analogies. The method makes use of corresponding image training patches of two different imaging modalities to learn a dictionary capturing appearance relations. We test our approach on backscattered electron (BSE) scanning electron microscopy (SEM)/confocal and transmission electron microscopy (TEM)/confocal images. We perform rigid, affine, and deformable registration via B-splines and show improvements over direct registration using both mutual information and sum of squared differences similarity measures to account for differences in image appearance. PMID:24387943

Quantitative assessment of image motion blur in diffraction images of moving biological cells

NASA Astrophysics Data System (ADS)

Wang, He; Jin, Changrong; Feng, Yuanming; Qi, Dandan; Sa, Yu; Hu, Xin-Hua

2016-02-01

Motion blur (MB) presents a significant challenge for obtaining high-contrast image data from biological cells with a polarization diffraction imaging flow cytometry (p-DIFC) method. A new p-DIFC experimental system has been developed to evaluate the MB and its effect on image analysis using a time-delay-integration (TDI) CCD camera. Diffraction images of MCF-7 and K562 cells have been acquired with different speed-mismatch ratios and compared to characterize MB quantitatively. Frequency analysis of the diffraction images shows that the degree of MB can be quantified by bandwidth variations of the diffraction images along the motion direction. The analytical results were confirmed by the p-DIFC image data acquired at different speed-mismatch ratios and used to validate a method of numerical simulation of MB on blur-free diffraction images, which provides a useful tool to examine the blurring effect on diffraction images acquired from the same cell. These results provide insights on the dependence of diffraction image on MB and allow significant improvement on rapid biological cell assay with the p-DIFC method.

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

PubMed

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

2017-04-01

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

Multiplicative noise removal via a learned dictionary.

PubMed

Huang, Yu-Mei; Moisan, Lionel; Ng, Michael K; Zeng, Tieyong

2012-11-01

Multiplicative noise removal is a challenging image processing problem, and most existing methods are based on the maximum a posteriori formulation and the logarithmic transformation of multiplicative denoising problems into additive denoising problems. Sparse representations of images have shown to be efficient approaches for image recovery. Following this idea, in this paper, we propose to learn a dictionary from the logarithmic transformed image, and then to use it in a variational model built for noise removal. Extensive experimental results suggest that in terms of visual quality, peak signal-to-noise ratio, and mean absolute deviation error, the proposed algorithm outperforms state-of-the-art methods.

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments.

PubMed

Van Valen, David A; Kudo, Takamasa; Lane, Keara M; Macklin, Derek N; Quach, Nicolas T; DeFelice, Mialy M; Maayan, Inbal; Tanouchi, Yu; Ashley, Euan A; Covert, Markus W

2016-11-01

Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domains of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.

Three-Dimensional Imaging and Quantification of Biomass and Biofilms in Porous Media

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

Dorthe Wildenschild

2012-10-10

A new method to resolve biofilms in three dimensions in porous media using high-resolution synchrotron-based x-ray computed microtomography (CMT) has been developed. Imaging biofilms in porous media without disturbing the natural spatial arrangement of the porous media and associated biofilm has been a challenging task, primarily because porous media generally precludes conventional imaging via optical microscopy; x-ray tomography offers a potential alternative. One challenge for using this method is that most conventional x-ray contrast agents are water-soluble and easily diffuse into biofilms. To overcome this problem, silver-coated microspheres were added to the fluid phase to create an x-ray contrast thatmore » does not diffuse into the biofilm mass. Using this approach, biofilm imaging in porous media was accomplished with sufficient contrast to differentiate between the biomass- and fluid-filled pore spaces. The method was validated by using a two-dimensional micro-model flow cell where both light microscopy and CMT imaging were used to im age the biofilm. The results of this work has been published in Water Resources Research (Iltis et al., 2010). Additional work needs to be done to optimize this imaging approach, specifically, we find that the quality of the images are highly dependent on the coverage of the biofilm with Ag particles, - which means that we may have issues in dead-end pore space and for very low density (fluffy) biofilms. What we can image for certain with this technique is the biofilm surface that is well-connected to flow paths and thus well-supplied with nutrients etc.« less

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments

DOE PAGES

Van Valen, David A.; Kudo, Takamasa; Lane, Keara M.; ...

2016-11-04

Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domainsmore » of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.« less

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments

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

Van Valen, David A.; Kudo, Takamasa; Lane, Keara M.

Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domainsmore » of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.« less

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments

PubMed Central

Van Valen, David A.; Lane, Keara M.; Quach, Nicolas T.; Maayan, Inbal

2016-01-01

Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domains of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems. PMID:27814364

Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success.

PubMed

Thrall, James H; Li, Xiang; Li, Quanzheng; Cruz, Cinthia; Do, Synho; Dreyer, Keith; Brink, James

2018-03-01

Worldwide interest in artificial intelligence (AI) applications, including imaging, is high and growing rapidly, fueled by availability of large datasets ("big data"), substantial advances in computing power, and new deep-learning algorithms. Apart from developing new AI methods per se, there are many opportunities and challenges for the imaging community, including the development of a common nomenclature, better ways to share image data, and standards for validating AI program use across different imaging platforms and patient populations. AI surveillance programs may help radiologists prioritize work lists by identifying suspicious or positive cases for early review. AI programs can be used to extract "radiomic" information from images not discernible by visual inspection, potentially increasing the diagnostic and prognostic value derived from image datasets. Predictions have been made that suggest AI will put radiologists out of business. This issue has been overstated, and it is much more likely that radiologists will beneficially incorporate AI methods into their practices. Current limitations in availability of technical expertise and even computing power will be resolved over time and can also be addressed by remote access solutions. Success for AI in imaging will be measured by value created: increased diagnostic certainty, faster turnaround, better outcomes for patients, and better quality of work life for radiologists. AI offers a new and promising set of methods for analyzing image data. Radiologists will explore these new pathways and are likely to play a leading role in medical applications of AI. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Time-of-Flight Microwave Camera

PubMed Central

Charvat, Gregory; Temme, Andrew; Feigin, Micha; Raskar, Ramesh

2015-01-01

Microwaves can penetrate many obstructions that are opaque at visible wavelengths, however microwave imaging is challenging due to resolution limits associated with relatively small apertures and unrecoverable “stealth” regions due to the specularity of most objects at microwave frequencies. We demonstrate a multispectral time-of-flight microwave imaging system which overcomes these challenges with a large passive aperture to improve lateral resolution, multiple illumination points with a data fusion method to reduce stealth regions, and a frequency modulated continuous wave (FMCW) receiver to achieve depth resolution. The camera captures images with a resolution of 1.5 degrees, multispectral images across the X frequency band (8 GHz–12 GHz), and a time resolution of 200 ps (6 cm optical path in free space). Images are taken of objects in free space as well as behind drywall and plywood. This architecture allows “camera-like” behavior from a microwave imaging system and is practical for imaging everyday objects in the microwave spectrum. PMID:26434598

Time-of-Flight Microwave Camera

NASA Astrophysics Data System (ADS)

Charvat, Gregory; Temme, Andrew; Feigin, Micha; Raskar, Ramesh

2015-10-01

Microwaves can penetrate many obstructions that are opaque at visible wavelengths, however microwave imaging is challenging due to resolution limits associated with relatively small apertures and unrecoverable “stealth” regions due to the specularity of most objects at microwave frequencies. We demonstrate a multispectral time-of-flight microwave imaging system which overcomes these challenges with a large passive aperture to improve lateral resolution, multiple illumination points with a data fusion method to reduce stealth regions, and a frequency modulated continuous wave (FMCW) receiver to achieve depth resolution. The camera captures images with a resolution of 1.5 degrees, multispectral images across the X frequency band (8 GHz-12 GHz), and a time resolution of 200 ps (6 cm optical path in free space). Images are taken of objects in free space as well as behind drywall and plywood. This architecture allows “camera-like” behavior from a microwave imaging system and is practical for imaging everyday objects in the microwave spectrum.

Fully Convolutional Architecture for Low-Dose CT Image Noise Reduction

NASA Astrophysics Data System (ADS)

Badretale, S.; Shaker, F.; Babyn, P.; Alirezaie, J.

2017-10-01

One of the critical topics in medical low-dose Computed Tomography (CT) imaging is how best to maintain image quality. As the quality of images decreases with lowering the X-ray radiation dose, improving image quality is extremely important and challenging. We have proposed a novel approach to denoise low-dose CT images. Our algorithm learns directly from an end-to-end mapping from the low-dose Computed Tomography images for denoising the normal-dose CT images. Our method is based on a deep convolutional neural network with rectified linear units. By learning various low-level to high-level features from a low-dose image the proposed algorithm is capable of creating a high-quality denoised image. We demonstrate the superiority of our technique by comparing the results with two other state-of-the-art methods in terms of the peak signal to noise ratio, root mean square error, and a structural similarity index.

Phase retrieval by coherent modulation imaging

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

Zhang, Fucai; Chen, Bo; Morrison, Graeme R.

Phase retrieval is a long-standing problem in imaging when only the intensity of the wavefield can be recorded. Coherent diffraction imaging (CDI) is a lensless technique that uses iterative algorithms to recover amplitude and phase contrast images from diffraction intensity data. For general samples, phase retrieval from a single diffraction pattern has been an algorithmic and experimental challenge. Here we report a method of phase retrieval that uses a known modulation of the sample exit-wave. This coherent modulation imaging (CMI) method removes inherent ambiguities of CDI and uses a reliable, rapidly converging iterative algorithm involving three planes. It works formore » extended samples, does not require tight support for convergence, and relaxes dynamic range requirements on the detector. CMI provides a robust method for imaging in materials and biological science, while its single-shot capability will benefit the investigation of dynamical processes with pulsed sources, such as X-ray free electron laser.« less

Phase retrieval by coherent modulation imaging

DOE PAGES

Zhang, Fucai; Chen, Bo; Morrison, Graeme R.; ...

2016-11-18

Phase retrieval is a long-standing problem in imaging when only the intensity of the wavefield can be recorded. Coherent diffraction imaging (CDI) is a lensless technique that uses iterative algorithms to recover amplitude and phase contrast images from diffraction intensity data. For general samples, phase retrieval from a single diffraction pattern has been an algorithmic and experimental challenge. Here we report a method of phase retrieval that uses a known modulation of the sample exit-wave. This coherent modulation imaging (CMI) method removes inherent ambiguities of CDI and uses a reliable, rapidly converging iterative algorithm involving three planes. It works formore » extended samples, does not require tight support for convergence, and relaxes dynamic range requirements on the detector. CMI provides a robust method for imaging in materials and biological science, while its single-shot capability will benefit the investigation of dynamical processes with pulsed sources, such as X-ray free electron laser.« less

High Dynamic Range Imaging Using Multiple Exposures

NASA Astrophysics Data System (ADS)

Hou, Xinglin; Luo, Haibo; Zhou, Peipei; Zhou, Wei

2017-06-01

It is challenging to capture a high-dynamic range (HDR) scene using a low-dynamic range (LDR) camera. This paper presents an approach for improving the dynamic range of cameras by using multiple exposure images of same scene taken under different exposure times. First, the camera response function (CRF) is recovered by solving a high-order polynomial in which only the ratios of the exposures are used. Then, the HDR radiance image is reconstructed by weighted summation of the each radiance maps. After that, a novel local tone mapping (TM) operator is proposed for the display of the HDR radiance image. By solving the high-order polynomial, the CRF can be recovered quickly and easily. Taken the local image feature and characteristic of histogram statics into consideration, the proposed TM operator could preserve the local details efficiently. Experimental result demonstrates the effectiveness of our method. By comparison, the method outperforms other methods in terms of imaging quality.

A survey of infrared and visual image fusion methods

NASA Astrophysics Data System (ADS)

Jin, Xin; Jiang, Qian; Yao, Shaowen; Zhou, Dongming; Nie, Rencan; Hai, Jinjin; He, Kangjian

2017-09-01

Infrared (IR) and visual (VI) image fusion is designed to fuse multiple source images into a comprehensive image to boost imaging quality and reduce redundancy information, which is widely used in various imaging equipment to improve the visual ability of human and robot. The accurate, reliable and complementary descriptions of the scene in fused images make these techniques be widely used in various fields. In recent years, a large number of fusion methods for IR and VI images have been proposed due to the ever-growing demands and the progress of image representation methods; however, there has not been published an integrated survey paper about this field in last several years. Therefore, we make a survey to report the algorithmic developments of IR and VI image fusion. In this paper, we first characterize the IR and VI image fusion based applications to represent an overview of the research status. Then we present a synthesize survey of the state of the art. Thirdly, the frequently-used image fusion quality measures are introduced. Fourthly, we perform some experiments of typical methods and make corresponding analysis. At last, we summarize the corresponding tendencies and challenges in IR and VI image fusion. This survey concludes that although various IR and VI image fusion methods have been proposed, there still exist further improvements or potential research directions in different applications of IR and VI image fusion.

External Prior Guided Internal Prior Learning for Real-World Noisy Image Denoising

NASA Astrophysics Data System (ADS)

Xu, Jun; Zhang, Lei; Zhang, David

2018-06-01

Most of existing image denoising methods learn image priors from either external data or the noisy image itself to remove noise. However, priors learned from external data may not be adaptive to the image to be denoised, while priors learned from the given noisy image may not be accurate due to the interference of corrupted noise. Meanwhile, the noise in real-world noisy images is very complex, which is hard to be described by simple distributions such as Gaussian distribution, making real noisy image denoising a very challenging problem. We propose to exploit the information in both external data and the given noisy image, and develop an external prior guided internal prior learning method for real noisy image denoising. We first learn external priors from an independent set of clean natural images. With the aid of learned external priors, we then learn internal priors from the given noisy image to refine the prior model. The external and internal priors are formulated as a set of orthogonal dictionaries to efficiently reconstruct the desired image. Extensive experiments are performed on several real noisy image datasets. The proposed method demonstrates highly competitive denoising performance, outperforming state-of-the-art denoising methods including those designed for real noisy images.

Feature extraction based on extended multi-attribute profiles and sparse autoencoder for remote sensing image classification

NASA Astrophysics Data System (ADS)

Teffahi, Hanane; Yao, Hongxun; Belabid, Nasreddine; Chaib, Souleyman

2018-02-01

The satellite images with very high spatial resolution have been recently widely used in image classification topic as it has become challenging task in remote sensing field. Due to a number of limitations such as the redundancy of features and the high dimensionality of the data, different classification methods have been proposed for remote sensing images classification particularly the methods using feature extraction techniques. This paper propose a simple efficient method exploiting the capability of extended multi-attribute profiles (EMAP) with sparse autoencoder (SAE) for remote sensing image classification. The proposed method is used to classify various remote sensing datasets including hyperspectral and multispectral images by extracting spatial and spectral features based on the combination of EMAP and SAE by linking them to kernel support vector machine (SVM) for classification. Experiments on new hyperspectral image "Huston data" and multispectral image "Washington DC data" shows that this new scheme can achieve better performance of feature learning than the primitive features, traditional classifiers and ordinary autoencoder and has huge potential to achieve higher accuracy for classification in short running time.

Retinal vessel segmentation on SLO image

PubMed Central

Xu, Juan; Ishikawa, Hiroshi; Wollstein, Gadi; Schuman, Joel S.

2010-01-01

A scanning laser ophthalmoscopy (SLO) image, taken from optical coherence tomography (OCT), usually has lower global/local contrast and more noise compared to the traditional retinal photograph, which makes the vessel segmentation challenging work. A hybrid algorithm is proposed to efficiently solve these problems by fusing several designed methods, taking the advantages of each method and reducing the error measurements. The algorithm has several steps consisting of image preprocessing, thresholding probe and weighted fusing. Four different methods are first designed to transform the SLO image into feature response images by taking different combinations of matched filter, contrast enhancement and mathematical morphology operators. A thresholding probe algorithm is then applied on those response images to obtain four vessel maps. Weighted majority opinion is used to fuse these vessel maps and generate a final vessel map. The experimental results showed that the proposed hybrid algorithm could successfully segment the blood vessels on SLO images, by detecting the major and small vessels and suppressing the noises. The algorithm showed substantial potential in various clinical applications. The use of this method can be also extended to medical image registration based on blood vessel location. PMID:19163149

Investigating the performance of reconstruction methods used in structured illumination microscopy as a function of the illumination pattern's modulation frequency

NASA Astrophysics Data System (ADS)

Shabani, H.; Sánchez-Ortiga, E.; Preza, C.

2016-03-01

Surpassing the resolution of optical microscopy defined by the Abbe diffraction limit, while simultaneously achieving optical sectioning, is a challenging problem particularly for live cell imaging of thick samples. Among a few developing techniques, structured illumination microscopy (SIM) addresses this challenge by imposing higher frequency information into the observable frequency band confined by the optical transfer function (OTF) of a conventional microscope either doubling the spatial resolution or filling the missing cone based on the spatial frequency of the pattern when the patterned illumination is two-dimensional. Standard reconstruction methods for SIM decompose the low and high frequency components from the recorded low-resolution images and then combine them to reach a high-resolution image. In contrast, model-based approaches rely on iterative optimization approaches to minimize the error between estimated and forward images. In this paper, we study the performance of both groups of methods by simulating fluorescence microscopy images from different type of objects (ranging from simulated two-point sources to extended objects). These simulations are used to investigate the methods' effectiveness on restoring objects with various types of power spectrum when modulation frequency of the patterned illumination is changing from zero to the incoherent cut-off frequency of the imaging system. Our results show that increasing the amount of imposed information by using a higher modulation frequency of the illumination pattern does not always yield a better restoration performance, which was found to be depended on the underlying object. Results from model-based restoration show performance improvement, quantified by an up to 62% drop in the mean square error compared to standard reconstruction, with increasing modulation frequency. However, we found cases for which results obtained with standard reconstruction methods do not always follow the same trend.

Single shot imaging through turbid medium and around corner using coherent light

NASA Astrophysics Data System (ADS)

Li, Guowei; Li, Dayan; Situ, Guohai

2018-01-01

Optical imaging through turbid media and around corner is a difficult challenge. Even a very thin layer of a turbid media, which randomly scatters the probe light, can appear opaque and hide any objects behind it. Despite many recent advances, no current method can image the object behind turbid media with single record using coherent laser illumination. Here we report a method that allows non-invasive single-shot optical imaging through turbid media and around corner via speckle correlation. Instead of being as an obstacle in forming diffractionlimited images, speckle actually can be a carrier that encodes sufficient information to imaging through visually opaque layers. Optical imaging through turbid media and around corner is experimentally demonstrated using traditional imaging system with the aid of iterative phase retrieval algorithm. Our method require neither scan of illumination nor two-arm interferometry or long-time exposure in acquisition, which has new implications in optical sensing through common obscurants such as fog, smoke and haze.

MO-G-12A-01: Quantitative Imaging Metrology: What Should Be Assessed and How?

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

Giger, M; Petrick, N; Obuchowski, N

The first two symposia in the Quantitative Imaging Track focused on 1) the introduction of quantitative imaging (QI) challenges and opportunities, and QI efforts of agencies and organizations such as the RSNA, NCI, FDA, and NIST, and 2) the techniques, applications, and challenges of QI, with specific examples from CT, PET/CT, and MR. This third symposium in the QI Track will focus on metrology and its importance in successfully advancing the QI field. While the specific focus will be on QI, many of the concepts presented are more broadly applicable to many areas of medical physics research and applications. Asmore » such, the topics discussed should be of interest to medical physicists involved in imaging as well as therapy. The first talk of the session will focus on the introduction to metrology and why it is critically important in QI. The second talk will focus on appropriate methods for technical performance assessment. The third talk will address statistically valid methods for algorithm comparison, a common problem not only in QI but also in other areas of medical physics. The final talk in the session will address strategies for publication of results that will allow statistically valid meta-analyses, which is critical for combining results of individual studies with typically small sample sizes in a manner that can best inform decisions and advance the field. Learning Objectives: Understand the importance of metrology in the QI efforts. Understand appropriate methods for technical performance assessment. Understand methods for comparing algorithms with or without reference data (i.e., “ground truth”). Understand the challenges and importance of reporting results in a manner that allows for statistically valid meta-analyses.« less

Discovering significant evolution patterns from satellite image time series.

PubMed

Petitjean, François; Masseglia, Florent; Gançarski, Pierre; Forestier, Germain

2011-12-01

Satellite Image Time Series (SITS) provide us with precious information on land cover evolution. By studying these series of images we can both understand the changes of specific areas and discover global phenomena that spread over larger areas. Changes that can occur throughout the sensing time can spread over very long periods and may have different start time and end time depending on the location, which complicates the mining and the analysis of series of images. This work focuses on frequent sequential pattern mining (FSPM) methods, since this family of methods fits the above-mentioned issues. This family of methods consists of finding the most frequent evolution behaviors, and is actually able to extract long-term changes as well as short term ones, whenever the change may start and end. However, applying FSPM methods to SITS implies confronting two main challenges, related to the characteristics of SITS and the domain's constraints. First, satellite images associate multiple measures with a single pixel (the radiometric levels of different wavelengths corresponding to infra-red, red, etc.), which makes the search space multi-dimensional and thus requires specific mining algorithms. Furthermore, the non evolving regions, which are the vast majority and overwhelm the evolving ones, challenge the discovery of these patterns. We propose a SITS mining framework that enables discovery of these patterns despite these constraints and characteristics. Our proposal is inspired from FSPM and provides a relevant visualization principle. Experiments carried out on 35 images sensed over 20 years show the proposed approach makes it possible to extract relevant evolution behaviors.

Markov random field based automatic image alignment for electron tomography.

PubMed

Amat, Fernando; Moussavi, Farshid; Comolli, Luis R; Elidan, Gal; Downing, Kenneth H; Horowitz, Mark

2008-03-01

We present a method for automatic full-precision alignment of the images in a tomographic tilt series. Full-precision automatic alignment of cryo electron microscopy images has remained a difficult challenge to date, due to the limited electron dose and low image contrast. These facts lead to poor signal to noise ratio (SNR) in the images, which causes automatic feature trackers to generate errors, even with high contrast gold particles as fiducial features. To enable fully automatic alignment for full-precision reconstructions, we frame the problem probabilistically as finding the most likely particle tracks given a set of noisy images, using contextual information to make the solution more robust to the noise in each image. To solve this maximum likelihood problem, we use Markov Random Fields (MRF) to establish the correspondence of features in alignment and robust optimization for projection model estimation. The resulting algorithm, called Robust Alignment and Projection Estimation for Tomographic Reconstruction, or RAPTOR, has not needed any manual intervention for the difficult datasets we have tried, and has provided sub-pixel alignment that is as good as the manual approach by an expert user. We are able to automatically map complete and partial marker trajectories and thus obtain highly accurate image alignment. Our method has been applied to challenging cryo electron tomographic datasets with low SNR from intact bacterial cells, as well as several plastic section and X-ray datasets.

Flow in Coal Seams: An Unconventional Challenge

NASA Astrophysics Data System (ADS)

Armstrong, R. T.; Mostaghimi, P.; Jing, Y.; Gerami, A.

2016-12-01

A significant unconventional resource for energy is the methane gas stored in shallow coal beds, known as coal seam gas. An integrated imaging and modelling framework is developed for analysing petrophysical behaviour of coals. X-ray micro-computed tomography (micro-CT) is applied using a novel contrast agent method for visualising micrometer-sized fractures in coal. The technique allows for the visualisation of coal features not visible with conventional imaging methods. A Late Permian medium volatile bituminous coal from Moura Coal Mine (Queensland, Australia) is imaged and the resulting three-dimensional coal fracture system is extracted for fluid flow simulations. The results demonstrate a direct relationship between coal lithotype and permeability. Scanning electron microscope and energy dispersive spectrometry (SEM-EDS) together with X-ray diffraction (XRD) methods are used for identifying mineral matters at high resolution. SEM high-resolution images are also used to calibrate the micro-CT images and measure the exact aperture size of fractures. This leads to a more accurate estimation of permeability using micro-CT images. To study the significance of geometry and topology of the fracture system, a fracture reconstruction method based on statistical properties of coal is also developed. The network properties including the frequency, aperture size distribution, length, and spacing of the imaged coal fracture system. This allows for a sensitivity analysis on the effects that coal fracture topology and geometry has on coal petrophysical properties. Furthermore, we generate microfluidic chips based on coal fracture observations. The chip is used for flow experiments to visualise multi-fluid processes and measure recovery of gas. A combined numerical and experimental approach is applied to obtain relative permeability curves for different regions of interest. A number of challenges associated with coal samples are discussed and insights are provided for better understanding of these complex porous media systems.

Molar axis estimation from computed tomography images.

PubMed

Dongxia Zhang; Yangzhou Gan; Zeyang Xia; Xinwen Zhou; Shoubin Liu; Jing Xiong; Guanglin Li

2016-08-01

Estimation of tooth axis is needed for some clinical dental treatment. Existing methods require to segment the tooth volume from Computed Tomography (CT) images, and then estimate the axis from the tooth volume. However, they may fail during estimating molar axis due to that the tooth segmentation from CT images is challenging and current segmentation methods may get poor segmentation results especially for these molars with angle which will result in the failure of axis estimation. To resolve this problem, this paper proposes a new method for molar axis estimation from CT images. The key innovation point is that: instead of estimating the 3D axis of each molar from the segmented volume, the method estimates the 3D axis from two projection images. The method includes three steps. (1) The 3D images of each molar are projected to two 2D image planes. (2) The molar contour are segmented and the contour's 2D axis are extracted in each 2D projection image. Principal Component Analysis (PCA) and a modified symmetry axis detection algorithm are employed to extract the 2D axis from the segmented molar contour. (3) A 3D molar axis is obtained by combining the two 2D axes. Experimental results verified that the proposed method was effective to estimate the axis of molar from CT images.

Noninvasive photoacoustic measurement of absorption coefficient using internal light irradiation of cylindrical diffusing fiber

NASA Astrophysics Data System (ADS)

Peng, Dong-qing; Zhu, Li-li; Li, Zhi-fang; Li, Hui

2017-09-01

Absorption coefficient of biological tissue is an important parameter in biomedicine, but its determination remains a challenge. In this paper, we propose a method using focusing photoacoustic imaging technique and internal light irradiation of cylindrical diffusing fiber (CDF) to quantify the target optical absorption coefficient. Absorption coefficients for ink absorbers are firstly determined through photoacoustic and spectrophotometric measurements at the same excitation, which demonstrates the feasibility of this method. Also, the optical absorption coefficients of ink absorbers with several concentrations are measured. Finally, the two-dimensional scanning photoacoustic image is obtained. Optical absorption coefficient measurement and simultaneous photoacoustic imaging of absorber non-invasively are the typical characteristics of the method. This method can play a significant role for non-invasive determination of blood oxygen saturation, the absorption-based imaging and therapy.

Elemental X-ray Imaging Using the Maia Detector Array: The Benefits and Challenges of Large Solid-Angle

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

Ryan, C.G.; De Geronimo, G.; Kirkham, R.

2009-11-13

The fundamental parameter method for quantitative SXRF and PIXE analysis and imaging using the dynamic analysis method is extended to model the changing X-ray yields and detector sensitivity with angle across large detector arrays. The method is implemented in the GeoPIXE software and applied to cope with the large solid-angle of the new Maia 384 detector array and its 96 detector prototype developed by CSIRO and BNL for SXRF imaging applications at the Australian and NSLS synchrotrons. Peak-to-background is controlled by mitigating charge-sharing between detectors through careful optimization of a patterned molybdenum absorber mask. A geological application demonstrates the capabilitymore » of the method to produce high definition elemental images up to {approx}100 M pixels in size.« less

Inverse scattering pre-stack depth imaging and it's comparison to some depth migration methods for imaging rich fault complex structure

NASA Astrophysics Data System (ADS)

Nurhandoko, Bagus Endar B.; Sukmana, Indriani; Mubarok, Syahrul; Deny, Agus; Widowati, Sri; Kurniadi, Rizal

2012-06-01

Migration is important issue for seismic imaging in complex structure. In this decade, depth imaging becomes important tools for producing accurate image in depth imaging instead of time domain imaging. The challenge of depth migration method, however, is in revealing the complex structure of subsurface. There are many methods of depth migration with their advantages and weaknesses. In this paper, we show our propose method of pre-stack depth migration based on time domain inverse scattering wave equation. Hopefully this method can be as solution for imaging complex structure in Indonesia, especially in rich thrusting fault zones. In this research, we develop a recent advance wave equation migration based on time domain inverse scattering wave which use more natural wave propagation using scattering wave. This wave equation pre-stack depth migration use time domain inverse scattering wave equation based on Helmholtz equation. To provide true amplitude recovery, an inverse of divergence procedure and recovering transmission loss are considered of pre-stack migration. Benchmarking the propose inverse scattering pre-stack depth migration with the other migration methods are also presented, i.e.: wave equation pre-stack depth migration, waveequation depth migration, and pre-stack time migration method. This inverse scattering pre-stack depth migration could image successfully the rich fault zone which consist extremely dip and resulting superior quality of seismic image. The image quality of inverse scattering migration is much better than the others migration methods.

An Efficient Method to Detect Mutual Overlap of a Large Set of Unordered Images for Structure-From

NASA Astrophysics Data System (ADS)

Wang, X.; Zhan, Z. Q.; Heipke, C.

2017-05-01

Recently, low-cost 3D reconstruction based on images has become a popular focus of photogrammetry and computer vision research. Methods which can handle an arbitrary geometric setup of a large number of unordered and convergent images are of particular interest. However, determining the mutual overlap poses a considerable challenge. We propose a new method which was inspired by and improves upon methods employing random k-d forests for this task. Specifically, we first derive features from the images and then a random k-d forest is used to find the nearest neighbours in feature space. Subsequently, the degree of similarity between individual images, the image overlaps and thus images belonging to a common block are calculated as input to a structure-from-motion (sfm) pipeline. In our experiments we show the general applicability of the new method and compare it with other methods by analyzing the time efficiency. Orientations and 3D reconstructions were successfully conducted with our overlap graphs by sfm. The results show a speed-up of a factor of 80 compared to conventional pairwise matching, and of 8 and 2 compared to the VocMatch approach using 1 and 4 CPU, respectively.

Application of the SNoW machine learning paradigm to a set of transportation imaging problems

NASA Astrophysics Data System (ADS)

Paul, Peter; Burry, Aaron M.; Wang, Yuheng; Kozitsky, Vladimir

2012-01-01

Machine learning methods have been successfully applied to image object classification problems where there is clear distinction between classes and where a comprehensive set of training samples and ground truth are readily available. The transportation domain is an area where machine learning methods are particularly applicable, since the classification problems typically have well defined class boundaries and, due to high traffic volumes in most applications, massive roadway data is available. Though these classes tend to be well defined, the particular image noises and variations can be challenging. Another challenge is the extremely high accuracy typically required in most traffic applications. Incorrect assignment of fines or tolls due to imaging mistakes is not acceptable in most applications. For the front seat vehicle occupancy detection problem, classification amounts to determining whether one face (driver only) or two faces (driver + passenger) are detected in the front seat of a vehicle on a roadway. For automatic license plate recognition, the classification problem is a type of optical character recognition problem encompassing multiple class classification. The SNoW machine learning classifier using local SMQT features is shown to be successful in these two transportation imaging applications.

Efficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift Algorithm.

PubMed

Yang, Mengzhao; Song, Wei; Mei, Haibin

2017-07-23

The rapid development of remote sensing (RS) technology has resulted in the proliferation of high-resolution images. There are challenges involved in not only storing large volumes of RS images but also in rapidly retrieving the images for ocean disaster analysis such as for storm surges and typhoon warnings. In this paper, we present an efficient retrieval of massive ocean RS images via a Cloud-based mean-shift algorithm. Distributed construction method via the pyramid model is proposed based on the maximum hierarchical layer algorithm and used to realize efficient storage structure of RS images on the Cloud platform. We achieve high-performance processing of massive RS images in the Hadoop system. Based on the pyramid Hadoop distributed file system (HDFS) storage method, an improved mean-shift algorithm for RS image retrieval is presented by fusion with the canopy algorithm via Hadoop MapReduce programming. The results show that the new method can achieve better performance for data storage than HDFS alone and WebGIS-based HDFS. Speedup and scaleup are very close to linear changes with an increase of RS images, which proves that image retrieval using our method is efficient.

Efficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift Algorithm

PubMed Central

Song, Wei; Mei, Haibin

2017-01-01

The rapid development of remote sensing (RS) technology has resulted in the proliferation of high-resolution images. There are challenges involved in not only storing large volumes of RS images but also in rapidly retrieving the images for ocean disaster analysis such as for storm surges and typhoon warnings. In this paper, we present an efficient retrieval of massive ocean RS images via a Cloud-based mean-shift algorithm. Distributed construction method via the pyramid model is proposed based on the maximum hierarchical layer algorithm and used to realize efficient storage structure of RS images on the Cloud platform. We achieve high-performance processing of massive RS images in the Hadoop system. Based on the pyramid Hadoop distributed file system (HDFS) storage method, an improved mean-shift algorithm for RS image retrieval is presented by fusion with the canopy algorithm via Hadoop MapReduce programming. The results show that the new method can achieve better performance for data storage than HDFS alone and WebGIS-based HDFS. Speedup and scaleup are very close to linear changes with an increase of RS images, which proves that image retrieval using our method is efficient. PMID:28737699

Multi-atlas label fusion using hybrid of discriminative and generative classifiers for segmentation of cardiac MR images.

PubMed

Sedai, Suman; Garnavi, Rahil; Roy, Pallab; Xi Liang

2015-08-01

Multi-atlas segmentation first registers each atlas image to the target image and transfers the label of atlas image to the coordinate system of the target image. The transferred labels are then combined, using a label fusion algorithm. In this paper, we propose a novel label fusion method which aggregates discriminative learning and generative modeling for segmentation of cardiac MR images. First, a probabilistic Random Forest classifier is trained as a discriminative model to obtain the prior probability of a label at the given voxel of the target image. Then, a probability distribution of image patches is modeled using Gaussian Mixture Model for each label, providing the likelihood of the voxel belonging to the label. The final label posterior is obtained by combining the classification score and the likelihood score under Bayesian rule. Comparative study performed on MICCAI 2013 SATA Segmentation Challenge demonstrates that our proposed hybrid label fusion algorithm is accurate than other five state-of-the-art label fusion methods. The proposed method obtains dice similarity coefficient of 0.94 and 0.92 in segmenting epicardium and endocardium respectively. Moreover, our label fusion method achieves more accurate segmentation results compared to four other label fusion methods.

Animals In Synchrotrons: Overcoming Challenges For High-Resolution, Live, Small-Animal Imaging

NASA Astrophysics Data System (ADS)

Donnelley, Martin; Parsons, David; Morgan, Kaye; Siu, Karen

2010-07-01

Physiological studies in small animals can be complicated, but the complexity is increased dramatically when performing live-animal synchrotron X-ray imaging studies. Our group has extensive experience in high-resolution live-animal imaging at the Japanese SPring-8 synchrotron, primarily examining airways in two-dimensions. These experiments normally image an area of 1.8 mm×1.2 mm at a pixel resolution of 0.45 μm and are performed with live, intact, anaesthetized mice. There are unique challenges in this experimental setting. Importantly, experiments must be performed in an isolated imaging hutch not specifically designed for small-animal imaging. This requires equipment adapted to remotely monitor animals, maintain their anesthesia, and deliver test substances while collecting images. The horizontal synchrotron X-ray beam has a fixed location and orientation that limits experimental flexibility. The extremely high resolution makes locating anatomical regions-of-interest slow and can result in a high radiation dose, and at this level of magnification small animal movements produce motion-artifacts that can render acquired images unusable. Here we describe our experimental techniques and how we have overcome several challenges involved in performing live mouse synchrotron imaging. Experiments have tested different mouse strains, with hairless strains minimizing overlying skin and hair artifacts. Different anesthetics have also be trialed due to the limited choices available at SPring-8. Tracheal-intubation methods have been refined and controlled-ventilation is now possible using a specialized small-animal ventilator. With appropriate animal restraint and respiratory-gating, motion-artifacts have been minimized. The animal orientation (supine vs. head-high) also appears to affect animal physiology, and can alter image quality. Our techniques and image quality at SPring-8 have dramatically improved and in the near future we plan to translate this experience to the Imaging and Medical Beamline at the Australian Synchrotron. Overcoming these challenges has permitted increasingly sophisticated imaging of animals with synchrotron X-rays, and we expect a bright future for these techniques.

Animals In Synchrotrons: Overcoming Challenges For High-Resolution, Live, Small-Animal Imaging

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

Donnelley, Martin; Parsons, David; Women's and Children's Health Research Institute, Adelaide, South Australia

Physiological studies in small animals can be complicated, but the complexity is increased dramatically when performing live-animal synchrotron X-ray imaging studies. Our group has extensive experience in high-resolution live-animal imaging at the Japanese SPring-8 synchrotron, primarily examining airways in two-dimensions. These experiments normally image an area of 1.8 mmx1.2 mm at a pixel resolution of 0.45 {mu}m and are performed with live, intact, anaesthetized mice.There are unique challenges in this experimental setting. Importantly, experiments must be performed in an isolated imaging hutch not specifically designed for small-animal imaging. This requires equipment adapted to remotely monitor animals, maintain their anesthesia, andmore » deliver test substances while collecting images. The horizontal synchrotron X-ray beam has a fixed location and orientation that limits experimental flexibility. The extremely high resolution makes locating anatomical regions-of-interest slow and can result in a high radiation dose, and at this level of magnification small animal movements produce motion-artifacts that can render acquired images unusable. Here we describe our experimental techniques and how we have overcome several challenges involved in performing live mouse synchrotron imaging.Experiments have tested different mouse strains, with hairless strains minimizing overlying skin and hair artifacts. Different anesthetics have also be trialed due to the limited choices available at SPring-8. Tracheal-intubation methods have been refined and controlled-ventilation is now possible using a specialized small-animal ventilator. With appropriate animal restraint and respiratory-gating, motion-artifacts have been minimized. The animal orientation (supine vs. head-high) also appears to affect animal physiology, and can alter image quality. Our techniques and image quality at SPring-8 have dramatically improved and in the near future we plan to translate this experience to the Imaging and Medical Beamline at the Australian Synchrotron.Overcoming these challenges has permitted increasingly sophisticated imaging of animals with synchrotron X-rays, and we expect a bright future for these techniques.« less

An improved artifact removal in exposure fusion with local linear constraints

NASA Astrophysics Data System (ADS)

Zhang, Hai; Yu, Mali

2018-04-01

In exposure fusion, it is challenging to remove artifacts because of camera motion and moving objects in the scene. An improved artifact removal method is proposed in this paper, which performs local linear adjustment in artifact removal progress. After determining a reference image, we first perform high-dynamic-range (HDR) deghosting to generate an intermediate image stack from the input image stack. Then, a linear Intensity Mapping Function (IMF) in each window is extracted based on the intensities of intermediate image and reference image, the intensity mean and variance of reference image. Finally, with the extracted local linear constraints, we reconstruct a target image stack, which can be directly used for fusing a single HDR-like image. Some experiments have been implemented and experimental results demonstrate that the proposed method is robust and effective in removing artifacts especially in the saturated regions of the reference image.

Roadmap on neurophotonics

NASA Astrophysics Data System (ADS)

Cho, Yong Ku; Zheng, Guoan; Augustine, George J.; Hochbaum, Daniel; Cohen, Adam; Knöpfel, Thomas; Pisanello, Ferruccio; Pavone, Francesco S.; Vellekoop, Ivo M.; Booth, Martin J.; Hu, Song; Zhu, Jiang; Chen, Zhongping; Hoshi, Yoko

2016-09-01

Mechanistic understanding of how the brain gives rise to complex behavioral and cognitive functions is one of science’s grand challenges. The technical challenges that we face as we attempt to gain a systems-level understanding of the brain are manifold. The brain’s structural complexity requires us to push the limit of imaging resolution and depth, while being able to cover large areas, resulting in enormous data acquisition and processing needs. Furthermore, it is necessary to detect functional activities and ‘map’ them onto the structural features. The functional activity occurs at multiple levels, using electrical and chemical signals. Certain electrical signals are only decipherable with sub-millisecond timescale resolution, while other modes of signals occur in minutes to hours. For these reasons, there is a wide consensus that new tools are necessary to undertake this daunting task. Optical techniques, due to their versatile and scalable nature, have great potentials to answer these challenges. Optical microscopy can now image beyond the diffraction limit, record multiple types of brain activity, and trace structural features across large areas of tissue. Genetically encoded molecular tools opened doors to controlling and detecting neural activity using light in specific cell types within the intact brain. Novel sample preparation methods that reduce light scattering have been developed, allowing whole brain imaging in rodent models. Adaptive optical methods have the potential to resolve images from deep brain regions. In this roadmap article, we showcase a few major advances in this area, survey the current challenges, and identify potential future needs that may be used as a guideline for the next steps to be taken.

Roadmap on neurophotonics

PubMed Central

Cho, Yong Ku; Zheng, Guoan; Augustine, George J; Hochbaum, Daniel; Cohen, Adam; Knöpfel, Thomas; Pisanello, Ferruccio; Pavone, Francesco S; Vellekoop, Ivo M; Booth, Martin J; Hu, Song; Zhu, Jiang; Chen, Zhongping; Hoshi, Yoko

2017-01-01

Mechanistic understanding of how the brain gives rise to complex behavioral and cognitive functions is one of science’s grand challenges. The technical challenges that we face as we attempt to gain a systems-level understanding of the brain are manifold. The brain’s structural complexity requires us to push the limit of imaging resolution and depth, while being able to cover large areas, resulting in enormous data acquisition and processing needs. Furthermore, it is necessary to detect functional activities and ‘map’ them onto the structural features. The functional activity occurs at multiple levels, using electrical and chemical signals. Certain electrical signals are only decipherable with sub-millisecond timescale resolution, while other modes of signals occur in minutes to hours. For these reasons, there is a wide consensus that new tools are necessary to undertake this daunting task. Optical techniques, due to their versatile and scalable nature, have great potentials to answer these challenges. Optical microscopy can now image beyond the diffraction limit, record multiple types of brain activity, and trace structural features across large areas of tissue. Genetically encoded molecular tools opened doors to controlling and detecting neural activity using light in specific cell types within the intact brain. Novel sample preparation methods that reduce light scattering have been developed, allowing whole brain imaging in rodent models. Adaptive optical methods have the potential to resolve images from deep brain regions. In this roadmap article, we showcase a few major advances in this area, survey the current challenges, and identify potential future needs that may be used as a guideline for the next steps to be taken. PMID:28386392

Data augmentation-assisted deep learning of hand-drawn partially colored sketches for visual search

PubMed Central

Muhammad, Khan; Baik, Sung Wook

2017-01-01

In recent years, image databases are growing at exponential rates, making their management, indexing, and retrieval, very challenging. Typical image retrieval systems rely on sample images as queries. However, in the absence of sample query images, hand-drawn sketches are also used. The recent adoption of touch screen input devices makes it very convenient to quickly draw shaded sketches of objects to be used for querying image databases. This paper presents a mechanism to provide access to visual information based on users’ hand-drawn partially colored sketches using touch screen devices. A key challenge for sketch-based image retrieval systems is to cope with the inherent ambiguity in sketches due to the lack of colors, textures, shading, and drawing imperfections. To cope with these issues, we propose to fine-tune a deep convolutional neural network (CNN) using augmented dataset to extract features from partially colored hand-drawn sketches for query specification in a sketch-based image retrieval framework. The large augmented dataset contains natural images, edge maps, hand-drawn sketches, de-colorized, and de-texturized images which allow CNN to effectively model visual contents presented to it in a variety of forms. The deep features extracted from CNN allow retrieval of images using both sketches and full color images as queries. We also evaluated the role of partial coloring or shading in sketches to improve the retrieval performance. The proposed method is tested on two large datasets for sketch recognition and sketch-based image retrieval and achieved better classification and retrieval performance than many existing methods. PMID:28859140

Defect detection of castings in radiography images using a robust statistical feature.

PubMed

Zhao, Xinyue; He, Zaixing; Zhang, Shuyou

2014-01-01

One of the most commonly used optical methods for defect detection is radiographic inspection. Compared with methods that extract defects directly from the radiography image, model-based methods deal with the case of an object with complex structure well. However, detection of small low-contrast defects in nonuniformly illuminated images is still a major challenge for them. In this paper, we present a new method based on the grayscale arranging pairs (GAP) feature to detect casting defects in radiography images automatically. First, a model is built using pixel pairs with a stable intensity relationship based on the GAP feature from previously acquired images. Second, defects can be extracted by comparing the difference of intensity-difference signs between the input image and the model statistically. The robustness of the proposed method to noise and illumination variations has been verified on casting radioscopic images with defects. The experimental results showed that the average computation time of the proposed method in the testing stage is 28 ms per image on a computer with a Pentium Core 2 Duo 3.00 GHz processor. For the comparison, we also evaluated the performance of the proposed method as well as that of the mixture-of-Gaussian-based and crossing line profile methods. The proposed method achieved 2.7% and 2.0% false negative rates in the noise and illumination variation experiments, respectively.

eHUGS: Enhanced Hierarchical Unbiased Graph Shrinkage for Efficient Groupwise Registration

PubMed Central

Wu, Guorong; Peng, Xuewei; Ying, Shihui; Wang, Qian; Yap, Pew-Thian; Shen, Dan; Shen, Dinggang

2016-01-01

Effective and efficient spatial normalization of a large population of brain images is critical for many clinical and research studies, but it is technically very challenging. A commonly used approach is to choose a certain image as the template and then align all other images in the population to this template by applying pairwise registration. To avoid the potential bias induced by the inappropriate template selection, groupwise registration methods have been proposed to simultaneously register all images to a latent common space. However, current groupwise registration methods do not make full use of image distribution information for more accurate registration. In this paper, we present a novel groupwise registration method that harnesses the image distribution information by capturing the image distribution manifold using a hierarchical graph with its nodes representing the individual images. More specifically, a low-level graph describes the image distribution in each subgroup, and a high-level graph encodes the relationship between representative images of subgroups. Given the graph representation, we can register all images to the common space by dynamically shrinking the graph on the image manifold. The topology of the entire image distribution is always maintained during graph shrinkage. Evaluations on two datasets, one for 80 elderly individuals and one for 285 infants, indicate that our method can yield promising results. PMID:26800361

Local facet approximation for image stitching

NASA Astrophysics Data System (ADS)

Li, Jing; Lai, Shiming; Liu, Yu; Wang, Zhengming; Zhang, Maojun

2018-01-01

Image stitching aims at eliminating multiview parallax and generating a seamless panorama given a set of input images. This paper proposes a local adaptive stitching method, which could achieve both accurate and robust image alignments across the whole panorama. A transformation estimation model is introduced by approximating the scene as a combination of neighboring facets. Then, the local adaptive stitching field is constructed using a series of linear systems of the facet parameters, which enables the parallax handling in three-dimensional space. We also provide a concise but effective global projectivity preserving technique that smoothly varies the transformations from local adaptive to global planar. The proposed model is capable of stitching both normal images and fisheye images. The efficiency of our method is quantitatively demonstrated in the comparative experiments on several challenging cases.

Airplane detection in remote sensing images using convolutional neural networks

NASA Astrophysics Data System (ADS)

Ouyang, Chao; Chen, Zhong; Zhang, Feng; Zhang, Yifei

2018-03-01

Airplane detection in remote sensing images remains a challenging problem and has also been taking a great interest to researchers. In this paper we propose an effective method to detect airplanes in remote sensing images using convolutional neural networks. Deep learning methods show greater advantages than the traditional methods with the rise of deep neural networks in target detection, and we give an explanation why this happens. To improve the performance on detection of airplane, we combine a region proposal algorithm with convolutional neural networks. And in the training phase, we divide the background into multi classes rather than one class, which can reduce false alarms. Our experimental results show that the proposed method is effective and robust in detecting airplane.

Graph-cut based discrete-valued image reconstruction.

PubMed

Tuysuzoglu, Ahmet; Karl, W Clem; Stojanovic, Ivana; Castañòn, David; Ünlü, M Selim

2015-05-01

Efficient graph-cut methods have been used with great success for labeling and denoising problems occurring in computer vision. Unfortunately, the presence of linear image mappings has prevented the use of these techniques in most discrete-amplitude image reconstruction problems. In this paper, we develop a graph-cut based framework for the direct solution of discrete amplitude linear image reconstruction problems cast as regularized energy function minimizations. We first analyze the structure of discrete linear inverse problem cost functions to show that the obstacle to the application of graph-cut methods to their solution is the variable mixing caused by the presence of the linear sensing operator. We then propose to use a surrogate energy functional that overcomes the challenges imposed by the sensing operator yet can be utilized efficiently in existing graph-cut frameworks. We use this surrogate energy functional to devise a monotonic iterative algorithm for the solution of discrete valued inverse problems. We first provide experiments using local convolutional operators and show the robustness of the proposed technique to noise and stability to changes in regularization parameter. Then we focus on nonlocal, tomographic examples where we consider limited-angle data problems. We compare our technique with state-of-the-art discrete and continuous image reconstruction techniques. Experiments show that the proposed method outperforms state-of-the-art techniques in challenging scenarios involving discrete valued unknowns.

From drones to ASO: Using 'Structure-From-Motion' photogrammetry to quantify variations in snow depth at multiple scales

NASA Astrophysics Data System (ADS)

Skiles, M.

2017-12-01

The ability to accurately measure and manage the natural snow water reservoir in mountainous regions has its challenges, namely mapping of snowpack depth and snow water equivalent (SWE). Presented here is a scalable method that differentially maps snow depth using Structure from Motion (SfM); a photogrammetric technique that uses 2d images to create a 3D model/Digital Surface Model (DSM). There are challenges with applying SfM to snow, namely, relatively uniform snow brightness can make it difficult to produce quality images needed for processing, and vegetation can limit the ability to `see' through the canopy to map both the ground and snow beneath. New techniques implemented in the method to adapt to these challenges will be demonstrated. Results include a time series at (1) the plot scale, imaged with an unmanned areal vehicle (DJI Phantom 2 adapted with Sony A5100) over the Utah Department of Transportation Atwater Study Plot in Little Cottonwood Canyon, UT, and at (2) the mountain watershed scale, imaged from the RGB camera aboard the Airborne Snow Observatory (ASO), over the headwaters of the Uncompahgre River in the San Juan Mountains, CO. At the plot scale we present comparisons to measured snow depth, and at the watershed scale we present comparisons to the ASO lidar DSM. This method is of interest due to its low cost relative to lidar, making it an accessible tool for snow research and the management of water resources. With advancing unmanned aerial vehicle technology there are implications for scalability to map snow depth, and SWE, across large basins.

ISCCP-B1U Geostationary Satellite VIS Channel Vicarious Calibration/Validation in support of creation of long term climate data records

NASA Astrophysics Data System (ADS)

Gopalan, A.; Doelling, D. R.; Bhatt, R.; Haney, C.; Scarino, B. R.

2017-12-01

The International Satellite Cloud Climatology Project (ISCCP) provides a 40-year geostationary (GEO) imager record from satellites worldwide of 3-hourly cloud properties and surface reflectances. ISCCP B1 data archived at the National Climatic Data Center (NCDC) are a collection of measurements from imagers on international GEO meteorological satellites which are sampled to approximately 10-km and at 3-hour intervals. ISCCP coordinated the ingestion of 3-hour geostationary imager pixel level radiances and placed them in a common and consistent unified format (ISCCP-B1U) across GEO imagers and archived the datasets at NCDC for future reprocessing efforts. The GEO imagers in the B1U record lacked onboard calibration to monitor the temporal stability of the visible channel. Consistent calibration of the B1U GEO imager record opens up the potential for their use in global climate studies. The NASA CERES project released the Edition4 products, where the GEO imager calibration has been referenced to the Aqua-MODIS band-1 Collection 6 calibration. This was done by matching coincident GEO and MODIS radiance pairs to transfer the MODIS calibration. This primary method was then validated by the independent vicarious calibration methods using invariant desert and deep convective cloud (DCC) targets. In this study we extend these vicarious methods to the historical ISCCP-B1U format GEO record going back from 2000-1978 while addressing some of the challenges viz. the short historical GEO imager lifetimes, spurious imagery, non-stationary VIS channel space counts, data source processing differences, inadequate spectral response function characterization and possible wavelength dependent degradations. Another challenge, is the occasional abrupt calibration gain discontinuities in time, these are validated by tracking the brightest pixels over time. We discuss the methodology used to address some of the challenges and present results from the two independent vicarious calibration approaches that are then merged according to their respective uncertainties to obtain optimal and self-consistent calibration gain timelines for the various GEO sensors in the historical record in support of global climate change studies

Topical Review: Unique Contributions of Magnetic Resonance Imaging to Pediatric Psychology Research

PubMed Central

Duraccio, Kara M.; Carbine, Kaylie M.; Kirwan, C. Brock

2016-01-01

Objective This review aims to provide a brief introduction of the utility of magnetic resonance imaging (MRI) methods in pediatric psychology research, describe several exemplar studies that highlight the unique benefits of MRI techniques for pediatric psychology research, and detail methods for addressing several challenges inherent to pediatric MRI research. Methods Literature review. Results Numerous useful applications of MRI research in pediatric psychology have been illustrated in published research. MRI methods yield information that cannot be obtained using neuropsychological or behavioral measures. Conclusions Using MRI in pediatric psychology research may facilitate examination of neural structures and processes that underlie health behaviors. Challenges inherent to conducting MRI research with pediatric research participants (e.g., head movement) may be addressed using evidence-based strategies. We encourage pediatric psychology researchers to consider adopting MRI techniques to answer research questions relevant to pediatric health and illness. PMID:26141118

Comparison of texture synthesis methods for content generation in ultrasound simulation for training

NASA Astrophysics Data System (ADS)

Mattausch, Oliver; Ren, Elizabeth; Bajka, Michael; Vanhoey, Kenneth; Goksel, Orcun

2017-03-01

Navigation and interpretation of ultrasound (US) images require substantial expertise, the training of which can be aided by virtual-reality simulators. However, a major challenge in creating plausible simulated US images is the generation of realistic ultrasound speckle. Since typical ultrasound speckle exhibits many properties of Markov Random Fields, it is conceivable to use texture synthesis for generating plausible US appearance. In this work, we investigate popular classes of texture synthesis methods for generating realistic US content. In a user study, we evaluate their performance for reproducing homogeneous tissue regions in B-mode US images from small image samples of similar tissue and report the best-performing synthesis methods. We further show that regression trees can be used on speckle texture features to learn a predictor for US realism.

Integration of Sparse Multi-modality Representation and Anatomical Constraint for Isointense Infant Brain MR Image Segmentation

PubMed Central

Wang, Li; Shi, Feng; Gao, Yaozong; Li, Gang; Gilmore, John H.; Lin, Weili; Shen, Dinggang

2014-01-01

Segmentation of infant brain MR images is challenging due to poor spatial resolution, severe partial volume effect, and the ongoing maturation and myelination process. During the first year of life, the brain image contrast between white and gray matters undergoes dramatic changes. In particular, the image contrast inverses around 6–8 months of age, where the white and gray matter tissues are isointense in T1 and T2 weighted images and hence exhibit the extremely low tissue contrast, posing significant challenges for automated segmentation. In this paper, we propose a general framework that adopts sparse representation to fuse the multi-modality image information and further incorporate the anatomical constraints for brain tissue segmentation. Specifically, we first derive an initial segmentation from a library of aligned images with ground-truth segmentations by using sparse representation in a patch-based fashion for the multi-modality T1, T2 and FA images. The segmentation result is further iteratively refined by integration of the anatomical constraint. The proposed method was evaluated on 22 infant brain MR images acquired at around 6 months of age by using a leave-one-out cross-validation, as well as other 10 unseen testing subjects. Our method achieved a high accuracy for the Dice ratios that measure the volume overlap between automated and manual segmentations, i.e., 0.889±0.008 for white matter and 0.870±0.006 for gray matter. PMID:24291615

Multiscale nonlinear microscopy and widefield white light imaging enables rapid histological imaging of surgical specimen margins

PubMed Central

Giacomelli, Michael G.; Yoshitake, Tadayuki; Cahill, Lucas C.; Vardeh, Hilde; Quintana, Liza M.; Faulkner-Jones, Beverly E.; Brooker, Jeff; Connolly, James L.; Fujimoto, James G.

2018-01-01

The ability to histologically assess surgical specimens in real-time is a long-standing challenge in cancer surgery, including applications such as breast conserving therapy (BCT). Up to 40% of women treated with BCT for breast cancer require a repeat surgery due to postoperative histological findings of close or positive surgical margins using conventional formalin fixed paraffin embedded histology. Imaging technologies such as nonlinear microscopy (NLM), combined with exogenous fluorophores can rapidly provide virtual H&E imaging of surgical specimens without requiring microtome sectioning, facilitating intraoperative assessment of margin status. However, the large volume of typical surgical excisions combined with the need for rapid assessment, make comprehensive cellular resolution margin assessment during surgery challenging. To address this limitation, we developed a multiscale, real-time microscope with variable magnification NLM and real-time, co-registered position display using a widefield white light imaging system. Margin assessment can be performed rapidly under operator guidance to image specific regions of interest located using widefield imaging. Using simulated surgical margins dissected from human breast excisions, we demonstrate that multi-centimeter margins can be comprehensively imaged at cellular resolution, enabling intraoperative margin assessment. These methods are consistent with pathology assessment performed using frozen section analysis (FSA), however NLM enables faster and more comprehensive assessment of surgical specimens because imaging can be performed without freezing and cryo-sectioning. Therefore, NLM methods have the potential to be applied to a wide range of intra-operative applications. PMID:29761001

A multi-frequency iterative imaging method for discontinuous inverse medium problem

NASA Astrophysics Data System (ADS)

Zhang, Lei; Feng, Lixin

2018-06-01

The inverse medium problem with discontinuous refractive index is a kind of challenging inverse problem. We employ the primal dual theory and fast solution of integral equations, and propose a new iterative imaging method. The selection criteria of regularization parameter is given by the method of generalized cross-validation. Based on multi-frequency measurements of the scattered field, a recursive linearization algorithm has been presented with respect to the frequency from low to high. We also discuss the initial guess selection strategy by semi-analytical approaches. Numerical experiments are presented to show the effectiveness of the proposed method.

Adaptive image inversion of contrast 3D echocardiography for enabling automated analysis.

PubMed

Shaheen, Anjuman; Rajpoot, Kashif

2015-08-01

Contrast 3D echocardiography (C3DE) is commonly used to enhance the visual quality of ultrasound images in comparison with non-contrast 3D echocardiography (3DE). Although the image quality in C3DE is perceived to be improved for visual analysis, however it actually deteriorates for the purpose of automatic or semi-automatic analysis due to higher speckle noise and intensity inhomogeneity. Therefore, the LV endocardial feature extraction and segmentation from the C3DE images remains a challenging problem. To address this challenge, this work proposes an adaptive pre-processing method to invert the appearance of C3DE image. The image inversion is based on an image intensity threshold value which is automatically estimated through image histogram analysis. In the inverted appearance, the LV cavity appears dark while the myocardium appears bright thus making it similar in appearance to a 3DE image. Moreover, the resulting inverted image has high contrast and low noise appearance, yielding strong LV endocardium boundary and facilitating feature extraction for segmentation. Our results demonstrate that the inverse appearance of contrast image enables the subsequent LV segmentation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Thermalnet: a Deep Convolutional Network for Synthetic Thermal Image Generation

NASA Astrophysics Data System (ADS)

Kniaz, V. V.; Gorbatsevich, V. S.; Mizginov, V. A.

2017-05-01

Deep convolutional neural networks have dramatically changed the landscape of the modern computer vision. Nowadays methods based on deep neural networks show the best performance among image recognition and object detection algorithms. While polishing of network architectures received a lot of scholar attention, from the practical point of view the preparation of a large image dataset for a successful training of a neural network became one of major challenges. This challenge is particularly profound for image recognition in wavelengths lying outside the visible spectrum. For example no infrared or radar image datasets large enough for successful training of a deep neural network are available to date in public domain. Recent advances of deep neural networks prove that they are also capable to do arbitrary image transformations such as super-resolution image generation, grayscale image colorisation and imitation of style of a given artist. Thus a natural question arise: how could be deep neural networks used for augmentation of existing large image datasets? This paper is focused on the development of the Thermalnet deep convolutional neural network for augmentation of existing large visible image datasets with synthetic thermal images. The Thermalnet network architecture is inspired by colorisation deep neural networks.

Radiomic analysis in prediction of Human Papilloma Virus status.

PubMed

Yu, Kaixian; Zhang, Youyi; Yu, Yang; Huang, Chao; Liu, Rongjie; Li, Tengfei; Yang, Liuqing; Morris, Jeffrey S; Baladandayuthapani, Veerabhadran; Zhu, Hongtu

2017-12-01

Human Papilloma Virus (HPV) has been associated with oropharyngeal cancer prognosis. Traditionally the HPV status is tested through invasive lab test. Recently, the rapid development of statistical image analysis techniques has enabled precise quantitative analysis of medical images. The quantitative analysis of Computed Tomography (CT) provides a non-invasive way to assess HPV status for oropharynx cancer patients. We designed a statistical radiomics approach analyzing CT images to predict HPV status. Various radiomics features were extracted from CT scans, and analyzed using statistical feature selection and prediction methods. Our approach ranked the highest in the 2016 Medical Image Computing and Computer Assisted Intervention (MICCAI) grand challenge: Oropharynx Cancer (OPC) Radiomics Challenge, Human Papilloma Virus (HPV) Status Prediction. Further analysis on the most relevant radiomic features distinguishing HPV positive and negative subjects suggested that HPV positive patients usually have smaller and simpler tumors.

Noisy Ocular Recognition Based on Three Convolutional Neural Networks

PubMed Central

Lee, Min Beom; Hong, Hyung Gil; Park, Kang Ryoung

2017-01-01

In recent years, the iris recognition system has been gaining increasing acceptance for applications such as access control and smartphone security. When the images of the iris are obtained under unconstrained conditions, an issue of undermined quality is caused by optical and motion blur, off-angle view (the user’s eyes looking somewhere else, not into the front of the camera), specular reflection (SR) and other factors. Such noisy iris images increase intra-individual variations and, as a result, reduce the accuracy of iris recognition. A typical iris recognition system requires a near-infrared (NIR) illuminator along with an NIR camera, which are larger and more expensive than fingerprint recognition equipment. Hence, many studies have proposed methods of using iris images captured by a visible light camera without the need for an additional illuminator. In this research, we propose a new recognition method for noisy iris and ocular images by using one iris and two periocular regions, based on three convolutional neural networks (CNNs). Experiments were conducted by using the noisy iris challenge evaluation-part II (NICE.II) training dataset (selected from the university of Beira iris (UBIRIS).v2 database), mobile iris challenge evaluation (MICHE) database, and institute of automation of Chinese academy of sciences (CASIA)-Iris-Distance database. As a result, the method proposed by this study outperformed previous methods. PMID:29258217

BOLD magnetic resonance imaging in nephrology

PubMed Central

Hall, Michael E; Jordan, Jennifer H; Juncos, Luis A; Hundley, W Gregory; Hall, John E

2018-01-01

Magnetic resonance (MR) imaging, a non-invasive modality that provides anatomic and physiologic information, is increasingly used for diagnosis of pathophysiologic conditions and for understanding renal physiology in humans. Although functional MR imaging methods were pioneered to investigate the brain, they also offer powerful techniques for investigation of other organ systems such as the kidneys. However, imaging the kidneys provides unique challenges due to potential complications from contrast agents. Therefore, development of non-contrast techniques to study kidney anatomy and physiology is important. Blood oxygen level-dependent (BOLD) MR is a non-contrast imaging technique that provides functional information related to renal tissue oxygenation in various pathophysiologic conditions. Here we discuss technical considerations, clinical uses and future directions for use of BOLD MR as well as complementary MR techniques to better understand renal pathophysiology. Our intent is to summarize kidney BOLD MR applications for the clinician rather than focusing on the complex physical challenges that functional MR imaging encompasses; however, we briefly discuss some of those issues. PMID:29559807

Image interpolation used in three-dimensional range data compression.

PubMed

Zhang, Shaoze; Zhang, Jianqi; Huang, Xi; Liu, Delian

2016-05-20

Advances in the field of three-dimensional (3D) scanning have made the acquisition of 3D range data easier and easier. However, with the large size of 3D range data comes the challenge of storing and transmitting it. To address this challenge, this paper presents a framework to further compress 3D range data using image interpolation. We first use a virtual fringe-projection system to store 3D range data as images, and then apply the interpolation algorithm to the images to reduce their resolution to further reduce the data size. When the 3D range data are needed, the low-resolution image is scaled up to its original resolution by applying the interpolation algorithm, and then the scaled-up image is decoded and the 3D range data are recovered according to the decoded result. Experimental results show that the proposed method could further reduce the data size while maintaining a low rate of error.

Social Image Tag Ranking by Two-View Learning

NASA Astrophysics Data System (ADS)

Zhuang, Jinfeng; Hoi, Steven C. H.

Tags play a central role in text-based social image retrieval and browsing. However, the tags annotated by web users could be noisy, irrelevant, and often incomplete for describing the image contents, which may severely deteriorate the performance of text-based image retrieval models. In order to solve this problem, researchers have proposed techniques to rank the annotated tags of a social image according to their relevance to the visual content of the image. In this paper, we aim to overcome the challenge of social image tag ranking for a corpus of social images with rich user-generated tags by proposing a novel two-view learning approach. It can effectively exploit both textual and visual contents of social images to discover the complicated relationship between tags and images. Unlike the conventional learning approaches that usually assumes some parametric models, our method is completely data-driven and makes no assumption about the underlying models, making the proposed solution practically more effective. We formulate our method as an optimization task and present an efficient algorithm to solve it. To evaluate the efficacy of our method, we conducted an extensive set of experiments by applying our technique to both text-based social image retrieval and automatic image annotation tasks. Our empirical results showed that the proposed method can be more effective than the conventional approaches.

Automated segmentation and isolation of touching cell nuclei in cytopathology smear images of pleural effusion using distance transform watershed method

NASA Astrophysics Data System (ADS)

Win, Khin Yadanar; Choomchuay, Somsak; Hamamoto, Kazuhiko

2017-06-01

The automated segmentation of cell nuclei is an essential stage in the quantitative image analysis of cell nuclei extracted from smear cytology images of pleural fluid. Cell nuclei can indicate cancer as the characteristics of cell nuclei are associated with cells proliferation and malignancy in term of size, shape and the stained color. Nevertheless, automatic nuclei segmentation has remained challenging due to the artifacts caused by slide preparation, nuclei heterogeneity such as the poor contrast, inconsistent stained color, the cells variation, and cells overlapping. In this paper, we proposed a watershed-based method that is capable to segment the nuclei of the variety of cells from cytology pleural fluid smear images. Firstly, the original image is preprocessed by converting into the grayscale image and enhancing by adjusting and equalizing the intensity using histogram equalization. Next, the cell nuclei are segmented using OTSU thresholding as the binary image. The undesirable artifacts are eliminated using morphological operations. Finally, the distance transform based watershed method is applied to isolate the touching and overlapping cell nuclei. The proposed method is tested with 25 Papanicolaou (Pap) stained pleural fluid images. The accuracy of our proposed method is 92%. The method is relatively simple, and the results are very promising.

Single Anisotropic 3-D MR Image Upsampling via Overcomplete Dictionary Trained From In-Plane High Resolution Slices.

PubMed

Jia, Yuanyuan; He, Zhongshi; Gholipour, Ali; Warfield, Simon K

2016-11-01

In magnetic resonance (MR), hardware limitation, scanning time, and patient comfort often result in the acquisition of anisotropic 3-D MR images. Enhancing image resolution is desired but has been very challenging in medical image processing. Super resolution reconstruction based on sparse representation and overcomplete dictionary has been lately employed to address this problem; however, these methods require extra training sets, which may not be always available. This paper proposes a novel single anisotropic 3-D MR image upsampling method via sparse representation and overcomplete dictionary that is trained from in-plane high resolution slices to upsample in the out-of-plane dimensions. The proposed method, therefore, does not require extra training sets. Abundant experiments, conducted on simulated and clinical brain MR images, show that the proposed method is more accurate than classical interpolation. When compared to a recent upsampling method based on the nonlocal means approach, the proposed method did not show improved results at low upsampling factors with simulated images, but generated comparable results with much better computational efficiency in clinical cases. Therefore, the proposed approach can be efficiently implemented and routinely used to upsample MR images in the out-of-planes views for radiologic assessment and postacquisition processing.

A Novel Segmentation Approach Combining Region- and Edge-Based Information for Ultrasound Images

PubMed Central

Luo, Yaozhong; Liu, Longzhong; Li, Xuelong

2017-01-01

Ultrasound imaging has become one of the most popular medical imaging modalities with numerous diagnostic applications. However, ultrasound (US) image segmentation, which is the essential process for further analysis, is a challenging task due to the poor image quality. In this paper, we propose a new segmentation scheme to combine both region- and edge-based information into the robust graph-based (RGB) segmentation method. The only interaction required is to select two diagonal points to determine a region of interest (ROI) on the original image. The ROI image is smoothed by a bilateral filter and then contrast-enhanced by histogram equalization. Then, the enhanced image is filtered by pyramid mean shift to improve homogeneity. With the optimization of particle swarm optimization (PSO) algorithm, the RGB segmentation method is performed to segment the filtered image. The segmentation results of our method have been compared with the corresponding results obtained by three existing approaches, and four metrics have been used to measure the segmentation performance. The experimental results show that the method achieves the best overall performance and gets the lowest ARE (10.77%), the second highest TPVF (85.34%), and the second lowest FPVF (4.48%). PMID:28536703

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

PubMed

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

2015-01-01

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

Fusion of visible and near-infrared images based on luminance estimation by weighted luminance algorithm

NASA Astrophysics Data System (ADS)

Wang, Zhun; Cheng, Feiyan; Shi, Junsheng; Huang, Xiaoqiao

2018-01-01

In a low-light scene, capturing color images needs to be at a high-gain setting or a long-exposure setting to avoid a visible flash. However, such these setting will lead to color images with serious noise or motion blur. Several methods have been proposed to improve a noise-color image through an invisible near infrared flash image. A novel method is that the luminance component and the chroma component of the improved color image are estimated from different image sources [1]. The luminance component is estimated mainly from the NIR image via a spectral estimation, and the chroma component is estimated from the noise-color image by denoising. However, it is challenging to estimate the luminance component. This novel method to estimate the luminance component needs to generate the learning data pairs, and the processes and algorithm are complex. It is difficult to achieve practical application. In order to reduce the complexity of the luminance estimation, an improved luminance estimation algorithm is presented in this paper, which is to weight the NIR image and the denoised-color image and the weighted coefficients are based on the mean value and standard deviation of both images. Experimental results show that the same fusion effect at aspect of color fidelity and texture quality is achieved, compared the proposed method with the novel method, however, the algorithm is more simple and practical.

A resolution-enhancing image reconstruction method for few-view differential phase-contrast tomography

NASA Astrophysics Data System (ADS)

Guan, Huifeng; Anastasio, Mark A.

2017-03-01

It is well-known that properly designed image reconstruction methods can facilitate reductions in imaging doses and data-acquisition times in tomographic imaging. The ability to do so is particularly important for emerging modalities such as differential X-ray phase-contrast tomography (D-XPCT), which are currently limited by these factors. An important application of D-XPCT is high-resolution imaging of biomedical samples. However, reconstructing high-resolution images from few-view tomographic measurements remains a challenging task. In this work, a two-step sub-space reconstruction strategy is proposed and investigated for use in few-view D-XPCT image reconstruction. It is demonstrated that the resulting iterative algorithm can mitigate the high-frequency information loss caused by data incompleteness and produce images that have better preserved high spatial frequency content than those produced by use of a conventional penalized least squares (PLS) estimator.

Automatic neuron segmentation and neural network analysis method for phase contrast microscopy images.

PubMed

Pang, Jincheng; Özkucur, Nurdan; Ren, Michael; Kaplan, David L; Levin, Michael; Miller, Eric L

2015-11-01

Phase Contrast Microscopy (PCM) is an important tool for the long term study of living cells. Unlike fluorescence methods which suffer from photobleaching of fluorophore or dye molecules, PCM image contrast is generated by the natural variations in optical index of refraction. Unfortunately, the same physical principles which allow for these studies give rise to complex artifacts in the raw PCM imagery. Of particular interest in this paper are neuron images where these image imperfections manifest in very different ways for the two structures of specific interest: cell bodies (somas) and dendrites. To address these challenges, we introduce a novel parametric image model using the level set framework and an associated variational approach which simultaneously restores and segments this class of images. Using this technique as the basis for an automated image analysis pipeline, results for both the synthetic and real images validate and demonstrate the advantages of our approach.

Discriminative parameter estimation for random walks segmentation.

PubMed

Baudin, Pierre-Yves; Goodman, Danny; Kumrnar, Puneet; Azzabou, Noura; Carlier, Pierre G; Paragios, Nikos; Kumar, M Pawan

2013-01-01

The Random Walks (RW) algorithm is one of the most efficient and easy-to-use probabilistic segmentation methods. By combining contrast terms with prior terms, it provides accurate segmentations of medical images in a fully automated manner. However, one of the main drawbacks of using the RW algorithm is that its parameters have to be hand-tuned. we propose a novel discriminative learning framework that estimates the parameters using a training dataset. The main challenge we face is that the training samples are not fully supervised. Specifically, they provide a hard segmentation of the images, instead of a probabilistic segmentation. We overcome this challenge by treating the optimal probabilistic segmentation that is compatible with the given hard segmentation as a latent variable. This allows us to employ the latent support vector machine formulation for parameter estimation. We show that our approach significantly outperforms the baseline methods on a challenging dataset consisting of real clinical 3D MRI volumes of skeletal muscles.

Batch settling curve registration via image data modeling.

PubMed

Derlon, Nicolas; Thürlimann, Christian; Dürrenmatt, David; Villez, Kris

2017-05-01

To this day, obtaining reliable characterization of sludge settling properties remains a challenging and time-consuming task. Without such assessments however, optimal design and operation of secondary settling tanks is challenging and conservative approaches will remain necessary. With this study, we show that automated sludge blanket height registration and zone settling velocity estimation is possible thanks to analysis of images taken during batch settling experiments. The experimental setup is particularly interesting for practical applications as it consists of off-the-shelf components only, no moving parts are required, and the software is released publicly. Furthermore, the proposed multivariate shape constrained spline model for image analysis appears to be a promising method for reliable sludge blanket height profile registration. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Dynamic Graph Cuts Method with Integrated Multiple Feature Maps for Segmenting Kidneys in 2D Ultrasound Images.

PubMed

Zheng, Qiang; Warner, Steven; Tasian, Gregory; Fan, Yong

2018-02-12

Automatic segmentation of kidneys in ultrasound (US) images remains a challenging task because of high speckle noise, low contrast, and large appearance variations of kidneys in US images. Because texture features may improve the US image segmentation performance, we propose a novel graph cuts method to segment kidney in US images by integrating image intensity information and texture feature maps. We develop a new graph cuts-based method to segment kidney US images by integrating original image intensity information and texture feature maps extracted using Gabor filters. To handle large appearance variation within kidney images and improve computational efficiency, we build a graph of image pixels close to kidney boundary instead of building a graph of the whole image. To make the kidney segmentation robust to weak boundaries, we adopt localized regional information to measure similarity between image pixels for computing edge weights to build the graph of image pixels. The localized graph is dynamically updated and the graph cuts-based segmentation iteratively progresses until convergence. Our method has been evaluated based on kidney US images of 85 subjects. The imaging data of 20 randomly selected subjects were used as training data to tune parameters of the image segmentation method, and the remaining data were used as testing data for validation. Experiment results demonstrated that the proposed method obtained promising segmentation results for bilateral kidneys (average Dice index = 0.9446, average mean distance = 2.2551, average specificity = 0.9971, average accuracy = 0.9919), better than other methods under comparison (P < .05, paired Wilcoxon rank sum tests). The proposed method achieved promising performance for segmenting kidneys in two-dimensional US images, better than segmentation methods built on any single channel of image information. This method will facilitate extraction of kidney characteristics that may predict important clinical outcomes such as progression of chronic kidney disease. Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Imaging Genetics and Genomics in Psychiatry: A Critical Review of Progress and Potential.

PubMed

Bogdan, Ryan; Salmeron, Betty Jo; Carey, Caitlin E; Agrawal, Arpana; Calhoun, Vince D; Garavan, Hugh; Hariri, Ahmad R; Heinz, Andreas; Hill, Matthew N; Holmes, Andrew; Kalin, Ned H; Goldman, David

2017-08-01

Imaging genetics and genomics research has begun to provide insight into the molecular and genetic architecture of neural phenotypes and the neural mechanisms through which genetic risk for psychopathology may emerge. As it approaches its third decade, imaging genetics is confronted by many challenges, including the proliferation of studies using small sample sizes and diverse designs, limited replication, problems with harmonization of neural phenotypes for meta-analysis, unclear mechanisms, and evidence that effect sizes may be more modest than originally posited, with increasing evidence of polygenicity. These concerns have encouraged the field to grow in many new directions, including the development of consortia and large-scale data collection projects and the use of novel methods (e.g., polygenic approaches, machine learning) that enhance the quality of imaging genetic studies but also introduce new challenges. We critically review progress in imaging genetics and offer suggestions and highlight potential pitfalls of novel approaches. Ultimately, the strength of imaging genetics and genomics lies in their translational and integrative potential with other research approaches (e.g., nonhuman animal models, psychiatric genetics, pharmacologic challenge) to elucidate brain-based pathways that give rise to the vast individual differences in behavior as well as risk for psychopathology. Copyright © 2017 Society of Biological Psychiatry. All rights reserved.

Fourier spatial frequency analysis for image classification: training the training set

NASA Astrophysics Data System (ADS)

Johnson, Timothy H.; Lhamo, Yigah; Shi, Lingyan; Alfano, Robert R.; Russell, Stewart

2016-04-01

The Directional Fourier Spatial Frequencies (DFSF) of a 2D image can identify similarity in spatial patterns within groups of related images. A Support Vector Machine (SVM) can then be used to classify images if the inter-image variance of the FSF in the training set is bounded. However, if variation in FSF increases with training set size, accuracy may decrease as the size of the training set increases. This calls for a method to identify a set of training images from among the originals that can form a vector basis for the entire class. Applying the Cauchy product method we extract the DFSF spectrum from radiographs of osteoporotic bone, and use it as a matched filter set to eliminate noise and image specific frequencies, and demonstrate that selection of a subset of superclassifiers from within a set of training images improves SVM accuracy. Central to this challenge is that the size of the search space can become computationally prohibitive for all but the smallest training sets. We are investigating methods to reduce the search space to identify an optimal subset of basis training images.

Degraded Chinese rubbing images thresholding based on local first-order statistics

NASA Astrophysics Data System (ADS)

Wang, Fang; Hou, Ling-Ying; Huang, Han

2017-06-01

It is a necessary step for Chinese character segmentation from degraded document images in Optical Character Recognizer (OCR); however, it is challenging due to various kinds of noising in such an image. In this paper, we present three local first-order statistics method that had been adaptive thresholding for segmenting text and non-text of Chinese rubbing image. Both visual inspection and numerically investigate for the segmentation results of rubbing image had been obtained. In experiments, it obtained better results than classical techniques in the binarization of real Chinese rubbing image and PHIBD 2012 datasets.

The challenging image-guided abdominal mass biopsy: established and emerging techniques 'if you can see it, you can biopsy it'.

PubMed

Sainani, Nisha I; Arellano, Ronald S; Shyn, Paul B; Gervais, Debra A; Mueller, Peter R; Silverman, Stuart G

2013-08-01

Image-guided percutaneous biopsy of abdominal masses is among the most commonly performed procedures in interventional radiology. While most abdominal masses are readily amenable to percutaneous biopsy, some may be technically challenging for a number of reasons. Low lesion conspicuity, small size, overlying or intervening structures, motion, such as that due to respiration, are some of the factors that can influence the ability and ultimately the success of an abdominal biopsy. Various techniques or technologies, such as choice of imaging modality, use of intravenous contrast and anatomic landmarks, patient positioning, organ displacement or trans-organ approach, angling CT gantry, triangulation method, real-time guidance with CT fluoroscopy or ultrasound, sedation or breath-hold, pre-procedural image fusion, electromagnetic tracking, and others, when used singularly or in combination, can overcome these challenges to facilitate needle placement in abdominal masses that otherwise would be considered not amenable to percutaneous biopsy. Familiarity and awareness of these techniques allows the interventional radiologist to expand the use of percutaneous biopsy in clinical practice, and help choose the most appropriate technique for a particular patient.

Hyperspectral fluorescence imaging with multi wavelength LED excitation

NASA Astrophysics Data System (ADS)

Luthman, A. Siri; Dumitru, Sebastian; Quirós-Gonzalez, Isabel; Bohndiek, Sarah E.

2016-04-01

Hyperspectral imaging (HSI) can combine morphological and molecular information, yielding potential for real-time and high throughput multiplexed fluorescent contrast agent imaging. Multiplexed readout from targets, such as cell surface receptors overexpressed in cancer cells, could improve both sensitivity and specificity of tumor identification. There remains, however, a need for compact and cost effective implementations of the technology. We have implemented a low-cost wide-field multiplexed fluorescence imaging system, which combines LED excitation at 590, 655 and 740 nm with a compact commercial solid state HSI system operating in the range 600 - 1000 nm. A key challenge for using reflectance-based HSI is the separation of contrast agent fluorescence from the reflectance of the excitation light. Here, we illustrate how it is possible to address this challenge in software, using two offline reflectance removal methods, prior to least-squares spectral unmixing. We made a quantitative comparison of the methods using data acquired from dilutions of contrast agents prepared in well-plates. We then established the capability of our HSI system for non-invasive in vivo fluorescence imaging in small animals using the optimal reflectance removal method. The HSI presented here enables quantitative unmixing of at least four fluorescent contrast agents (Alexa Fluor 610, 647, 700 and 750) simultaneously in living mice. A successful unmixing of the four fluorescent contrast agents was possible both using the pure contrast agents and with mixtures. The system could in principle also be applied to imaging of ex vivo tissue or intraoperative imaging in a clinical setting. These data suggest a promising approach for developing clinical applications of HSI based on multiplexed fluorescence contrast agent imaging.

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

NASA Astrophysics Data System (ADS)

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

2017-02-01

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

A new blood vessel extraction technique using edge enhancement and object classification.

PubMed

Badsha, Shahriar; Reza, Ahmed Wasif; Tan, Kim Geok; Dimyati, Kaharudin

2013-12-01

Diabetic retinopathy (DR) is increasing progressively pushing the demand of automatic extraction and classification of severity of diseases. Blood vessel extraction from the fundus image is a vital and challenging task. Therefore, this paper presents a new, computationally simple, and automatic method to extract the retinal blood vessel. The proposed method comprises several basic image processing techniques, namely edge enhancement by standard template, noise removal, thresholding, morphological operation, and object classification. The proposed method has been tested on a set of retinal images. The retinal images were collected from the DRIVE database and we have employed robust performance analysis to evaluate the accuracy. The results obtained from this study reveal that the proposed method offers an average accuracy of about 97 %, sensitivity of 99 %, specificity of 86 %, and predictive value of 98 %, which is superior to various well-known techniques.

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

Jurrus, Elizabeth R.; Hodas, Nathan O.; Baker, Nathan A.

Forensic analysis of nanoparticles is often conducted through the collection and identifi- cation of electron microscopy images to determine the origin of suspected nuclear material. Each image is carefully studied by experts for classification of materials based on texture, shape, and size. Manually inspecting large image datasets takes enormous amounts of time. However, automatic classification of large image datasets is a challenging problem due to the complexity involved in choosing image features, the lack of training data available for effective machine learning methods, and the availability of user interfaces to parse through images. Therefore, a significant need exists for automatedmore » and semi-automated methods to help analysts perform accurate image classification in large image datasets. We present INStINCt, our Intelligent Signature Canvas, as a framework for quickly organizing image data in a web based canvas framework. Images are partitioned using small sets of example images, chosen by users, and presented in an optimal layout based on features derived from convolutional neural networks.« less

Current Status of Single Particle Imaging with X-ray Lasers

DOE PAGES

Sun, Zhibin; Fan, Jiadong; Li, Haoyuan; ...

2018-01-22

The advent of ultrafast X-ray free-electron lasers (XFELs) opens the tantalizing possibility of the atomic-resolution imaging of reproducible objects such as viruses, nanoparticles, single molecules, clusters, and perhaps biological cells, achieving a resolution for single particle imaging better than a few tens of nanometers. Improving upon this is a significant challenge which has been the focus of a global single particle imaging (SPI) initiative launched in December 2014 at the Linac Coherent Light Source (LCLS), SLAC National Accelerator Laboratory, USA. A roadmap was outlined, and significant multi-disciplinary effort has since been devoted to work on the technical challenges of SPImore » such as radiation damage, beam characterization, beamline instrumentation and optics, sample preparation and delivery and algorithm development at multiple institutions involved in the SPI initiative. Currently, the SPI initiative has achieved 3D imaging of rice dwarf virus (RDV) and coliphage PR772 viruses at ~10 nm resolution by using soft X-ray FEL pulses at the Atomic Molecular and Optical (AMO) instrument of LCLS. Meanwhile, diffraction patterns with signal above noise up to the corner of the detector with a resolution of ~6 Ångström (Å) were also recorded with hard X-rays at the Coherent X-ray Imaging (CXI) instrument, also at LCLS. Achieving atomic resolution is truly a grand challenge and there is still a long way to go in light of recent developments in electron microscopy. However, the potential for studying dynamics at physiological conditions and capturing ultrafast biological, chemical and physical processes represents a tremendous potential application, attracting continued interest in pursuing further method development. In this paper, we give a brief introduction of SPI developments and look ahead to further method development.« less

Current Status of Single Particle Imaging with X-ray Lasers

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

Sun, Zhibin; Fan, Jiadong; Li, Haoyuan

The advent of ultrafast X-ray free-electron lasers (XFELs) opens the tantalizing possibility of the atomic-resolution imaging of reproducible objects such as viruses, nanoparticles, single molecules, clusters, and perhaps biological cells, achieving a resolution for single particle imaging better than a few tens of nanometers. Improving upon this is a significant challenge which has been the focus of a global single particle imaging (SPI) initiative launched in December 2014 at the Linac Coherent Light Source (LCLS), SLAC National Accelerator Laboratory, USA. A roadmap was outlined, and significant multi-disciplinary effort has since been devoted to work on the technical challenges of SPImore » such as radiation damage, beam characterization, beamline instrumentation and optics, sample preparation and delivery and algorithm development at multiple institutions involved in the SPI initiative. Currently, the SPI initiative has achieved 3D imaging of rice dwarf virus (RDV) and coliphage PR772 viruses at ~10 nm resolution by using soft X-ray FEL pulses at the Atomic Molecular and Optical (AMO) instrument of LCLS. Meanwhile, diffraction patterns with signal above noise up to the corner of the detector with a resolution of ~6 Ångström (Å) were also recorded with hard X-rays at the Coherent X-ray Imaging (CXI) instrument, also at LCLS. Achieving atomic resolution is truly a grand challenge and there is still a long way to go in light of recent developments in electron microscopy. However, the potential for studying dynamics at physiological conditions and capturing ultrafast biological, chemical and physical processes represents a tremendous potential application, attracting continued interest in pursuing further method development. In this paper, we give a brief introduction of SPI developments and look ahead to further method development.« less

MO-DE-210-03: Ultrasound imaging is an attractive method for image guided radiation treatment (IGRT), by itself or to complement other imaging modalities

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

Ding, K.

Ultrasound imaging is an attractive method for image guided radiation treatment (IGRT), by itself or to complement other imaging modalities. It is inexpensive, portable and provides good soft tissue contrast. For challenging soft tissue targets such as pancreatic cancer, ultrasound imaging can be used in combination with pre-treatment MRI and/or CT to transfer important anatomical features for target localization at time of treatment. The non-invasive and non-ionizing nature of ultrasound imaging is particularly powerful for intra-fraction localization and monitoring. Recognizing these advantages, efforts are being made to incorporate novel robotic approaches to position and manipulate the ultrasound probe during irradiation.more » These recent enabling developments hold potential to bring ultrasound imaging to a new level of IGRT applications. However, many challenges, not limited to image registration, robotic deployment, probe interference and image acquisition rate, need to be addressed to realize the full potential of IGRT with ultrasound imaging. Learning Objectives: Understand the benefits and limitations in using ultrasound to augment MRI and/or CT for motion monitoring during radiation therapy delivery. Understanding passive and active robotic approaches to implement ultrasound imaging for intra-fraction monitoring. Understand issues of probe interference with radiotherapy treatment. Understand the critical clinical workflow for effective and reproducible IGRT using ultrasound guidance. The work of X.L. is supported in part by Elekta; J.W. and K.D. is supported in part by a NIH grant R01 CA161613 and by Elekta; D.H. is support in part by a NIH grant R41 CA174089.« less

A Framework for Integrating Cultural Factors in Military Modeling and Simulation

DTIC Science & Technology

2006-01-01

how individuals and groups view their surroundings. Here, Beach’s (1990) image theory is used to elucidate the major cultural image questions relevant...10 Figure 3: Beach’s Image Theory for Cultural Knowledge Capture ............................. 13 Figure 4: Cultural Cognition of Peace Symbol...and language to define the rhythms of war, including new methods of deception. The Coalition Forces, led by the USA, are challenged with the daily

Analysis of 3-D Tongue Motion from Tagged and Cine Magnetic Resonance Images

ERIC Educational Resources Information Center

Xing, Fangxu; Woo, Jonghye; Lee, Junghoon; Murano, Emi Z.; Stone, Maureen; Prince, Jerry L.

2016-01-01

Purpose: Measuring tongue deformation and internal muscle motion during speech has been a challenging task because the tongue deforms in 3 dimensions, contains interdigitated muscles, and is largely hidden within the vocal tract. In this article, a new method is proposed to analyze tagged and cine magnetic resonance images of the tongue during…

Deep feature representation with stacked sparse auto-encoder and convolutional neural network for hyperspectral imaging-based detection of cucumber defects

USDA-ARS?s Scientific Manuscript database

It is challenging to achieve rapid and accurate processing of large amounts of hyperspectral image data. This research was aimed to develop a novel classification method by employing deep feature representation with the stacked sparse auto-encoder (SSAE) and the SSAE combined with convolutional neur...

Development and Validation of the Suprathreshold Stochastic Resonance-Based Image Processing Method for the Detection of Abdomino-pelvic Tumor on PET/CT Scans.

PubMed

Saroha, Kartik; Pandey, Anil Kumar; Sharma, Param Dev; Behera, Abhishek; Patel, Chetan; Bal, Chandrashekhar; Kumar, Rakesh

2017-01-01

The detection of abdomino-pelvic tumors embedded in or nearby radioactive urine containing 18F-FDG activity is a challenging task on PET/CT scan. In this study, we propose and validate the suprathreshold stochastic resonance-based image processing method for the detection of these tumors. The method consists of the addition of noise to the input image, and then thresholding it that creates one frame of intermediate image. One hundred such frames were generated and averaged to get the final image. The method was implemented using MATLAB R2013b on a personal computer. Noisy image was generated using random Poisson variates corresponding to each pixel of the input image. In order to verify the method, 30 sets of pre-diuretic and its corresponding post-diuretic PET/CT scan images (25 tumor images and 5 control images with no tumor) were included. For each sets of pre-diuretic image (input image), 26 images (at threshold values equal to mean counts multiplied by a constant factor ranging from 1.0 to 2.6 with increment step of 0.1) were created and visually inspected, and the image that most closely matched with the gold standard (corresponding post-diuretic image) was selected as the final output image. These images were further evaluated by two nuclear medicine physicians. In 22 out of 25 images, tumor was successfully detected. In five control images, no false positives were reported. Thus, the empirical probability of detection of abdomino-pelvic tumors evaluates to 0.88. The proposed method was able to detect abdomino-pelvic tumors on pre-diuretic PET/CT scan with a high probability of success and no false positives.

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.

Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images.

PubMed

Karim, Rashed; Bhagirath, Pranav; Claus, Piet; James Housden, R; Chen, Zhong; Karimaghaloo, Zahra; Sohn, Hyon-Mok; Lara Rodríguez, Laura; Vera, Sergio; Albà, Xènia; Hennemuth, Anja; Peitgen, Heinz-Otto; Arbel, Tal; Gonzàlez Ballester, Miguel A; Frangi, Alejandro F; Götte, Marco; Razavi, Reza; Schaeffter, Tobias; Rhode, Kawal

2016-05-01

Studies have demonstrated the feasibility of late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging for guiding the management of patients with sequelae to myocardial infarction, such as ventricular tachycardia and heart failure. Clinical implementation of these developments necessitates a reproducible and reliable segmentation of the infarcted regions. It is challenging to compare new algorithms for infarct segmentation in the left ventricle (LV) with existing algorithms. Benchmarking datasets with evaluation strategies are much needed to facilitate comparison. This manuscript presents a benchmarking evaluation framework for future algorithms that segment infarct from LGE CMR of the LV. The image database consists of 30 LGE CMR images of both humans and pigs that were acquired from two separate imaging centres. A consensus ground truth was obtained for all data using maximum likelihood estimation. Six widely-used fixed-thresholding methods and five recently developed algorithms are tested on the benchmarking framework. Results demonstrate that the algorithms have better overlap with the consensus ground truth than most of the n-SD fixed-thresholding methods, with the exception of the Full-Width-at-Half-Maximum (FWHM) fixed-thresholding method. Some of the pitfalls of fixed thresholding methods are demonstrated in this work. The benchmarking evaluation framework, which is a contribution of this work, can be used to test and benchmark future algorithms that detect and quantify infarct in LGE CMR images of the LV. The datasets, ground truth and evaluation code have been made publicly available through the website: https://www.cardiacatlas.org/web/guest/challenges. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

High quality image-pair-based deblurring method using edge mask and improved residual deconvolution

NASA Astrophysics Data System (ADS)

Cui, Guangmang; Zhao, Jufeng; Gao, Xiumin; Feng, Huajun; Chen, Yueting

2017-04-01

Image deconvolution problem is a challenging task in the field of image process. Using image pairs could be helpful to provide a better restored image compared with the deblurring method from a single blurred image. In this paper, a high quality image-pair-based deblurring method is presented using the improved RL algorithm and the gain-controlled residual deconvolution technique. The input image pair includes a non-blurred noisy image and a blurred image captured for the same scene. With the estimated blur kernel, an improved RL deblurring method based on edge mask is introduced to obtain the preliminary deblurring result with effective ringing suppression and detail preservation. Then the preliminary deblurring result is served as the basic latent image and the gain-controlled residual deconvolution is utilized to recover the residual image. A saliency weight map is computed as the gain map to further control the ringing effects around the edge areas in the residual deconvolution process. The final deblurring result is obtained by adding the preliminary deblurring result with the recovered residual image. An optical experimental vibration platform is set up to verify the applicability and performance of the proposed algorithm. Experimental results demonstrate that the proposed deblurring framework obtains a superior performance in both subjective and objective assessments and has a wide application in many image deblurring fields.

Astronomy In The Cloud: Using Mapreduce For Image Coaddition

NASA Astrophysics Data System (ADS)

Wiley, Keith; Connolly, A.; Gardner, J.; Krughoff, S.; Balazinska, M.; Howe, B.; Kwon, Y.; Bu, Y.

2011-01-01

In the coming decade, astronomical surveys of the sky will generate tens of terabytes of images and detect hundreds of millions of sources every night. The study of these sources will involve computational challenges such as anomaly detection, classification, and moving object tracking. Since such studies require the highest quality data, methods such as image coaddition, i.e., registration, stacking, and mosaicing, will be critical to scientific investigation. With a requirement that these images be analyzed on a nightly basis to identify moving sources, e.g., asteroids, or transient objects, e.g., supernovae, these datastreams present many computational challenges. Given the quantity of data involved, the computational load of these problems can only be addressed by distributing the workload over a large number of nodes. However, the high data throughput demanded by these applications may present scalability challenges for certain storage architectures. One scalable data-processing method that has emerged in recent years is MapReduce, and in this paper we focus on its popular open-source implementation called Hadoop. In the Hadoop framework, the data is partitioned among storage attached directly to worker nodes, and the processing workload is scheduled in parallel on the nodes that contain the required input data. A further motivation for using Hadoop is that it allows us to exploit cloud computing resources, i.e., platforms where Hadoop is offered as a service. We report on our experience implementing a scalable image-processing pipeline for the SDSS imaging database using Hadoop. This multi-terabyte imaging dataset provides a good testbed for algorithm development since its scope and structure approximate future surveys. First, we describe MapReduce and how we adapted image coaddition to the MapReduce framework. Then we describe a number of optimizations to our basic approach and report experimental results compring their performance. This work is funded by the NSF and by NASA.

Astronomy in the Cloud: Using MapReduce for Image Co-Addition

NASA Astrophysics Data System (ADS)

Wiley, K.; Connolly, A.; Gardner, J.; Krughoff, S.; Balazinska, M.; Howe, B.; Kwon, Y.; Bu, Y.

2011-03-01

In the coming decade, astronomical surveys of the sky will generate tens of terabytes of images and detect hundreds of millions of sources every night. The study of these sources will involve computation challenges such as anomaly detection and classification and moving-object tracking. Since such studies benefit from the highest-quality data, methods such as image co-addition, i.e., astrometric registration followed by per-pixel summation, will be a critical preprocessing step prior to scientific investigation. With a requirement that these images be analyzed on a nightly basis to identify moving sources such as potentially hazardous asteroids or transient objects such as supernovae, these data streams present many computational challenges. Given the quantity of data involved, the computational load of these problems can only be addressed by distributing the workload over a large number of nodes. However, the high data throughput demanded by these applications may present scalability challenges for certain storage architectures. One scalable data-processing method that has emerged in recent years is MapReduce, and in this article we focus on its popular open-source implementation called Hadoop. In the Hadoop framework, the data are partitioned among storage attached directly to worker nodes, and the processing workload is scheduled in parallel on the nodes that contain the required input data. A further motivation for using Hadoop is that it allows us to exploit cloud computing resources: i.e., platforms where Hadoop is offered as a service. We report on our experience of implementing a scalable image-processing pipeline for the SDSS imaging database using Hadoop. This multiterabyte imaging data set provides a good testbed for algorithm development, since its scope and structure approximate future surveys. First, we describe MapReduce and how we adapted image co-addition to the MapReduce framework. Then we describe a number of optimizations to our basic approach and report experimental results comparing their performance.

Ultrasonographic imaging of inflammatory bowel disease in pediatric patients

PubMed Central

Chiorean, Liliana; Schreiber-Dietrich, Dagmar; Braden, Barbara; Cui, Xin-Wu; Buchhorn, Reiner; Chang, Jian-Min; Dietrich, Christoph F

2015-01-01

Inflammatory bowel disease (IBD) is one of the most common chronic gastrointestinal diseases in pediatric patients. Choosing the optimal imaging modality for the assessment of gastrointestinal disease in pediatric patients can be challenging. The invasiveness and patient acceptance, the radiation exposure and the quality performance of the diagnostic test need to be considered. By reviewing the literature regarding imaging in inflammatory bowel disease the value of ultrasound in the clinical management of pediatric patients is highlighted. Transabdominal ultrasound is a useful, noninvasive method for the initial diagnosis of IBD in children; it also provides guidance for therapeutic decisions and helps to characterize and predict the course of the disease in individual patients. Ultrasound techniques including color Doppler imaging and contrast-enhanced ultrasound are promising imaging tools to determine disease activity and complications. Comparative studies between different imaging methods are needed. PMID:25954096

Skeletal camera network embedded structure-from-motion for 3D scene reconstruction from UAV images

NASA Astrophysics Data System (ADS)

Xu, Zhihua; Wu, Lixin; Gerke, Markus; Wang, Ran; Yang, Huachao

2016-11-01

Structure-from-Motion (SfM) techniques have been widely used for 3D scene reconstruction from multi-view images. However, due to the large computational costs of SfM methods there is a major challenge in processing highly overlapping images, e.g. images from unmanned aerial vehicles (UAV). This paper embeds a novel skeletal camera network (SCN) into SfM to enable efficient 3D scene reconstruction from a large set of UAV images. First, the flight control data are used within a weighted graph to construct a topologically connected camera network (TCN) to determine the spatial connections between UAV images. Second, the TCN is refined using a novel hierarchical degree bounded maximum spanning tree to generate a SCN, which contains a subset of edges from the TCN and ensures that each image is involved in at least a 3-view configuration. Third, the SCN is embedded into the SfM to produce a novel SCN-SfM method, which allows performing tie-point matching only for the actually connected image pairs. The proposed method was applied in three experiments with images from two fixed-wing UAVs and an octocopter UAV, respectively. In addition, the SCN-SfM method was compared to three other methods for image connectivity determination. The comparison shows a significant reduction in the number of matched images if our method is used, which leads to less computational costs. At the same time the achieved scene completeness and geometric accuracy are comparable.

Connectome imaging for mapping human brain pathways

PubMed Central

Shi, Y; Toga, A W

2017-01-01

With the fast advance of connectome imaging techniques, we have the opportunity of mapping the human brain pathways in vivo at unprecedented resolution. In this article we review the current developments of diffusion magnetic resonance imaging (MRI) for the reconstruction of anatomical pathways in connectome studies. We first introduce the background of diffusion MRI with an emphasis on the technical advances and challenges in state-of-the-art multi-shell acquisition schemes used in the Human Connectome Project. Characterization of the microstructural environment in the human brain is discussed from the tensor model to the general fiber orientation distribution (FOD) models that can resolve crossing fibers in each voxel of the image. Using FOD-based tractography, we describe novel methods for fiber bundle reconstruction and graph-based connectivity analysis. Building upon these novel developments, there have already been successful applications of connectome imaging techniques in reconstructing challenging brain pathways. Examples including retinofugal and brainstem pathways will be reviewed. Finally, we discuss future directions in connectome imaging and its interaction with other aspects of brain imaging research. PMID:28461700

Ice Growth Measurements from Image Data to Support Ice Crystal and Mixed-Phase Accretion Testing

NASA Technical Reports Server (NTRS)

Struk, Peter M.; Lynch, Christopher J.

2012-01-01

This paper describes the imaging techniques as well as the analysis methods used to measure the ice thickness and growth rate in support of ice-crystal icing tests performed at the National Research Council of Canada (NRC) Research Altitude Test Facility (RATFac). A detailed description of the camera setup, which involves both still and video cameras, as well as the analysis methods using the NASA Spotlight software, are presented. Two cases, one from two different test entries, showing significant ice growth are analyzed in detail describing the ice thickness and growth rate which is generally linear. Estimates of the bias uncertainty are presented for all measurements. Finally some of the challenges related to the imaging and analysis methods are discussed as well as methods used to overcome them.

Ice Growth Measurements from Image Data to Support Ice-Crystal and Mixed-Phase Accretion Testing

NASA Technical Reports Server (NTRS)

Struk, Peter, M; Lynch, Christopher, J.

2012-01-01

This paper describes the imaging techniques as well as the analysis methods used to measure the ice thickness and growth rate in support of ice-crystal icing tests performed at the National Research Council of Canada (NRC) Research Altitude Test Facility (RATFac). A detailed description of the camera setup, which involves both still and video cameras, as well as the analysis methods using the NASA Spotlight software, are presented. Two cases, one from two different test entries, showing significant ice growth are analyzed in detail describing the ice thickness and growth rate which is generally linear. Estimates of the bias uncertainty are presented for all measurements. Finally some of the challenges related to the imaging and analysis methods are discussed as well as methods used to overcome them.

High-energy proton imaging for biomedical applications

DOE PAGES

Prall, Matthias; Durante, Marco; Berger, Thomas; ...

2016-06-10

The charged particle community is looking for techniques exploiting proton interactions instead of X-ray absorption for creating images of human tissue. Due to multiple Coulomb scattering inside the measured object it has shown to be highly non-trivial to achieve sufficient spatial resolution. We present imaging of biological tissue with a proton microscope. This device relies on magnetic optics, distinguishing it from most published proton imaging methods. For these methods reducing the data acquisition time to a clinically acceptable level has turned out to be challenging. In a proton microscope, data acquisition and processing are much simpler. This device even allowsmore » imaging in real time. The primary medical application will be image guidance in proton radiosurgery. Proton images demonstrating the potential for this application are presented. As a result, tomographic reconstructions are included to raise awareness of the possibility of high-resolution proton tomography using magneto-optics.« less

High-energy proton imaging for biomedical applications

NASA Astrophysics Data System (ADS)

Prall, M.; Durante, M.; Berger, T.; Przybyla, B.; Graeff, C.; Lang, P. M.; Latessa, C.; Shestov, L.; Simoniello, P.; Danly, C.; Mariam, F.; Merrill, F.; Nedrow, P.; Wilde, C.; Varentsov, D.

2016-06-01

The charged particle community is looking for techniques exploiting proton interactions instead of X-ray absorption for creating images of human tissue. Due to multiple Coulomb scattering inside the measured object it has shown to be highly non-trivial to achieve sufficient spatial resolution. We present imaging of biological tissue with a proton microscope. This device relies on magnetic optics, distinguishing it from most published proton imaging methods. For these methods reducing the data acquisition time to a clinically acceptable level has turned out to be challenging. In a proton microscope, data acquisition and processing are much simpler. This device even allows imaging in real time. The primary medical application will be image guidance in proton radiosurgery. Proton images demonstrating the potential for this application are presented. Tomographic reconstructions are included to raise awareness of the possibility of high-resolution proton tomography using magneto-optics.

Geometric Metamorphosis

PubMed Central

Niethammer, Marc; Hart, Gabriel L.; Pace, Danielle F.; Vespa, Paul M.; Irimia, Andrei; Van Horn, John D.; Aylward, Stephen R.

2013-01-01

Standard image registration methods do not account for changes in image appearance. Hence, metamorphosis approaches have been developed which jointly estimate a space deformation and a change in image appearance to construct a spatio-temporal trajectory smoothly transforming a source to a target image. For standard metamorphosis, geometric changes are not explicitly modeled. We propose a geometric metamorphosis formulation, which explains changes in image appearance by a global deformation, a deformation of a geometric model, and an image composition model. This work is motivated by the clinical challenge of predicting the long-term effects of traumatic brain injuries based on time-series images. This work is also applicable to the quantification of tumor progression (e.g., estimating its infiltrating and displacing components) and predicting chronic blood perfusion changes after stroke. We demonstrate the utility of the method using simulated data as well as scans from a clinical traumatic brain injury patient. PMID:21995083

Composite SAR imaging using sequential joint sparsity

NASA Astrophysics Data System (ADS)

Sanders, Toby; Gelb, Anne; Platte, Rodrigo B.

2017-06-01

This paper investigates accurate and efficient ℓ1 regularization methods for generating synthetic aperture radar (SAR) images. Although ℓ1 regularization algorithms are already employed in SAR imaging, practical and efficient implementation in terms of real time imaging remain a challenge. Here we demonstrate that fast numerical operators can be used to robustly implement ℓ1 regularization methods that are as or more efficient than traditional approaches such as back projection, while providing superior image quality. In particular, we develop a sequential joint sparsity model for composite SAR imaging which naturally combines the joint sparsity methodology with composite SAR. Our technique, which can be implemented using standard, fractional, or higher order total variation regularization, is able to reduce the effects of speckle and other noisy artifacts with little additional computational cost. Finally we show that generalizing total variation regularization to non-integer and higher orders provides improved flexibility and robustness for SAR imaging.

High-energy proton imaging for biomedical applications

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

Prall, Matthias; Durante, Marco; Berger, Thomas

The charged particle community is looking for techniques exploiting proton interactions instead of X-ray absorption for creating images of human tissue. Due to multiple Coulomb scattering inside the measured object it has shown to be highly non-trivial to achieve sufficient spatial resolution. We present imaging of biological tissue with a proton microscope. This device relies on magnetic optics, distinguishing it from most published proton imaging methods. For these methods reducing the data acquisition time to a clinically acceptable level has turned out to be challenging. In a proton microscope, data acquisition and processing are much simpler. This device even allowsmore » imaging in real time. The primary medical application will be image guidance in proton radiosurgery. Proton images demonstrating the potential for this application are presented. As a result, tomographic reconstructions are included to raise awareness of the possibility of high-resolution proton tomography using magneto-optics.« less

Iris recognition: on the segmentation of degraded images acquired in the visible wavelength.

PubMed

Proença, Hugo

2010-08-01

Iris recognition imaging constraints are receiving increasing attention. There are several proposals to develop systems that operate in the visible wavelength and in less constrained environments. These imaging conditions engender acquired noisy artifacts that lead to severely degraded images, making iris segmentation a major issue. Having observed that existing iris segmentation methods tend to fail in these challenging conditions, we present a segmentation method that can handle degraded images acquired in less constrained conditions. We offer the following contributions: 1) to consider the sclera the most easily distinguishable part of the eye in degraded images, 2) to propose a new type of feature that measures the proportion of sclera in each direction and is fundamental in segmenting the iris, and 3) to run the entire procedure in deterministically linear time in respect to the size of the image, making the procedure suitable for real-time applications.

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

NASA Astrophysics Data System (ADS)

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

2016-03-01

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

Structural Image Analysis of the Brain in Neuropsychology Using Magnetic Resonance Imaging (MRI) Techniques.

PubMed

Bigler, Erin D

2015-09-01

Magnetic resonance imaging (MRI) of the brain provides exceptional image quality for visualization and neuroanatomical classification of brain structure. A variety of image analysis techniques provide both qualitative as well as quantitative methods to relate brain structure with neuropsychological outcome and are reviewed herein. Of particular importance are more automated methods that permit analysis of a broad spectrum of anatomical measures including volume, thickness and shape. The challenge for neuropsychology is which metric to use, for which disorder and the timing of when image analysis methods are applied to assess brain structure and pathology. A basic overview is provided as to the anatomical and pathoanatomical relations of different MRI sequences in assessing normal and abnormal findings. Some interpretive guidelines are offered including factors related to similarity and symmetry of typical brain development along with size-normalcy features of brain anatomy related to function. The review concludes with a detailed example of various quantitative techniques applied to analyzing brain structure for neuropsychological outcome studies in traumatic brain injury.

Software phantom with realistic speckle modeling for validation of image analysis methods in echocardiography

NASA Astrophysics Data System (ADS)

Law, Yuen C.; Tenbrinck, Daniel; Jiang, Xiaoyi; Kuhlen, Torsten

2014-03-01

Computer-assisted processing and interpretation of medical ultrasound images is one of the most challenging tasks within image analysis. Physical phenomena in ultrasonographic images, e.g., the characteristic speckle noise and shadowing effects, make the majority of standard methods from image analysis non optimal. Furthermore, validation of adapted computer vision methods proves to be difficult due to missing ground truth information. There is no widely accepted software phantom in the community and existing software phantoms are not exible enough to support the use of specific speckle models for different tissue types, e.g., muscle and fat tissue. In this work we propose an anatomical software phantom with a realistic speckle pattern simulation to _ll this gap and provide a exible tool for validation purposes in medical ultrasound image analysis. We discuss the generation of speckle patterns and perform statistical analysis of the simulated textures to obtain quantitative measures of the realism and accuracy regarding the resulting textures.

Novel Descattering Approach for Stereo Vision in Dense Suspended Scatterer Environments

PubMed Central

Nguyen, Chanh D. Tr.; Park, Jihyuk; Cho, Kyeong-Yong; Kim, Kyung-Soo; Kim, Soohyun

2017-01-01

In this paper, we propose a model-based scattering removal method for stereo vision for robot manipulation in indoor scattering media where the commonly used ranging sensors are unable to work. Stereo vision is an inherently ill-posed and challenging problem. It is even more difficult in the case of images of dense fog or dense steam scenes illuminated by active light sources. Images taken in such environments suffer attenuation of object radiance and scattering of the active light sources. To solve this problem, we first derive the imaging model for images taken in a dense scattering medium with a single active illumination close to the cameras. Based on this physical model, the non-uniform backscattering signal is efficiently removed. The descattered images are then utilized as the input images of stereo vision. The performance of the method is evaluated based on the quality of the depth map from stereo vision. We also demonstrate the effectiveness of the proposed method by carrying out the real robot manipulation task. PMID:28629139

LUNGx Challenge for computerized lung nodule classification

DOE PAGES

Armato, Samuel G.; Drukker, Karen; Li, Feng; ...

2016-12-19

The purpose of this work is to describe the LUNGx Challenge for the computerized classification of lung nodules on diagnostic computed tomography (CT) scans as benign or malignant and report the performance of participants’ computerized methods along with that of six radiologists who participated in an observer study performing the same Challenge task on the same dataset. The Challenge provided sets of calibration and testing scans, established a performance assessment process, and created an infrastructure for case dissemination and result submission. We present ten groups that applied their own methods to 73 lung nodules (37 benign and 36 malignant) thatmore » were selected to achieve approximate size matching between the two cohorts. Area under the receiver operating characteristic curve (AUC) values for these methods ranged from 0.50 to 0.68; only three methods performed statistically better than random guessing. The radiologists’ AUC values ranged from 0.70 to 0.85; three radiologists performed statistically better than the best-performing computer method. The LUNGx Challenge compared the performance of computerized methods in the task of differentiating benign from malignant lung nodules on CT scans, placed in the context of the performance of radiologists on the same task. Lastly, the continued public availability of the Challenge cases will provide a valuable resource for the medical imaging research community.« less

LUNGx Challenge for computerized lung nodule classification

PubMed Central

Armato, Samuel G.; Drukker, Karen; Li, Feng; Hadjiiski, Lubomir; Tourassi, Georgia D.; Engelmann, Roger M.; Giger, Maryellen L.; Redmond, George; Farahani, Keyvan; Kirby, Justin S.; Clarke, Laurence P.

2016-01-01

Abstract. The purpose of this work is to describe the LUNGx Challenge for the computerized classification of lung nodules on diagnostic computed tomography (CT) scans as benign or malignant and report the performance of participants’ computerized methods along with that of six radiologists who participated in an observer study performing the same Challenge task on the same dataset. The Challenge provided sets of calibration and testing scans, established a performance assessment process, and created an infrastructure for case dissemination and result submission. Ten groups applied their own methods to 73 lung nodules (37 benign and 36 malignant) that were selected to achieve approximate size matching between the two cohorts. Area under the receiver operating characteristic curve (AUC) values for these methods ranged from 0.50 to 0.68; only three methods performed statistically better than random guessing. The radiologists’ AUC values ranged from 0.70 to 0.85; three radiologists performed statistically better than the best-performing computer method. The LUNGx Challenge compared the performance of computerized methods in the task of differentiating benign from malignant lung nodules on CT scans, placed in the context of the performance of radiologists on the same task. The continued public availability of the Challenge cases will provide a valuable resource for the medical imaging research community. PMID:28018939

LUNGx Challenge for computerized lung nodule classification

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

Armato, Samuel G.; Drukker, Karen; Li, Feng

The purpose of this work is to describe the LUNGx Challenge for the computerized classification of lung nodules on diagnostic computed tomography (CT) scans as benign or malignant and report the performance of participants’ computerized methods along with that of six radiologists who participated in an observer study performing the same Challenge task on the same dataset. The Challenge provided sets of calibration and testing scans, established a performance assessment process, and created an infrastructure for case dissemination and result submission. We present ten groups that applied their own methods to 73 lung nodules (37 benign and 36 malignant) thatmore » were selected to achieve approximate size matching between the two cohorts. Area under the receiver operating characteristic curve (AUC) values for these methods ranged from 0.50 to 0.68; only three methods performed statistically better than random guessing. The radiologists’ AUC values ranged from 0.70 to 0.85; three radiologists performed statistically better than the best-performing computer method. The LUNGx Challenge compared the performance of computerized methods in the task of differentiating benign from malignant lung nodules on CT scans, placed in the context of the performance of radiologists on the same task. Lastly, the continued public availability of the Challenge cases will provide a valuable resource for the medical imaging research community.« less

Medical image computing for computer-supported diagnostics and therapy. Advances and perspectives.

PubMed

Handels, H; Ehrhardt, J

2009-01-01

Medical image computing has become one of the most challenging fields in medical informatics. In image-based diagnostics of the future software assistance will become more and more important, and image analysis systems integrating advanced image computing methods are needed to extract quantitative image parameters to characterize the state and changes of image structures of interest (e.g. tumors, organs, vessels, bones etc.) in a reproducible and objective way. Furthermore, in the field of software-assisted and navigated surgery medical image computing methods play a key role and have opened up new perspectives for patient treatment. However, further developments are needed to increase the grade of automation, accuracy, reproducibility and robustness. Moreover, the systems developed have to be integrated into the clinical workflow. For the development of advanced image computing systems methods of different scientific fields have to be adapted and used in combination. The principal methodologies in medical image computing are the following: image segmentation, image registration, image analysis for quantification and computer assisted image interpretation, modeling and simulation as well as visualization and virtual reality. Especially, model-based image computing techniques open up new perspectives for prediction of organ changes and risk analysis of patients and will gain importance in diagnostic and therapy of the future. From a methodical point of view the authors identify the following future trends and perspectives in medical image computing: development of optimized application-specific systems and integration into the clinical workflow, enhanced computational models for image analysis and virtual reality training systems, integration of different image computing methods, further integration of multimodal image data and biosignals and advanced methods for 4D medical image computing. The development of image analysis systems for diagnostic support or operation planning is a complex interdisciplinary process. Image computing methods enable new insights into the patient's image data and have the future potential to improve medical diagnostics and patient treatment.

Motion detection and compensation in infrared retinal image sequences.

PubMed

Scharcanski, J; Schardosim, L R; Santos, D; Stuchi, A

2013-01-01

Infrared image data captured by non-mydriatic digital retinography systems often are used in the diagnosis and treatment of the diabetic macular edema (DME). Infrared illumination is less aggressive to the patient retina, and retinal studies can be carried out without pupil dilation. However, sequences of infrared eye fundus images of static scenes, tend to present pixel intensity fluctuations in time, and noisy and background illumination changes pose a challenge to most motion detection methods proposed in the literature. In this paper, we present a retinal motion detection method that is adaptive to background noise and illumination changes. Our experimental results indicate that this method is suitable for detecting retinal motion in infrared image sequences, and compensate the detected motion, which is relevant in retinal laser treatment systems for DME. Copyright © 2013 Elsevier Ltd. All rights reserved.

Study on Building Extraction from High-Resolution Images Using Mbi

NASA Astrophysics Data System (ADS)

Ding, Z.; Wang, X. Q.; Li, Y. L.; Zhang, S. S.

2018-04-01

Building extraction from high resolution remote sensing images is a hot research topic in the field of photogrammetry and remote sensing. However, the diversity and complexity of buildings make building extraction methods still face challenges in terms of accuracy, efficiency, and so on. In this study, a new building extraction framework based on MBI and combined with image segmentation techniques, spectral constraint, shadow constraint, and shape constraint is proposed. In order to verify the proposed method, worldview-2, GF-2, GF-1 remote sensing images covered Xiamen Software Park were used for building extraction experiments. Experimental results indicate that the proposed method improve the original MBI significantly, and the correct rate is over 86 %. Furthermore, the proposed framework reduces the false alarms by 42 % on average compared to the performance of the original MBI.

Cell nuclei segmentation in fluorescence microscopy images using inter- and intra-region discriminative information.

PubMed

Song, Yang; Cai, Weidong; Feng, David Dagan; Chen, Mei

2013-01-01

Automated segmentation of cell nuclei in microscopic images is critical to high throughput analysis of the ever increasing amount of data. Although cell nuclei are generally visually distinguishable for human, automated segmentation faces challenges when there is significant intensity inhomogeneity among cell nuclei or in the background. In this paper, we propose an effective method for automated cell nucleus segmentation using a three-step approach. It first obtains an initial segmentation by extracting salient regions in the image, then reduces false positives using inter-region feature discrimination, and finally refines the boundary of the cell nuclei using intra-region contrast information. This method has been evaluated on two publicly available datasets of fluorescence microscopic images with 4009 cells, and has achieved superior performance compared to popular state of the art methods using established metrics.

Red Lesion Detection Using Dynamic Shape Features for Diabetic Retinopathy Screening.

PubMed

Seoud, Lama; Hurtut, Thomas; Chelbi, Jihed; Cheriet, Farida; Langlois, J M Pierre

2016-04-01

The development of an automatic telemedicine system for computer-aided screening and grading of diabetic retinopathy depends on reliable detection of retinal lesions in fundus images. In this paper, a novel method for automatic detection of both microaneurysms and hemorrhages in color fundus images is described and validated. The main contribution is a new set of shape features, called Dynamic Shape Features, that do not require precise segmentation of the regions to be classified. These features represent the evolution of the shape during image flooding and allow to discriminate between lesions and vessel segments. The method is validated per-lesion and per-image using six databases, four of which are publicly available. It proves to be robust with respect to variability in image resolution, quality and acquisition system. On the Retinopathy Online Challenge's database, the method achieves a FROC score of 0.420 which ranks it fourth. On the Messidor database, when detecting images with diabetic retinopathy, the proposed method achieves an area under the ROC curve of 0.899, comparable to the score of human experts, and it outperforms state-of-the-art approaches.

Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures.

PubMed

Larue, Ruben T H M; Defraene, Gilles; De Ruysscher, Dirk; Lambin, Philippe; van Elmpt, Wouter

2017-02-01

Quantitative analysis of tumour characteristics based on medical imaging is an emerging field of research. In recent years, quantitative imaging features derived from CT, positron emission tomography and MR scans were shown to be of added value in the prediction of outcome parameters in oncology, in what is called the radiomics field. However, results might be difficult to compare owing to a lack of standardized methodologies to conduct quantitative image analyses. In this review, we aim to present an overview of the current challenges, technical routines and protocols that are involved in quantitative imaging studies. The first issue that should be overcome is the dependency of several features on the scan acquisition and image reconstruction parameters. Adopting consistent methods in the subsequent target segmentation step is evenly crucial. To further establish robust quantitative image analyses, standardization or at least calibration of imaging features based on different feature extraction settings is required, especially for texture- and filter-based features. Several open-source and commercial software packages to perform feature extraction are currently available, all with slightly different functionalities, which makes benchmarking quite challenging. The number of imaging features calculated is typically larger than the number of patients studied, which emphasizes the importance of proper feature selection and prediction model-building routines to prevent overfitting. Even though many of these challenges still need to be addressed before quantitative imaging can be brought into daily clinical practice, radiomics is expected to be a critical component for the integration of image-derived information to personalize treatment in the future.

Sparse models for correlative and integrative analysis of imaging and genetic data

PubMed Central

Lin, Dongdong; Cao, Hongbao; Calhoun, Vince D.

2014-01-01

The development of advanced medical imaging technologies and high-throughput genomic measurements has enhanced our ability to understand their interplay as well as their relationship with human behavior by integrating these two types of datasets. However, the high dimensionality and heterogeneity of these datasets presents a challenge to conventional statistical methods; there is a high demand for the development of both correlative and integrative analysis approaches. Here, we review our recent work on developing sparse representation based approaches to address this challenge. We show how sparse models are applied to the correlation and integration of imaging and genetic data for biomarker identification. We present examples on how these approaches are used for the detection of risk genes and classification of complex diseases such as schizophrenia. Finally, we discuss future directions on the integration of multiple imaging and genomic datasets including their interactions such as epistasis. PMID:25218561

Imaging challenges in biomaterials and tissue engineering

PubMed Central

Appel, Alyssa A.; Anastasio, Mark A.; Larson, Jeffery C.; Brey, Eric M.

2013-01-01

Biomaterials are employed in the fields of tissue engineering and regenerative medicine (TERM) in order to enhance the regeneration or replacement of tissue function and/or structure. The unique environments resulting from the presence of biomaterials, cells, and tissues result in distinct challenges in regards to monitoring and assessing the results of these interventions. Imaging technologies for three-dimensional (3D) analysis have been identified as a strategic priority in TERM research. Traditionally, histological and immunohistochemical techniques have been used to evaluate engineered tissues. However, these methods do not allow for an accurate volume assessment, are invasive, and do not provide information on functional status. Imaging techniques are needed that enable non-destructive, longitudinal, quantitative, and three-dimensional analysis of TERM strategies. This review focuses on evaluating the application of available imaging modalities for assessment of biomaterials and tissue in TERM applications. Included is a discussion of limitations of these techniques and identification of areas for further development. PMID:23768903

Design of a rear anamorphic attachment for digital cinematography

NASA Astrophysics Data System (ADS)

Cifuentes, A.; Valles, A.

2008-09-01

Digital taking systems for HDTV and now for the film industry present a particularly challenging design problem for rear adapters in general. The thick 3-channel prism block in the camera provides an important challenge in the design. In this paper the design of a 1.33x rear anamorphic attachment is presented. The new design departs significantly from the traditional Bravais condition due to the thick dichroic prism block. Design strategies for non-rotationally symmetric systems and fields of view are discussed. Anamorphic images intrinsically have a lower contrast and less resolution than their rotationally symmetric counterparts, therefore proper image evaluation must be considered. The interpretation of the traditional image quality methods applied to anamorphic images is also discussed in relation to the design process. The final design has a total track less than 50 mm, maintaining the telecentricity of the digital prime lens and taking full advantage of the f/1.4 prism block.

Quantitative imaging of heterogeneous dynamics in drying and aging paints

PubMed Central

van der Kooij, Hanne M.; Fokkink, Remco; van der Gucht, Jasper; Sprakel, Joris

2016-01-01

Drying and aging paint dispersions display a wealth of complex phenomena that make their study fascinating yet challenging. To meet the growing demand for sustainable, high-quality paints, it is essential to unravel the microscopic mechanisms underlying these phenomena. Visualising the governing dynamics is, however, intrinsically difficult because the dynamics are typically heterogeneous and span a wide range of time scales. Moreover, the high turbidity of paints precludes conventional imaging techniques from reaching deep inside the paint. To address these challenges, we apply a scattering technique, Laser Speckle Imaging, as a versatile and quantitative tool to elucidate the internal dynamics, with microscopic resolution and spanning seven decades of time. We present a toolbox of data analysis and image processing methods that allows a tailored investigation of virtually any turbid dispersion, regardless of the geometry and substrate. Using these tools we watch a variety of paints dry and age with unprecedented detail. PMID:27682840

WE-EF-BRD-02: Battling Maxwell’s Equations: Physics Challenges and Solutions for Hybrid MRI Systems

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

Keall, P.

MRI-guided treatment is a growing area of medicine, particularly in radiotherapy and surgery. The exquisite soft tissue anatomic contrast offered by MRI, along with functional imaging, makes the use of MRI during therapeutic procedures very attractive. Challenging the utility of MRI in the therapy room are many issues including the physics of MRI and the impact on the environment and therapeutic instruments, the impact of the room and instruments on the MRI; safety, space, design and cost. In this session, the applications and challenges of MRI-guided treatment will be described. The session format is: Past, present and future: MRI-guided radiotherapymore » from 2005 to 2025: Jan Lagendijk Battling Maxwell’s equations: Physics challenges and solutions for hybrid MRI systems: Paul Keall I want it now!: Advances in MRI acquisition, reconstruction and the use of priors to enable fast anatomic and physiologic imaging to inform guidance and adaptation decisions: Yanle Hu MR in the OR: The growth and applications of MRI for interventional radiology and surgery: Rebecca Fahrig Learning Objectives: To understand the history and trajectory of MRI-guided radiotherapy To understand the challenges of integrating MR imaging systems with linear accelerators To understand the latest in fast MRI methods to enable the visualisation of anatomy and physiology on radiotherapy treatment timescales To understand the growing role and challenges of MRI for image-guided surgical procedures My disclosures are publicly available and updated at: http://sydney.edu.au/medicine/radiation-physics/about-us/disclosures.php.« less

Effective Alternating Direction Optimization Methods for Sparsity-Constrained Blind Image Deblurring.

PubMed

Xiong, Naixue; Liu, Ryan Wen; Liang, Maohan; Wu, Di; Liu, Zhao; Wu, Huisi

2017-01-18

Single-image blind deblurring for imaging sensors in the Internet of Things (IoT) is a challenging ill-conditioned inverse problem, which requires regularization techniques to stabilize the image restoration process. The purpose is to recover the underlying blur kernel and latent sharp image from only one blurred image. Under many degraded imaging conditions, the blur kernel could be considered not only spatially sparse, but also piecewise smooth with the support of a continuous curve. By taking advantage of the hybrid sparse properties of the blur kernel, a hybrid regularization method is proposed in this paper to robustly and accurately estimate the blur kernel. The effectiveness of the proposed blur kernel estimation method is enhanced by incorporating both the L 1 -norm of kernel intensity and the squared L 2 -norm of the intensity derivative. Once the accurate estimation of the blur kernel is obtained, the original blind deblurring can be simplified to the direct deconvolution of blurred images. To guarantee robust non-blind deconvolution, a variational image restoration model is presented based on the L 1 -norm data-fidelity term and the total generalized variation (TGV) regularizer of second-order. All non-smooth optimization problems related to blur kernel estimation and non-blind deconvolution are effectively handled by using the alternating direction method of multipliers (ADMM)-based numerical methods. Comprehensive experiments on both synthetic and realistic datasets have been implemented to compare the proposed method with several state-of-the-art methods. The experimental comparisons have illustrated the satisfactory imaging performance of the proposed method in terms of quantitative and qualitative evaluations.

Infrared Ship Target Segmentation Based on Spatial Information Improved FCM.

PubMed

Bai, Xiangzhi; Chen, Zhiguo; Zhang, Yu; Liu, Zhaoying; Lu, Yi

2016-12-01

Segmentation of infrared (IR) ship images is always a challenging task, because of the intensity inhomogeneity and noise. The fuzzy C-means (FCM) clustering is a classical method widely used in image segmentation. However, it has some shortcomings, like not considering the spatial information or being sensitive to noise. In this paper, an improved FCM method based on the spatial information is proposed for IR ship target segmentation. The improvements include two parts: 1) adding the nonlocal spatial information based on the ship target and 2) using the spatial shape information of the contour of the ship target to refine the local spatial constraint by Markov random field. In addition, the results of K -means are used to initialize the improved FCM method. Experimental results show that the improved method is effective and performs better than the existing methods, including the existing FCM methods, for segmentation of the IR ship images.

Classification of Hyperspectral Data Based on Guided Filtering and Random Forest

NASA Astrophysics Data System (ADS)

Ma, H.; Feng, W.; Cao, X.; Wang, L.

2017-09-01

Hyperspectral images usually consist of more than one hundred spectral bands, which have potentials to provide rich spatial and spectral information. However, the application of hyperspectral data is still challengeable due to "the curse of dimensionality". In this context, many techniques, which aim to make full use of both the spatial and spectral information, are investigated. In order to preserve the geometrical information, meanwhile, with less spectral bands, we propose a novel method, which combines principal components analysis (PCA), guided image filtering and the random forest classifier (RF). In detail, PCA is firstly employed to reduce the dimension of spectral bands. Secondly, the guided image filtering technique is introduced to smooth land object, meanwhile preserving the edge of objects. Finally, the features are fed into RF classifier. To illustrate the effectiveness of the method, we carry out experiments over the popular Indian Pines data set, which is collected by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. By comparing the proposed method with the method of only using PCA or guided image filter, we find that effect of the proposed method is better.

Fetal MRI: A Technical Update with Educational Aspirations

PubMed Central

Gholipour, Ali; Estroff, Judith A.; Barnewolt, Carol E.; Robertson, Richard L.; Grant, P. Ellen; Gagoski, Borjan; Warfield, Simon K.; Afacan, Onur; Connolly, Susan A.; Neil, Jeffrey J.; Wolfberg, Adam; Mulkern, Robert V.

2015-01-01

Fetal magnetic resonance imaging (MRI) examinations have become well-established procedures at many institutions and can serve as useful adjuncts to ultrasound (US) exams when diagnostic doubts remain after US. Due to fetal motion, however, fetal MRI exams are challenging and require the MR scanner to be used in a somewhat different mode than that employed for more routine clinical studies. Herein we review the techniques most commonly used, and those that are available, for fetal MRI with an emphasis on the physics of the techniques and how to deploy them to improve success rates for fetal MRI exams. By far the most common technique employed is single-shot T2-weighted imaging due to its excellent tissue contrast and relative immunity to fetal motion. Despite the significant challenges involved, however, many of the other techniques commonly employed in conventional neuro- and body MRI such as T1 and T2*-weighted imaging, diffusion and perfusion weighted imaging, as well as spectroscopic methods remain of interest for fetal MR applications. An effort to understand the strengths and limitations of these basic methods within the context of fetal MRI is made in order to optimize their use and facilitate implementation of technical improvements for the further development of fetal MR imaging, both in acquisition and post-processing strategies. PMID:26225129

Simulation of Earth textures by conditional image quilting

NASA Astrophysics Data System (ADS)

Mahmud, K.; Mariethoz, G.; Caers, J.; Tahmasebi, P.; Baker, A.

2014-04-01

Training image-based approaches for stochastic simulations have recently gained attention in surface and subsurface hydrology. This family of methods allows the creation of multiple realizations of a study domain, with a spatial continuity based on a training image (TI) that contains the variability, connectivity, and structural properties deemed realistic. A major drawback of these methods is their computational and/or memory cost, making certain applications challenging. It was found that similar methods, also based on training images or exemplars, have been proposed in computer graphics. One such method, image quilting (IQ), is introduced in this paper and adapted for hydrogeological applications. The main difficulty is that Image Quilting was originally not designed to produce conditional simulations and was restricted to 2-D images. In this paper, the original method developed in computer graphics has been modified to accommodate conditioning data and 3-D problems. This new conditional image quilting method (CIQ) is patch based, does not require constructing a pattern databases, and can be used with both categorical and continuous training images. The main concept is to optimally cut the patches such that they overlap with minimum discontinuity. The optimal cut is determined using a dynamic programming algorithm. Conditioning is accomplished by prior selection of patches that are compatible with the conditioning data. The performance of CIQ is tested for a variety of hydrogeological test cases. The results, when compared with previous multiple-point statistics (MPS) methods, indicate an improvement in CPU time by a factor of at least 50.

Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge

PubMed Central

Litjens, Geert; Toth, Robert; van de Ven, Wendy; Hoeks, Caroline; Kerkstra, Sjoerd; van Ginneken, Bram; Vincent, Graham; Guillard, Gwenael; Birbeck, Neil; Zhang, Jindang; Strand, Robin; Malmberg, Filip; Ou, Yangming; Davatzikos, Christos; Kirschner, Matthias; Jung, Florian; Yuan, Jing; Qiu, Wu; Gao, Qinquan; Edwards, Philip “Eddie”; Maan, Bianca; van der Heijden, Ferdinand; Ghose, Soumya; Mitra, Jhimli; Dowling, Jason; Barratt, Dean; Huisman, Henkjan; Madabhushi, Anant

2014-01-01

Prostate MRI image segmentation has been an area of intense research due to the increased use of MRI as a modality for the clinical workup of prostate cancer. Segmentation is useful for various tasks, e.g. to accurately localize prostate boundaries for radiotherapy or to initialize multi-modal registration algorithms. In the past, it has been difficult for research groups to evaluate prostate segmentation algorithms on multi-center, multi-vendor and multi-protocol data. Especially because we are dealing with MR images, image appearance, resolution and the presence of artifacts are affected by differences in scanners and/or protocols, which in turn can have a large influence on algorithm accuracy. The Prostate MR Image Segmentation (PROMISE12) challenge was setup to allow a fair and meaningful comparison of segmentation methods on the basis of performance and robustness. In this work we will discuss the initial results of the online PROMISE12 challenge, and the results obtained in the live challenge workshop hosted by the MICCAI2012 conference. In the challenge, 100 prostate MR cases from 4 different centers were included, with differences in scanner manufacturer, field strength and protocol. A total of 11 teams from academic research groups and industry participated. Algorithms showed a wide variety in methods and implementation, including active appearance models, atlas registration and level sets. Evaluation was performed using boundary and volume based metrics which were combined into a single score relating the metrics to human expert performance. The winners of the challenge where the algorithms by teams Imorphics and ScrAutoProstate, with scores of 85.72 and 84.29 overall. Both algorithms where significantly better than all other algorithms in the challenge (p < 0.05) and had an efficient implementation with a run time of 8 minutes and 3 second per case respectively. Overall, active appearance model based approaches seemed to outperform other approaches like multi-atlas registration, both on accuracy and computation time. Although average algorithm performance was good to excellent and the Imorphics algorithm outperformed the second observer on average, we showed that algorithm combination might lead to further improvement, indicating that optimal performance for prostate segmentation is not yet obtained. All results are available online at http://promise12.grand-challenge.org/. PMID:24418598

Super-resolution Microscopy in Plant Cell Imaging.

PubMed

Komis, George; Šamajová, Olga; Ovečka, Miroslav; Šamaj, Jozef

2015-12-01

Although the development of super-resolution microscopy methods dates back to 1994, relevant applications in plant cell imaging only started to emerge in 2010. Since then, the principal super-resolution methods, including structured-illumination microscopy (SIM), photoactivation localization microscopy (PALM), stochastic optical reconstruction microscopy (STORM), and stimulated emission depletion microscopy (STED), have been implemented in plant cell research. However, progress has been limited due to the challenging properties of plant material. Here we summarize the basic principles of existing super-resolution methods and provide examples of applications in plant science. The limitations imposed by the nature of plant material are reviewed and the potential for future applications in plant cell imaging is highlighted. Copyright © 2015 Elsevier Ltd. All rights reserved.

Dissection of C. elegans behavioral genetics in 3-D environments

PubMed Central

Kwon, Namseop; Hwang, Ara B.; You, Young-Jai; V. Lee, Seung-Jae; Ho Je, Jung

2015-01-01

The nematode Caenorhabditis elegans is a widely used model for genetic dissection of animal behaviors. Despite extensive technical advances in imaging methods, it remains challenging to visualize and quantify C. elegans behaviors in three-dimensional (3-D) natural environments. Here we developed an innovative 3-D imaging method that enables quantification of C. elegans behavior in 3-D environments. Furthermore, for the first time, we characterized 3-D-specific behavioral phenotypes of mutant worms that have defects in head movement or mechanosensation. This approach allowed us to reveal previously unknown functions of genes in behavioral regulation. We expect that our 3-D imaging method will facilitate new investigations into genetic basis of animal behaviors in natural 3-D environments. PMID:25955271

Developing the Quantitative Histopathology Image Ontology (QHIO): A case study using the hot spot detection problem.

PubMed

Gurcan, Metin N; Tomaszewski, John; Overton, James A; Doyle, Scott; Ruttenberg, Alan; Smith, Barry

2017-02-01

Interoperability across data sets is a key challenge for quantitative histopathological imaging. There is a need for an ontology that can support effective merging of pathological image data with associated clinical and demographic data. To foster organized, cross-disciplinary, information-driven collaborations in the pathological imaging field, we propose to develop an ontology to represent imaging data and methods used in pathological imaging and analysis, and call it Quantitative Histopathological Imaging Ontology - QHIO. We apply QHIO to breast cancer hot-spot detection with the goal of enhancing reliability of detection by promoting the sharing of data between image analysts. Copyright © 2016 Elsevier Inc. All rights reserved.

ImageParser: a tool for finite element generation from three-dimensional medical images

PubMed Central

Yin, HM; Sun, LZ; Wang, G; Yamada, T; Wang, J; Vannier, MW

2004-01-01

Background The finite element method (FEM) is a powerful mathematical tool to simulate and visualize the mechanical deformation of tissues and organs during medical examinations or interventions. It is yet a challenge to build up an FEM mesh directly from a volumetric image partially because the regions (or structures) of interest (ROIs) may be irregular and fuzzy. Methods A software package, ImageParser, is developed to generate an FEM mesh from 3-D tomographic medical images. This software uses a semi-automatic method to detect ROIs from the context of image including neighboring tissues and organs, completes segmentation of different tissues, and meshes the organ into elements. Results The ImageParser is shown to build up an FEM model for simulating the mechanical responses of the breast based on 3-D CT images. The breast is compressed by two plate paddles under an overall displacement as large as 20% of the initial distance between the paddles. The strain and tangential Young's modulus distributions are specified for the biomechanical analysis of breast tissues. Conclusion The ImageParser can successfully exact the geometry of ROIs from a complex medical image and generate the FEM mesh with customer-defined segmentation information. PMID:15461787

Attenuation correction for brain PET imaging using deep neural network based on dixon and ZTE MR images.

PubMed

Gong, Kuang; Yang, Jaewon; Kim, Kyungsang; El Fakhri, Georges; Seo, Youngho; Li, Quanzheng

2018-05-23

Positron Emission Tomography (PET) is a functional imaging modality widely used in neuroscience studies. To obtain meaningful quantitative results from PET images, attenuation correction is necessary during image reconstruction. For PET/MR hybrid systems, PET attenuation is challenging as Magnetic Resonance (MR) images do not reflect attenuation coefficients directly. To address this issue, we present deep neural network methods to derive the continuous attenuation coefficients for brain PET imaging from MR images. With only Dixon MR images as the network input, the existing U-net structure was adopted and analysis using forty patient data sets shows it is superior than other Dixon based methods. When both Dixon and zero echo time (ZTE) images are available, we have proposed a modified U-net structure, named GroupU-net, to efficiently make use of both Dixon and ZTE information through group convolution modules when the network goes deeper. Quantitative analysis based on fourteen real patient data sets demonstrates that both network approaches can perform better than the standard methods, and the proposed network structure can further reduce the PET quantification error compared to the U-net structure. © 2018 Institute of Physics and Engineering in Medicine.

Infrared imagery acquisition process supporting simulation and real image training

NASA Astrophysics Data System (ADS)

O'Connor, John

2012-05-01

The increasing use of infrared sensors requires development of advanced infrared training and simulation tools to meet current Warfighter needs. In order to prepare the force, a challenge exists for training and simulation images to be both realistic and consistent with each other to be effective and avoid negative training. The US Army Night Vision and Electronic Sensors Directorate has corrected this deficiency by developing and implementing infrared image collection methods that meet the needs of both real image trainers and real-time simulations. The author presents innovative methods for collection of high-fidelity digital infrared images and the associated equipment and environmental standards. The collected images are the foundation for US Army, and USMC Recognition of Combat Vehicles (ROC-V) real image combat ID training and also support simulations including the Night Vision Image Generator and Synthetic Environment Core. The characteristics, consistency, and quality of these images have contributed to the success of these and other programs. To date, this method has been employed to generate signature sets for over 350 vehicles. The needs of future physics-based simulations will also be met by this data. NVESD's ROC-V image database will support the development of training and simulation capabilities as Warfighter needs evolve.

Image analysis tools and emerging algorithms for expression proteomics

PubMed Central

English, Jane A.; Lisacek, Frederique; Morris, Jeffrey S.; Yang, Guang-Zhong; Dunn, Michael J.

2012-01-01

Since their origins in academic endeavours in the 1970s, computational analysis tools have matured into a number of established commercial packages that underpin research in expression proteomics. In this paper we describe the image analysis pipeline for the established 2-D Gel Electrophoresis (2-DE) technique of protein separation, and by first covering signal analysis for Mass Spectrometry (MS), we also explain the current image analysis workflow for the emerging high-throughput ‘shotgun’ proteomics platform of Liquid Chromatography coupled to MS (LC/MS). The bioinformatics challenges for both methods are illustrated and compared, whilst existing commercial and academic packages and their workflows are described from both a user’s and a technical perspective. Attention is given to the importance of sound statistical treatment of the resultant quantifications in the search for differential expression. Despite wide availability of proteomics software, a number of challenges have yet to be overcome regarding algorithm accuracy, objectivity and automation, generally due to deterministic spot-centric approaches that discard information early in the pipeline, propagating errors. We review recent advances in signal and image analysis algorithms in 2-DE, MS, LC/MS and Imaging MS. Particular attention is given to wavelet techniques, automated image-based alignment and differential analysis in 2-DE, Bayesian peak mixture models and functional mixed modelling in MS, and group-wise consensus alignment methods for LC/MS. PMID:21046614

A Complete Color Normalization Approach to Histopathology Images Using Color Cues Computed From Saturation-Weighted Statistics.

PubMed

Li, Xingyu; Plataniotis, Konstantinos N

2015-07-01

In digital histopathology, tasks of segmentation and disease diagnosis are achieved by quantitative analysis of image content. However, color variation in image samples makes it challenging to produce reliable results. This paper introduces a complete normalization scheme to address the problem of color variation in histopathology images jointly caused by inconsistent biopsy staining and nonstandard imaging condition. Method : Different from existing normalization methods that either address partial cause of color variation or lump them together, our method identifies causes of color variation based on a microscopic imaging model and addresses inconsistency in biopsy imaging and staining by an illuminant normalization module and a spectral normalization module, respectively. In evaluation, we use two public datasets that are representative of histopathology images commonly received in clinics to examine the proposed method from the aspects of robustness to system settings, performance consistency against achromatic pixels, and normalization effectiveness in terms of histological information preservation. As the saturation-weighted statistics proposed in this study generates stable and reliable color cues for stain normalization, our scheme is robust to system parameters and insensitive to image content and achromatic colors. Extensive experimentation suggests that our approach outperforms state-of-the-art normalization methods as the proposed method is the only approach that succeeds to preserve histological information after normalization. The proposed color normalization solution would be useful to mitigate effects of color variation in pathology images on subsequent quantitative analysis.

Example-Based Image Colorization Using Locality Consistent Sparse Representation.

PubMed

Bo Li; Fuchen Zhao; Zhuo Su; Xiangguo Liang; Yu-Kun Lai; Rosin, Paul L

2017-11-01

Image colorization aims to produce a natural looking color image from a given gray-scale image, which remains a challenging problem. In this paper, we propose a novel example-based image colorization method exploiting a new locality consistent sparse representation. Given a single reference color image, our method automatically colorizes the target gray-scale image by sparse pursuit. For efficiency and robustness, our method operates at the superpixel level. We extract low-level intensity features, mid-level texture features, and high-level semantic features for each superpixel, which are then concatenated to form its descriptor. The collection of feature vectors for all the superpixels from the reference image composes the dictionary. We formulate colorization of target superpixels as a dictionary-based sparse reconstruction problem. Inspired by the observation that superpixels with similar spatial location and/or feature representation are likely to match spatially close regions from the reference image, we further introduce a locality promoting regularization term into the energy formulation, which substantially improves the matching consistency and subsequent colorization results. Target superpixels are colorized based on the chrominance information from the dominant reference superpixels. Finally, to further improve coherence while preserving sharpness, we develop a new edge-preserving filter for chrominance channels with the guidance from the target gray-scale image. To the best of our knowledge, this is the first work on sparse pursuit image colorization from single reference images. Experimental results demonstrate that our colorization method outperforms the state-of-the-art methods, both visually and quantitatively using a user study.

Fluorescent marker-based and marker-free discrimination between healthy and cancerous human tissues using hyper-spectral imaging

NASA Astrophysics Data System (ADS)

Arnold, Thomas; De Biasio, Martin; Leitner, Raimund

2015-06-01

Two problems are addressed in this paper (i) the fluorescent marker-based and the (ii) marker-free discrimination between healthy and cancerous human tissues. For both applications the performance of hyper-spectral methods are quantified. Fluorescent marker-based tissue classification uses a number of fluorescent markers to dye specific parts of a human cell. The challenge is that the emission spectra of the fluorescent dyes overlap considerably. They are, furthermore disturbed by the inherent auto-fluorescence of human tissue. This results in ambiguities and decreased image contrast causing difficulties for the treatment decision. The higher spectral resolution introduced by tunable-filter-based spectral imaging in combination with spectral unmixing techniques results in an improvement of the image contrast and therefore more reliable information for the physician to choose the treatment decision. Marker-free tissue classification is based solely on the subtle spectral features of human tissue without the use of artificial markers. The challenge in this case is that the spectral differences between healthy and cancerous tissues are subtle and embedded in intra- and inter-patient variations of these features. The contributions of this paper are (i) the evaluation of hyper-spectral imaging in combination with spectral unmixing techniques for fluorescence marker-based tissue classification, (ii) the evaluation of spectral imaging for marker-free intra surgery tissue classification. Within this paper, we consider real hyper-spectral fluorescence and endoscopy data sets to emphasize the practical capability of the proposed methods. It is shown that the combination of spectral imaging with multivariate statistical methods can improve the sensitivity and specificity of the detection and the staging of cancerous tissues compared to standard procedures.

A multimodal imaging framework for enhanced robot-assisted partial nephrectomy guidance

NASA Astrophysics Data System (ADS)

Halter, Ryan J.; Wu, Xiaotian; Hartov, Alex; Seigne, John; Khan, Shadab

2015-03-01

Robot-assisted laparoscopic partial nephrectomies (RALPN) are performed to treat patients with locally confined renal carcinoma. There are well-documented benefits to performing partial (opposed to radical) kidney resections and to using robot-assisted laparoscopic (opposed to open) approaches. However, there are challenges in identifying tumor margins and critical benign structures including blood vessels and collecting systems during current RALPN procedures. The primary objective of this effort is to couple multiple image and data streams together to augment visual information currently provided to surgeons performing RALPN and ultimately ensure complete tumor resection and minimal damage to functional structures (i.e. renal vasculature and collecting systems). To meet this challenge we have developed a framework and performed initial feasibility experiments to couple pre-operative high-resolution anatomic images with intraoperative MRI, ultrasound (US) and optical-based surface mapping and kidney tracking. With these registered images and data streams, we aim to overlay the high-resolution contrast-enhanced anatomic (CT or MR) images onto the surgeon's view screen for enhanced guidance. To date we have integrated the following components of our framework: 1) a method for tracking an intraoperative US probe to extract the kidney surface and a set of embedded kidney markers, 2) a method for co-registering intraoperative US scans with pre-operative MR scans, and 3) a method for deforming pre-op scans to match intraoperative scans. These components have been evaluated through phantom studies to demonstrate protocol feasibility.

Robust feature matching via support-line voting and affine-invariant ratios

NASA Astrophysics Data System (ADS)

Li, Jiayuan; Hu, Qingwu; Ai, Mingyao; Zhong, Ruofei

2017-10-01

Robust image matching is crucial for many applications of remote sensing and photogrammetry, such as image fusion, image registration, and change detection. In this paper, we propose a robust feature matching method based on support-line voting and affine-invariant ratios. We first use popular feature matching algorithms, such as SIFT, to obtain a set of initial matches. A support-line descriptor based on multiple adaptive binning gradient histograms is subsequently applied in the support-line voting stage to filter outliers. In addition, we use affine-invariant ratios computed by a two-line structure to refine the matching results and estimate the local affine transformation. The local affine model is more robust to distortions caused by elevation differences than the global affine transformation, especially for high-resolution remote sensing images and UAV images. Thus, the proposed method is suitable for both rigid and non-rigid image matching problems. Finally, we extract as many high-precision correspondences as possible based on the local affine extension and build a grid-wise affine model for remote sensing image registration. We compare the proposed method with six state-of-the-art algorithms on several data sets and show that our method significantly outperforms the other methods. The proposed method achieves 94.46% average precision on 15 challenging remote sensing image pairs, while the second-best method, RANSAC, only achieves 70.3%. In addition, the number of detected correct matches of the proposed method is approximately four times the number of initial SIFT matches.

Improved Seam-Line Searching Algorithm for UAV Image Mosaic with Optical Flow.

PubMed

Zhang, Weilong; Guo, Bingxuan; Li, Ming; Liao, Xuan; Li, Wenzhuo

2018-04-16

Ghosting and seams are two major challenges in creating unmanned aerial vehicle (UAV) image mosaic. In response to these problems, this paper proposes an improved method for UAV image seam-line searching. First, an image matching algorithm is used to extract and match the features of adjacent images, so that they can be transformed into the same coordinate system. Then, the gray scale difference, the gradient minimum, and the optical flow value of pixels in adjacent image overlapped area in a neighborhood are calculated, which can be applied to creating an energy function for seam-line searching. Based on that, an improved dynamic programming algorithm is proposed to search the optimal seam-lines to complete the UAV image mosaic. This algorithm adopts a more adaptive energy aggregation and traversal strategy, which can find a more ideal splicing path for adjacent UAV images and avoid the ground objects better. The experimental results show that the proposed method can effectively solve the problems of ghosting and seams in the panoramic UAV images.

Covariance descriptor fusion for target detection

NASA Astrophysics Data System (ADS)

Cukur, Huseyin; Binol, Hamidullah; Bal, Abdullah; Yavuz, Fatih

2016-05-01

Target detection is one of the most important topics for military or civilian applications. In order to address such detection tasks, hyperspectral imaging sensors provide useful images data containing both spatial and spectral information. Target detection has various challenging scenarios for hyperspectral images. To overcome these challenges, covariance descriptor presents many advantages. Detection capability of the conventional covariance descriptor technique can be improved by fusion methods. In this paper, hyperspectral bands are clustered according to inter-bands correlation. Target detection is then realized by fusion of covariance descriptor results based on the band clusters. The proposed combination technique is denoted Covariance Descriptor Fusion (CDF). The efficiency of the CDF is evaluated by applying to hyperspectral imagery to detect man-made objects. The obtained results show that the CDF presents better performance than the conventional covariance descriptor.

Automated grading of lumbar disc degeneration via supervised distance metric learning

NASA Astrophysics Data System (ADS)

He, Xiaoxu; Landis, Mark; Leung, Stephanie; Warrington, James; Shmuilovich, Olga; Li, Shuo

2017-03-01

Lumbar disc degeneration (LDD) is a commonly age-associated condition related to low back pain, while its consequences are responsible for over 90% of spine surgical procedures. In clinical practice, grading of LDD by inspecting MRI is a necessary step to make a suitable treatment plan. This step purely relies on physicians manual inspection so that it brings the unbearable tediousness and inefficiency. An automated method for grading of LDD is highly desirable. However, the technical implementation faces a big challenge from class ambiguity, which is typical in medical image classification problems with a large number of classes. This typical challenge is derived from the complexity and diversity of medical images, which lead to a serious class overlapping and brings a great challenge in discriminating different classes. To solve this problem, we proposed an automated grading approach, which is based on supervised distance metric learning to classify the input discs into four class labels (0: normal, 1: slight, 2: marked, 3: severe). By learning distance metrics from labeled instances, an optimal distance metric is modeled and with two attractive advantages: (1) keeps images from the same classes close, and (2) keeps images from different classes far apart. The experiments, performed in 93 subjects, demonstrated the superiority of our method with accuracy 0.9226, sensitivity 0.9655, specificity 0.9083, F-score 0.8615. With our approach, physicians will be free from the tediousness and patients will be provided an effective treatment.

Principles of Simultaneous PET/MR Imaging.

PubMed

Catana, Ciprian

2017-05-01

Combined PET/MR imaging scanners capable of acquiring simultaneously the complementary information provided by the 2 imaging modalities are now available for human use. After addressing the hardware challenges for integrating the 2 imaging modalities, most of the efforts in the field have focused on developing MR-based attenuation correction methods for neurologic and whole-body applications, implementing approaches for improving one modality by using the data provided by the other and exploring research and clinical applications that could benefit from the synergistic use of the multimodal data. Copyright © 2017 Elsevier Inc. All rights reserved.

Accurate, rapid identification of dislocation lines in coherent diffractive imaging via a min-max optimization formulation

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

Ulvestad, A.; Menickelly, M.; Wild, S. M.

Defects such as dislocations impact materials properties and their response during external stimuli. Imaging these defects in their native operating conditions to establish the structure-function relationship and, ultimately, to improve performance via defect engineering has remained a considerable challenge for both electron-based and x-ray-based imaging techniques. While Bragg coherent x-ray diffractive imaging (BCDI) is successful in many cases, nuances in identifying the dislocations has left manual identification as the preferred method. Derivative-based methods are also used, but they can be inaccurate and are computationally inefficient. Here we demonstrate a derivative-free method that is both more accurate and more computationally efficientmore » than either derivative-or human-based methods for identifying 3D dislocation lines in nanocrystal images produced by BCDI. We formulate the problem as a min-max optimization problem and show exceptional accuracy for experimental images. We demonstrate a 227x speedup for a typical experimental dataset with higher accuracy over current methods. We discuss the possibility of using this algorithm as part of a sparsity-based phase retrieval process. We also provide MATLAB code for use by other researchers.« less

Accurate, rapid identification of dislocation lines in coherent diffractive imaging via a min-max optimization formulation

NASA Astrophysics Data System (ADS)

Ulvestad, A.; Menickelly, M.; Wild, S. M.

2018-01-01

Defects such as dislocations impact materials properties and their response during external stimuli. Imaging these defects in their native operating conditions to establish the structure-function relationship and, ultimately, to improve performance via defect engineering has remained a considerable challenge for both electron-based and x-ray-based imaging techniques. While Bragg coherent x-ray diffractive imaging (BCDI) is successful in many cases, nuances in identifying the dislocations has left manual identification as the preferred method. Derivative-based methods are also used, but they can be inaccurate and are computationally inefficient. Here we demonstrate a derivative-free method that is both more accurate and more computationally efficient than either derivative- or human-based methods for identifying 3D dislocation lines in nanocrystal images produced by BCDI. We formulate the problem as a min-max optimization problem and show exceptional accuracy for experimental images. We demonstrate a 227x speedup for a typical experimental dataset with higher accuracy over current methods. We discuss the possibility of using this algorithm as part of a sparsity-based phase retrieval process. We also provide MATLAB code for use by other researchers.

WE-EF-BRD-00: New Developments in Hybrid MR-Treatment: Applications

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

NONE

2015-06-15

MRI-guided treatment is a growing area of medicine, particularly in radiotherapy and surgery. The exquisite soft tissue anatomic contrast offered by MRI, along with functional imaging, makes the use of MRI during therapeutic procedures very attractive. Challenging the utility of MRI in the therapy room are many issues including the physics of MRI and the impact on the environment and therapeutic instruments, the impact of the room and instruments on the MRI; safety, space, design and cost. In this session, the applications and challenges of MRI-guided treatment will be described. The session format is: Past, present and future: MRI-guided radiotherapymore » from 2005 to 2025: Jan Lagendijk Battling Maxwell’s equations: Physics challenges and solutions for hybrid MRI systems: Paul Keall I want it now!: Advances in MRI acquisition, reconstruction and the use of priors to enable fast anatomic and physiologic imaging to inform guidance and adaptation decisions: Yanle Hu MR in the OR: The growth and applications of MRI for interventional radiology and surgery: Rebecca Fahrig Learning Objectives: To understand the history and trajectory of MRI-guided radiotherapy To understand the challenges of integrating MR imaging systems with linear accelerators To understand the latest in fast MRI methods to enable the visualisation of anatomy and physiology on radiotherapy treatment timescales To understand the growing role and challenges of MRI for image-guided surgical procedures My disclosures are publicly available and updated at: http://sydney.edu.au/medicine/radiation-physics/about-us/disclosures.php.« less

Optimizing care for the obese patient in interventional radiology

PubMed Central

Aberle, Dwight; Charles, Hearns; Hodak, Steven; O’Neill, Daniel; Oklu, Rahmi; Deipolyi, Amy R.

2017-01-01

With the rising epidemic of obesity, interventional radiologists are treating increasing numbers of obese patients, as comorbidities associated with obesity preclude more invasive treatments. These patients are at heightened risk of vascular and oncologic disease, both of which often require interventional radiology care. Obese patients pose unique challenges in imaging, technical feasibility, and periprocedural monitoring. This review describes the technical and clinical challenges posed by this population, with proposed methods to mitigate these challenges and optimize care. PMID:28082253

Hierarchical and symmetric infant image registration by robust longitudinal-example-guided correspondence detection

PubMed Central

Wu, Yao; Wu, Guorong; Wang, Li; Munsell, Brent C.; Wang, Qian; Lin, Weili; Feng, Qianjin; Chen, Wufan; Shen, Dinggang

2015-01-01

Purpose: To investigate anatomical differences across individual subjects, or longitudinal changes in early brain development, it is important to perform accurate image registration. However, due to fast brain development and dynamic tissue appearance changes, it is very difficult to align infant brain images acquired from birth to 1-yr-old. Methods: To solve this challenging problem, a novel image registration method is proposed to align two infant brain images, regardless of age at acquisition. The main idea is to utilize the growth trajectories, or spatial-temporal correspondences, learned from a set of longitudinal training images, for guiding the registration of two different time-point images with different image appearances. Specifically, in the training stage, an intrinsic growth trajectory is first estimated for each training subject using the longitudinal images. To register two new infant images with potentially a large age gap, the corresponding images patches between each new image and its respective training images with similar age are identified. Finally, the registration between the two new images can be assisted by the learned growth trajectories from one time point to another time point that have been established in the training stage. To further improve registration accuracy, the proposed method is combined with a hierarchical and symmetric registration framework that can iteratively add new key points in both images to steer the estimation of the deformation between the two infant brain images under registration. Results: To evaluate image registration accuracy, the proposed method is used to align 24 infant subjects at five different time points (2-week-old, 3-month-old, 6-month-old, 9-month-old, and 12-month-old). Compared to the state-of-the-art methods, the proposed method demonstrated superior registration performance. Conclusions: The proposed method addresses the difficulties in the infant brain registration and produces better results compared to existing state-of-the-art registration methods. PMID:26133617

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

PubMed Central

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

2015-01-01

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

Microscopic neural image registration based on the structure of mitochondria

NASA Astrophysics Data System (ADS)

Cao, Huiwen; Han, Hua; Rao, Qiang; Xiao, Chi; Chen, Xi

2017-02-01

Microscopic image registration is a key component of the neural structure reconstruction with serial sections of neural tissue. The goal of microscopic neural image registration is to recover the 3D continuity and geometrical properties of specimen. During image registration, various distortions need to be corrected, including image rotation, translation, tissue deformation et.al, which come from the procedure of sample cutting, staining and imaging. Furthermore, there is only certain similarity between adjacent sections, and the degree of similarity depends on local structure of the tissue and the thickness of the sections. These factors make the microscopic neural image registration a challenging problem. To tackle the difficulty of corresponding landmarks extraction, we introduce a novel image registration method for Scanning Electron Microscopy (SEM) images of serial neural tissue sections based on the structure of mitochondria. The ellipsoidal shape of mitochondria ensures that the same mitochondria has similar shape between adjacent sections, and its characteristic of broad distribution in the neural tissue guarantees that landmarks based on the mitochondria distributed widely in the image. The proposed image registration method contains three parts: landmarks extraction between adjacent sections, corresponding landmarks matching and image deformation based on the correspondences. We demonstrate the performance of our method with SEM images of drosophila brain.

Light field creating and imaging with different order intensity derivatives

NASA Astrophysics Data System (ADS)

Wang, Yu; Jiang, Huan

2014-10-01

Microscopic image restoration and reconstruction is a challenging topic in the image processing and computer vision, which can be widely applied to life science, biology and medicine etc. A microscopic light field creating and three dimensional (3D) reconstruction method is proposed for transparent or partially transparent microscopic samples, which is based on the Taylor expansion theorem and polynomial fitting. Firstly the image stack of the specimen is divided into several groups in an overlapping or non-overlapping way along the optical axis, and the first image of every group is regarded as reference image. Then different order intensity derivatives are calculated using all the images of every group and polynomial fitting method based on the assumption that the structure of the specimen contained by the image stack in a small range along the optical axis are possessed of smooth and linear property. Subsequently, new images located any position from which to reference image the distance is Δz along the optical axis can be generated by means of Taylor expansion theorem and the calculated different order intensity derivatives. Finally, the microscopic specimen can be reconstructed in 3D form using deconvolution technology and all the images including both the observed images and the generated images. The experimental results show the effectiveness and feasibility of our method.

HEp-2 cell image classification method based on very deep convolutional networks with small datasets

NASA Astrophysics Data System (ADS)

Lu, Mengchi; Gao, Long; Guo, Xifeng; Liu, Qiang; Yin, Jianping

2017-07-01

Human Epithelial-2 (HEp-2) cell images staining patterns classification have been widely used to identify autoimmune diseases by the anti-Nuclear antibodies (ANA) test in the Indirect Immunofluorescence (IIF) protocol. Because manual test is time consuming, subjective and labor intensive, image-based Computer Aided Diagnosis (CAD) systems for HEp-2 cell classification are developing. However, methods proposed recently are mostly manual features extraction with low accuracy. Besides, the scale of available benchmark datasets is small, which does not exactly suitable for using deep learning methods. This issue will influence the accuracy of cell classification directly even after data augmentation. To address these issues, this paper presents a high accuracy automatic HEp-2 cell classification method with small datasets, by utilizing very deep convolutional networks (VGGNet). Specifically, the proposed method consists of three main phases, namely image preprocessing, feature extraction and classification. Moreover, an improved VGGNet is presented to address the challenges of small-scale datasets. Experimental results over two benchmark datasets demonstrate that the proposed method achieves superior performance in terms of accuracy compared with existing methods.

An Evaluation of Feature Learning Methods for High Resolution Image Classification

NASA Astrophysics Data System (ADS)

Tokarczyk, P.; Montoya, J.; Schindler, K.

2012-07-01

Automatic image classification is one of the fundamental problems of remote sensing research. The classification problem is even more challenging in high-resolution images of urban areas, where the objects are small and heterogeneous. Two questions arise, namely which features to extract from the raw sensor data to capture the local radiometry and image structure at each pixel or segment, and which classification method to apply to the feature vectors. While classifiers are nowadays well understood, selecting the right features remains a largely empirical process. Here we concentrate on the features. Several methods are evaluated which allow one to learn suitable features from unlabelled image data by analysing the image statistics. In a comparative study, we evaluate unsupervised feature learning with different linear and non-linear learning methods, including principal component analysis (PCA) and deep belief networks (DBN). We also compare these automatically learned features with popular choices of ad-hoc features including raw intensity values, standard combinations like the NDVI, a few PCA channels, and texture filters. The comparison is done in a unified framework using the same images, the target classes, reference data and a Random Forest classifier.

Advances in medical image computing.

PubMed

Tolxdorff, T; Deserno, T M; Handels, H; Meinzer, H-P

2009-01-01

Medical image computing has become a key technology in high-tech applications in medicine and an ubiquitous part of modern imaging systems and the related processes of clinical diagnosis and intervention. Over the past years significant progress has been made in the field, both on methodological and on application level. Despite this progress there are still big challenges to meet in order to establish image processing routinely in health care. In this issue, selected contributions of the German Conference on Medical Image Processing (BVM) are assembled to present latest advances in the field of medical image computing. The winners of scientific awards of the German Conference on Medical Image Processing (BVM) 2008 were invited to submit a manuscript on their latest developments and results for possible publication in Methods of Information in Medicine. Finally, seven excellent papers were selected to describe important aspects of recent advances in the field of medical image processing. The selected papers give an impression of the breadth and heterogeneity of new developments. New methods for improved image segmentation, non-linear image registration and modeling of organs are presented together with applications of image analysis methods in different medical disciplines. Furthermore, state-of-the-art tools and techniques to support the development and evaluation of medical image processing systems in practice are described. The selected articles describe different aspects of the intense development in medical image computing. The image processing methods presented enable new insights into the patient's image data and have the future potential to improve medical diagnostics and patient treatment.

FPGA Implementation of the Coupled Filtering Method and the Affine Warping Method.

PubMed

Zhang, Chen; Liang, Tianzhu; Mok, Philip K T; Yu, Weichuan

2017-07-01

In ultrasound image analysis, the speckle tracking methods are widely applied to study the elasticity of body tissue. However, "feature-motion decorrelation" still remains as a challenge for the speckle tracking methods. Recently, a coupled filtering method and an affine warping method were proposed to accurately estimate strain values, when the tissue deformation is large. The major drawback of these methods is the high computational complexity. Even the graphics processing unit (GPU)-based program requires a long time to finish the analysis. In this paper, we propose field-programmable gate array (FPGA)-based implementations of both methods for further acceleration. The capability of FPGAs on handling different image processing components in these methods is discussed. A fast and memory-saving image warping approach is proposed. The algorithms are reformulated to build a highly efficient pipeline on FPGA. The final implementations on a Xilinx Virtex-7 FPGA are at least 13 times faster than the GPU implementation on the NVIDIA graphic card (GeForce GTX 580).

Taking digital imaging to the next level: challenges and opportunities.

PubMed

Hobbs, W Cecyl

2004-01-01

New medical imaging technology, such as multi-detector computed tomography (CT) scanners and positron emission tomography (PET) scanners, are creating new possibilities for non-invasive diagnosis that are leading providers to invest heavily in these new technologies. The volume of data produced by such technology is so large that it cannot be "read" using traditional film-based methods, and once in digital form, it creates a massive data integration and archiving challenge. Despite the benefits of digital imaging and archiving, there are several key challenges that healthcare organizations should consider in planning, selecting, and implementing the information technology (IT) infrastructure to support digital imaging. Decisions about storage and image distribution are essentially questions of "where" and "how fast." When planning the digital archiving infrastructure, organizations should think about where they want to store and distribute their images. This is similar to decisions that organizations have to make in regard to physical film storage and distribution, except the portability of images is even greater in a digital environment. The principle of "network effects" seems like a simple concept, yet the effect is not always considered when implementing a technology plan. To fully realize the benefits of digital imaging, the radiology department must integrate the archiving solutions throughout the department and, ultimately, with applications across other departments and enterprises. Medical institutions can derive a number of benefits from implementing digital imaging and archiving solutions like PACS. Hospitals and imaging centers can use the transition from film-based imaging as a foundational opportunity to reduce costs, increase competitive advantage, attract talent, and improve service to patients. The key factors in achieving these goals include attention to the means of data storage, distribution and protection.

Image velocimetry and spectral analysis enable quantitative characterization of larval zebrafish gut motility.

PubMed

Ganz, J; Baker, R P; Hamilton, M K; Melancon, E; Diba, P; Eisen, J S; Parthasarathy, R

2018-05-02

Normal gut function requires rhythmic and coordinated movements that are affected by developmental processes, physical and chemical stimuli, and many debilitating diseases. The imaging and characterization of gut motility, especially regarding periodic, propagative contractions driving material transport, are therefore critical goals. Previous image analysis approaches have successfully extracted properties related to the temporal frequency of motility modes, but robust measures of contraction magnitude, especially from in vivo image data, remain challenging to obtain. We developed a new image analysis method based on image velocimetry and spectral analysis that reveals temporal characteristics such as frequency and wave propagation speed, while also providing quantitative measures of the amplitude of gut motion. We validate this approach using several challenges to larval zebrafish, imaged with differential interference contrast microscopy. Both acetylcholine exposure and feeding increase frequency and amplitude of motility. Larvae lacking enteric nervous system gut innervation show the same average motility frequency, but reduced and less variable amplitude compared to wild types. Our image analysis approach enables insights into gut dynamics in a wide variety of developmental and physiological contexts and can also be extended to analyze other types of cell movements. © 2018 John Wiley & Sons Ltd.

Possibilities of Processing Archival Photogrammetric Images Captured by Rollei 6006 Metric Camera Using Current Method

NASA Astrophysics Data System (ADS)

Dlesk, A.; Raeva, P.; Vach, K.

2018-05-01

Processing of analog photogrammetric negatives using current methods brings new challenges and possibilities, for example, creation of a 3D model from archival images which enables the comparison of historical state and current state of cultural heritage objects. The main purpose of this paper is to present possibilities of processing archival analog images captured by photogrammetric camera Rollei 6006 metric. In 1994, the Czech company EuroGV s.r.o. carried out photogrammetric measurements of former limestone quarry the Great America located in the Central Bohemian Region in the Czech Republic. All the negatives of photogrammetric images, complete documentation, coordinates of geodetically measured ground control points, calibration reports and external orientation of images calculated in the Combined Adjustment Program are preserved and were available for the current processing. Negatives of images were scanned and processed using structure from motion method (SfM). The result of the research is a statement of what accuracy is possible to expect from the proposed methodology using Rollei metric images originally obtained for terrestrial intersection photogrammetry while adhering to the proposed methodology.

Zero echo time MRI-only treatment planning for radiation therapy of brain tumors after resection.

PubMed

Boydev, C; Demol, B; Pasquier, D; Saint-Jalmes, H; Delpon, G; Reynaert, N

2017-10-01

Using magnetic resonance imaging (MRI) as the sole imaging modality for patient modeling in radiation therapy (RT) is a challenging task due to the need to derive electron density information from MRI and construct a so-called pseudo-computed tomography (pCT) image. We have previously published a new method to derive pCT images from head T1-weighted (T1-w) MR images using a single-atlas propagation scheme followed by a post hoc correction of the mapped CT numbers using local intensity information. The purpose of this study was to investigate the performance of our method with head zero echo time (ZTE) MR images. To evaluate results, the mean absolute error in bins of 20 HU was calculated with respect to the true planning CT scan of the patient. We demonstrated that applying our method using ZTE MR images instead of T1-w improved the correctness of the pCT in case of bone resection surgery prior to RT (that is, an example of large anatomical difference between the atlas and the patient). Copyright © 2017. Published by Elsevier Ltd.

Image quality improvement in MDCT cardiac imaging via SMART-RECON method

NASA Astrophysics Data System (ADS)

Li, Yinsheng; Cao, Ximiao; Xing, Zhanfeng; Sun, Xuguang; Hsieh, Jiang; Chen, Guang-Hong

2017-03-01

Coronary CT angiography (CCTA) is a challenging imaging task currently limited by the achievable temporal resolution of modern Multi-Detector CT (MDCT) scanners. In this paper, the recently proposed SMARTRECON method has been applied in MDCT-based CCTA imaging to improve the image quality without any prior knowledge of cardiac motion. After the prospective ECG-gated data acquisition from a short-scan angular span, the acquired data were sorted into several sub-sectors of view angles; each corresponds to a 1/4th of the short-scan angular range. Information of the cardiac motion was thus encoded into the data in each view angle sub-sector. The SMART-RECON algorithm was then applied to jointly reconstruct several image volumes, each of which is temporally consistent with the data acquired in the corresponding view angle sub-sector. Extensive numerical simulations were performed to validate the proposed technique and investigate the performance dependence.

Convolutional Sparse Coding for RGB+NIR Imaging.

PubMed

Hu, Xuemei; Heide, Felix; Dai, Qionghai; Wetzstein, Gordon

2018-04-01

Emerging sensor designs increasingly rely on novel color filter arrays (CFAs) to sample the incident spectrum in unconventional ways. In particular, capturing a near-infrared (NIR) channel along with conventional RGB color is an exciting new imaging modality. RGB+NIR sensing has broad applications in computational photography, such as low-light denoising, it has applications in computer vision, such as facial recognition and tracking, and it paves the way toward low-cost single-sensor RGB and depth imaging using structured illumination. However, cost-effective commercial CFAs suffer from severe spectral cross talk. This cross talk represents a major challenge in high-quality RGB+NIR imaging, rendering existing spatially multiplexed sensor designs impractical. In this work, we introduce a new approach to RGB+NIR image reconstruction using learned convolutional sparse priors. We demonstrate high-quality color and NIR imaging for challenging scenes, even including high-frequency structured NIR illumination. The effectiveness of the proposed method is validated on a large data set of experimental captures, and simulated benchmark results which demonstrate that this work achieves unprecedented reconstruction quality.

Segment and fit thresholding: a new method for image analysis applied to microarray and immunofluorescence data.

PubMed

Ensink, Elliot; Sinha, Jessica; Sinha, Arkadeep; Tang, Huiyuan; Calderone, Heather M; Hostetter, Galen; Winter, Jordan; Cherba, David; Brand, Randall E; Allen, Peter J; Sempere, Lorenzo F; Haab, Brian B

2015-10-06

Experiments involving the high-throughput quantification of image data require algorithms for automation. A challenge in the development of such algorithms is to properly interpret signals over a broad range of image characteristics, without the need for manual adjustment of parameters. Here we present a new approach for locating signals in image data, called Segment and Fit Thresholding (SFT). The method assesses statistical characteristics of small segments of the image and determines the best-fit trends between the statistics. Based on the relationships, SFT identifies segments belonging to background regions; analyzes the background to determine optimal thresholds; and analyzes all segments to identify signal pixels. We optimized the initial settings for locating background and signal in antibody microarray and immunofluorescence data and found that SFT performed well over multiple, diverse image characteristics without readjustment of settings. When used for the automated analysis of multicolor, tissue-microarray images, SFT correctly found the overlap of markers with known subcellular localization, and it performed better than a fixed threshold and Otsu's method for selected images. SFT promises to advance the goal of full automation in image analysis.

Optimized SIFTFlow for registration of whole-mount histology to reference optical images

PubMed Central

Shojaii, Rushin; Martel, Anne L.

2016-01-01

Abstract. The registration of two-dimensional histology images to reference images from other modalities is an important preprocessing step in the reconstruction of three-dimensional histology volumes. This is a challenging problem because of the differences in the appearances of histology images and other modalities, and the presence of large nonrigid deformations which occur during slide preparation. This paper shows the feasibility of using densely sampled scale-invariant feature transform (SIFT) features and a SIFTFlow deformable registration algorithm for coregistering whole-mount histology images with blockface optical images. We present a method for jointly optimizing the regularization parameters used by the SIFTFlow objective function and use it to determine the most appropriate values for the registration of breast lumpectomy specimens. We demonstrate that tuning the regularization parameters results in significant improvements in accuracy and we also show that SIFTFlow outperforms a previously described edge-based registration method. The accuracy of the histology images to blockface images registration using the optimized SIFTFlow method was assessed using an independent test set of images from five different lumpectomy specimens and the mean registration error was 0.32±0.22  mm. PMID:27774494

Segment and Fit Thresholding: A New Method for Image Analysis Applied to Microarray and Immunofluorescence Data

PubMed Central

Ensink, Elliot; Sinha, Jessica; Sinha, Arkadeep; Tang, Huiyuan; Calderone, Heather M.; Hostetter, Galen; Winter, Jordan; Cherba, David; Brand, Randall E.; Allen, Peter J.; Sempere, Lorenzo F.; Haab, Brian B.

2016-01-01

Certain experiments involve the high-throughput quantification of image data, thus requiring algorithms for automation. A challenge in the development of such algorithms is to properly interpret signals over a broad range of image characteristics, without the need for manual adjustment of parameters. Here we present a new approach for locating signals in image data, called Segment and Fit Thresholding (SFT). The method assesses statistical characteristics of small segments of the image and determines the best-fit trends between the statistics. Based on the relationships, SFT identifies segments belonging to background regions; analyzes the background to determine optimal thresholds; and analyzes all segments to identify signal pixels. We optimized the initial settings for locating background and signal in antibody microarray and immunofluorescence data and found that SFT performed well over multiple, diverse image characteristics without readjustment of settings. When used for the automated analysis of multi-color, tissue-microarray images, SFT correctly found the overlap of markers with known subcellular localization, and it performed better than a fixed threshold and Otsu’s method for selected images. SFT promises to advance the goal of full automation in image analysis. PMID:26339978

Time of flight imaging through scattering environments (Conference Presentation)

NASA Astrophysics Data System (ADS)

Le, Toan H.; Breitbach, Eric C.; Jackson, Jonathan A.; Velten, Andreas

2017-02-01

Light scattering is a primary obstacle to imaging in many environments. On small scales in biomedical microscopy and diffuse tomography scenarios scattering is caused by tissue. On larger scales scattering from dust and fog provide challenges to vision systems for self driving cars and naval remote imaging systems. We are developing scale models for scattering environments and investigation methods for improved imaging particularly using time of flight transient information. With the emergence of Single Photon Avalanche Diode detectors and fast semiconductor lasers, illumination and capture on picosecond timescales are becoming possible in inexpensive, compact, and robust devices. This opens up opportunities for new computational imaging techniques that make use of photon time of flight. Time of flight or range information is used in remote imaging scenarios in gated viewing and in biomedical imaging in time resolved diffuse tomography. In addition spatial filtering is popular in biomedical scenarios with structured illumination and confocal microscopy. We are presenting a combination analytical, computational, and experimental models that allow us develop and test imaging methods across scattering scenarios and scales. This framework will be used for proof of concept experiments to evaluate new computational imaging methods.

Prolonged in vivo imaging of Xenopus laevis.

PubMed

Hamilton, Paul W; Henry, Jonathan J

2014-08-01

While live imaging of embryonic development over long periods of time is a well established method for embryos of the frog Xenopus laevis, once development has progressed to the swimming stages, continuous live imaging becomes more challenging because the tadpoles must be immobilized. Current imaging techniques for these advanced stages generally require bringing the tadpoles in and out of anesthesia for short imaging sessions at selected time points, severely limiting the resolution of the data. Here we demonstrate that creating a constant flow of diluted tricaine methanesulfonate (MS-222) over a tadpole greatly improves their survival under anesthesia. Based on this result, we describe a new method for imaging stage 48 to 65 X. laevis, by circulating the anesthetic using a peristaltic pump. This supports the animal during continuous live imaging sessions for at least 48 hr. The addition of a stable optical window allows for high quality imaging through the anesthetic solution. This automated imaging system provides for the first time a method for continuous observations of developmental and regenerative processes in advanced stages of Xenopus over 2 days. Developmental Dynamics 243:1011-1019, 2014. © 2014 Wiley Periodicals, Inc. © 2014 Wiley Periodicals, Inc.

Generating region proposals for histopathological whole slide image retrieval.

PubMed

Ma, Yibing; Jiang, Zhiguo; Zhang, Haopeng; Xie, Fengying; Zheng, Yushan; Shi, Huaqiang; Zhao, Yu; Shi, Jun

2018-06-01

Content-based image retrieval is an effective method for histopathological image analysis. However, given a database of huge whole slide images (WSIs), acquiring appropriate region-of-interests (ROIs) for training is significant and difficult. Moreover, histopathological images can only be annotated by pathologists, resulting in the lack of labeling information. Therefore, it is an important and challenging task to generate ROIs from WSI and retrieve image with few labels. This paper presents a novel unsupervised region proposing method for histopathological WSI based on Selective Search. Specifically, the WSI is over-segmented into regions which are hierarchically merged until the WSI becomes a single region. Nucleus-oriented similarity measures for region mergence and Nucleus-Cytoplasm color space for histopathological image are specially defined to generate accurate region proposals. Additionally, we propose a new semi-supervised hashing method for image retrieval. The semantic features of images are extracted with Latent Dirichlet Allocation and transformed into binary hashing codes with Supervised Hashing. The methods are tested on a large-scale multi-class database of breast histopathological WSIs. The results demonstrate that for one WSI, our region proposing method can generate 7.3 thousand contoured regions which fit well with 95.8% of the ROIs annotated by pathologists. The proposed hashing method can retrieve a query image among 136 thousand images in 0.29 s and reach precision of 91% with only 10% of images labeled. The unsupervised region proposing method can generate regions as predictions of lesions in histopathological WSI. The region proposals can also serve as the training samples to train machine-learning models for image retrieval. The proposed hashing method can achieve fast and precise image retrieval with small amount of labels. Furthermore, the proposed methods can be potentially applied in online computer-aided-diagnosis systems. Copyright © 2018 Elsevier B.V. All rights reserved.

The challenge of on-tissue digestion for MALDI MSI- a comparison of different protocols to improve imaging experiments.

PubMed

Diehl, Hanna C; Beine, Birte; Elm, Julian; Trede, Dennis; Ahrens, Maike; Eisenacher, Martin; Marcus, Katrin; Meyer, Helmut E; Henkel, Corinna

2015-03-01

Mass spectrometry imaging (MSI) has become a powerful and successful tool in the context of biomarker detection especially in recent years. This emerging technique is based on the combination of histological information of a tissue and its corresponding spatial resolved mass spectrometric information. The identification of differentially expressed protein peaks between samples is still the method's bottleneck. Therefore, peptide MSI compared to protein MSI is closer to the final goal of identification since peptides are easier to measure than proteins. Nevertheless, the processing of peptide imaging samples is challenging due to experimental complexity. To address this issue, a method development study for peptide MSI using cryoconserved and formalin-fixed paraffin-embedded (FFPE) rat brain tissue is provided. Different digestion times, matrices, and proteases were tested to define an optimal workflow for peptide MSI. All practical experiments were done in triplicates and analyzed by the SCiLS Lab software, using structures derived from myelin basic protein (MBP) peaks, principal component analysis (PCA) and probabilistic latent semantic analysis (pLSA) to rate the experiments' quality. Blinded experimental evaluation in case of defining countable structures in the datasets was performed by three individuals. Such an extensive method development for peptide matrix-assisted laser desorption/ionization (MALDI) imaging experiments has not been performed so far, and the resulting problems and consequences were analyzed and discussed.

Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance.

PubMed

Yuan, Yading; Chao, Ming; Lo, Yeh-Chi

2017-09-01

Automatic skin lesion segmentation in dermoscopic images is a challenging task due to the low contrast between lesion and the surrounding skin, the irregular and fuzzy lesion borders, the existence of various artifacts, and various imaging acquisition conditions. In this paper, we present a fully automatic method for skin lesion segmentation by leveraging 19-layer deep convolutional neural networks that is trained end-to-end and does not rely on prior knowledge of the data. We propose a set of strategies to ensure effective and efficient learning with limited training data. Furthermore, we design a novel loss function based on Jaccard distance to eliminate the need of sample re-weighting, a typical procedure when using cross entropy as the loss function for image segmentation due to the strong imbalance between the number of foreground and background pixels. We evaluated the effectiveness, efficiency, as well as the generalization capability of the proposed framework on two publicly available databases. One is from ISBI 2016 skin lesion analysis towards melanoma detection challenge, and the other is the PH2 database. Experimental results showed that the proposed method outperformed other state-of-the-art algorithms on these two databases. Our method is general enough and only needs minimum pre- and post-processing, which allows its adoption in a variety of medical image segmentation tasks.

WE-EF-BRD-03: I Want It Now!: Advances in MRI Acquisition, Reconstruction and the Use of Priors to Enable Fast Anatomic and Physiologic Imaging to Inform Guidance and Adaptation Decisions

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

Hu, Y.

MRI-guided treatment is a growing area of medicine, particularly in radiotherapy and surgery. The exquisite soft tissue anatomic contrast offered by MRI, along with functional imaging, makes the use of MRI during therapeutic procedures very attractive. Challenging the utility of MRI in the therapy room are many issues including the physics of MRI and the impact on the environment and therapeutic instruments, the impact of the room and instruments on the MRI; safety, space, design and cost. In this session, the applications and challenges of MRI-guided treatment will be described. The session format is: Past, present and future: MRI-guided radiotherapymore » from 2005 to 2025: Jan Lagendijk Battling Maxwell’s equations: Physics challenges and solutions for hybrid MRI systems: Paul Keall I want it now!: Advances in MRI acquisition, reconstruction and the use of priors to enable fast anatomic and physiologic imaging to inform guidance and adaptation decisions: Yanle Hu MR in the OR: The growth and applications of MRI for interventional radiology and surgery: Rebecca Fahrig Learning Objectives: To understand the history and trajectory of MRI-guided radiotherapy To understand the challenges of integrating MR imaging systems with linear accelerators To understand the latest in fast MRI methods to enable the visualisation of anatomy and physiology on radiotherapy treatment timescales To understand the growing role and challenges of MRI for image-guided surgical procedures My disclosures are publicly available and updated at: http://sydney.edu.au/medicine/radiation-physics/about-us/disclosures.php.« less

Using image mapping towards biomedical and biological data sharing

PubMed Central

2013-01-01

Image-based data integration in eHealth and life sciences is typically concerned with the method used for anatomical space mapping, needed to retrieve, compare and analyse large volumes of biomedical data. In mapping one image onto another image, a mechanism is used to match and find the corresponding spatial regions which have the same meaning between the source and the matching image. Image-based data integration is useful for integrating data of various information structures. Here we discuss a broad range of issues related to data integration of various information structures, review exemplary work on image representation and mapping, and discuss the challenges that these techniques may bring. PMID:24059352

Study of Health Outcomes in School Children: Key Challenges and Lessons Learned from the Framingham Schools' Natural History of Nevi Study

ERIC Educational Resources Information Center

Geller, Alan C.; Oliveria, Susan A.; Bishop, Marilyn; Buckminster, Marcia; Brooks, Katie R.; Halpern, Allan C.

2007-01-01

Background: We describe the planning, recruitment, key challenges, and lessons learned in the development of a study of the evolution of nevi (moles) among children in a school setting. Methods: This population-based study of digital photography and dermoscopy of the child's back (overview, close-up, and dermoscopic images) and genetic specimens…

A novel star extraction method based on modified water flow model

NASA Astrophysics Data System (ADS)

Zhang, Hao; Niu, Yanxiong; Lu, Jiazhen; Ouyang, Zibiao; Yang, Yanqiang

2017-11-01

Star extraction is the essential procedure for attitude measurement of star sensor. The great challenge for star extraction is to segment star area exactly from various noise and background. In this paper, a novel star extraction method based on Modified Water Flow Model(MWFM) is proposed. The star image is regarded as a 3D terrain. The morphology is adopted for noise elimination and Tentative Star Area(TSA) selection. Star area can be extracted through adaptive water flowing within TSAs. This method can achieve accurate star extraction with improved efficiency under complex conditions such as loud noise and uneven backgrounds. Several groups of different types of star images are processed using proposed method. Comparisons with existing methods are conducted. Experimental results show that MWFM performs excellently under different imaging conditions. The star extraction rate is better than 95%. The star centroid accuracy is better than 0.075 pixels. The time-consumption is also significantly reduced.

A Review of Significant Advances in Neutron Imaging from Conception to the Present

NASA Astrophysics Data System (ADS)

Brenizer, J. S.

This review summarizes the history of neutron imaging with a focus on the significant events and technical advancements in neutron imaging methods, from the first radiograph to more recent imaging methods. A timeline is presented to illustrate the key accomplishments that advanced the neutron imaging technique. Only three years after the discovery of the neutron by English physicist James Chadwick in 1932, neutron imaging began with the work of Hartmut Kallmann and Ernst Kuhn in Berlin, Germany, from 1935-1944. Kallmann and Kuhn were awarded a joint US Patent issued in January 1940. Little progress was made until the mid-1950's when Thewlis utilized a neutron beam from the BEPO reactor at Harwell, marking the beginning of the application of neutron imaging to practical applications. As the film method was improved, imaging moved from a qualitative to a quantitative technique, with applications in industry and in nuclear fuels. Standards were developed to aid in the quantification of the neutron images and the facility's capabilities. The introduction of dynamic neutron imaging (initially called real-time neutron radiography and neutron television) in the late 1970's opened the door to new opportunities and new challenges. As the electronic imaging matured, the introduction of the CCD imaging devices and solid-state light intensifiers helped address some of these challenges. Development of improved imaging devices for the medical community has had a major impact on neutron imaging. Additionally, amorphous silicon sensors provided improvements in temporal resolution, while providing a reasonably large imaging area. The development of new neutron imaging sensors and the development of new neutron imaging techniques in the past decade has advanced the technique's ability to provide insight and understanding of problems that other non-destructive techniques could not provide. This rapid increase in capability and application would not have been possible without the advances in computer processing speed and increased memory storage. For example, images with enhanced contrast are created by using the reflection, refraction, diffraction and ultra small angle scattering interactions. It is somewhat ironic that, like the first development of neutron images, the technique remains limited by the availability of high-intensity neutron sources, both in the facility cost and portability.

Measurement of the velocity of neutral fragments by the "correlated ion and neutral time of flight" method combined with "velocity-map imaging"

NASA Astrophysics Data System (ADS)

Berthias, F.; Feketeová, L.; Della Negra, R.; Dupasquier, T.; Fillol, R.; Abdoul-Carime, H.; Farizon, B.; Farizon, M.; Märk, T. D.

2017-08-01

In the challenging field of imaging molecular dynamics, a novel method has been developed and implemented that allows the measurement of the velocity of neutral fragments produced in collision induced dissociation experiments on an event-by-event basis. This has been made possible by combining a correlated ion and neutral time of flight method with a velocity map imaging technique. This new method relies on a multiparametric correlated detection of the neutral and charged fragments from collision induced dissociation on one single detector. Its implementation on the DIAM device (Device for irradiation of biomolecular clusters) (Dispositif d'Irradiation d'Agrégats bioMoléculaires) allowed us to measure the velocity distribution of water molecules evaporated from collision induced dissociation of mass- and energy-selected protonated water clusters.

Determination of optical absorption coefficient with focusing photoacoustic imaging.

PubMed

Li, Zhifang; Li, Hui; Zeng, Zhiping; Xie, Wenming; Chen, Wei R

2012-06-01

Absorption coefficient of biological tissue is an important factor for photothermal therapy and photoacoustic imaging. However, its determination remains a challenge. In this paper, we propose a method using focusing photoacoustic imaging technique to quantify the target optical absorption coefficient. It utilizes the ratio of the amplitude of the peak signal from the top boundary of the target to that from the bottom boundary based on wavelet transform. This method is self-calibrating. Factors, such as absolute optical fluence, ultrasound parameters, and Grüneisen parameter, can be canceled by dividing the amplitudes of the two peaks. To demonstrate this method, we quantified the optical absorption coefficient of a target with various concentrations of an absorbing dye. This method is particularly useful to provide accurate absorption coefficient for predicting the outcomes of photothermal interaction for cancer treatment with absorption enhancement.

Atomic-resolution transmission electron microscopy of electron beam–sensitive crystalline materials

NASA Astrophysics Data System (ADS)

Zhang, Daliang; Zhu, Yihan; Liu, Lingmei; Ying, Xiangrong; Hsiung, Chia-En; Sougrat, Rachid; Li, Kun; Han, Yu

2018-02-01

High-resolution imaging of electron beam–sensitive materials is one of the most difficult applications of transmission electron microscopy (TEM). The challenges are manifold, including the acquisition of images with extremely low beam doses, the time-constrained search for crystal zone axes, the precise image alignment, and the accurate determination of the defocus value. We develop a suite of methods to fulfill these requirements and acquire atomic-resolution TEM images of several metal organic frameworks that are generally recognized as highly sensitive to electron beams. The high image resolution allows us to identify individual metal atomic columns, various types of surface termination, and benzene rings in the organic linkers. We also apply our methods to other electron beam–sensitive materials, including the organic-inorganic hybrid perovskite CH3NH3PbBr3.

Deformable image registration with content mismatch: a demons variant to account for added material and surgical devices in the target image

NASA Astrophysics Data System (ADS)

Nithiananthan, S.; Uneri, A.; Schafer, S.; Mirota, D.; Otake, Y.; Stayman, J. W.; Zbijewski, W.; Khanna, A. J.; Reh, D. D.; Gallia, G. L.; Siewerdsen, J. H.

2013-03-01

Fast, accurate, deformable image registration is an important aspect of image-guided interventions. Among the factors that can confound registration is the presence of additional material in the intraoperative image - e.g., contrast bolus or a surgical implant - that was not present in the prior image. Existing deformable registration methods generally fail to account for tissue excised between image acquisitions and typically simply "move" voxels within the images with no ability to account for tissue that is removed or introduced between scans. We present a variant of the Demons algorithm to accommodate such content mismatch. The approach combines segmentation of mismatched content with deformable registration featuring an extra pseudo-spatial dimension representing a reservoir from which material can be drawn into the registered image. Previous work tested the registration method in the presence of tissue excision ("missing tissue"). The current paper tests the method in the presence of additional material in the target image and presents a general method by which either missing or additional material can be accommodated. The method was tested in phantom studies, simulations, and cadaver models in the context of intraoperative cone-beam CT with three examples of content mismatch: a variable-diameter bolus (contrast injection); surgical device (rod), and additional material (bone cement). Registration accuracy was assessed in terms of difference images and normalized cross correlation (NCC). We identify the difficulties that traditional registration algorithms encounter when faced with content mismatch and evaluate the ability of the proposed method to overcome these challenges.

3D Filament Network Segmentation with Multiple Active Contours

NASA Astrophysics Data System (ADS)

Xu, Ting; Vavylonis, Dimitrios; Huang, Xiaolei

2014-03-01

Fluorescence microscopy is frequently used to study two and three dimensional network structures formed by cytoskeletal polymer fibers such as actin filaments and microtubules. While these cytoskeletal structures are often dilute enough to allow imaging of individual filaments or bundles of them, quantitative analysis of these images is challenging. To facilitate quantitative, reproducible and objective analysis of the image data, we developed a semi-automated method to extract actin networks and retrieve their topology in 3D. Our method uses multiple Stretching Open Active Contours (SOACs) that are automatically initialized at image intensity ridges and then evolve along the centerlines of filaments in the network. SOACs can merge, stop at junctions, and reconfigure with others to allow smooth crossing at junctions of filaments. The proposed approach is generally applicable to images of curvilinear networks with low SNR. We demonstrate its potential by extracting the centerlines of synthetic meshwork images, actin networks in 2D TIRF Microscopy images, and 3D actin cable meshworks of live fission yeast cells imaged by spinning disk confocal microscopy.

Low-dose 4D cardiac imaging in small animals using dual source micro-CT

NASA Astrophysics Data System (ADS)

Holbrook, M.; Clark, D. P.; Badea, C. T.

2018-01-01

Micro-CT is widely used in preclinical studies, generating substantial interest in extending its capabilities in functional imaging applications such as blood perfusion and cardiac function. However, imaging cardiac structure and function in mice is challenging due to their small size and rapid heart rate. To overcome these challenges, we propose and compare improvements on two strategies for cardiac gating in dual-source, preclinical micro-CT: fast prospective gating (PG) and uncorrelated retrospective gating (RG). These sampling strategies combined with a sophisticated iterative image reconstruction algorithm provide faster acquisitions and high image quality in low-dose 4D (i.e. 3D  +  Time) cardiac micro-CT. Fast PG is performed under continuous subject rotation which results in interleaved projection angles between cardiac phases. Thus, fast PG provides a well-sampled temporal average image for use as a prior in iterative reconstruction. Uncorrelated RG incorporates random delays during sampling to prevent correlations between heart rate and sampling rate. We have performed both simulations and animal studies to validate these new sampling protocols. Sampling times for 1000 projections using fast PG and RG were 2 and 3 min, respectively, and the total dose was 170 mGy each. Reconstructions were performed using a 4D iterative reconstruction technique based on the split Bregman method. To examine undersampling robustness, subsets of 500 and 250 projections were also used for reconstruction. Both sampling strategies in conjunction with our iterative reconstruction method are capable of resolving cardiac phases and provide high image quality. In general, for equal numbers of projections, fast PG shows fewer errors than RG and is more robust to undersampling. Our results indicate that only 1000-projection based reconstruction with fast PG satisfies a 5% error criterion in left ventricular volume estimation. These methods promise low-dose imaging with a wide range of preclinical applications in cardiac imaging.

A robust object-based shadow detection method for cloud-free high resolution satellite images over urban areas and water bodies

NASA Astrophysics Data System (ADS)

Tatar, Nurollah; Saadatseresht, Mohammad; Arefi, Hossein; Hadavand, Ahmad

2018-06-01

Unwanted contrast in high resolution satellite images such as shadow areas directly affects the result of further processing in urban remote sensing images. Detecting and finding the precise position of shadows is critical in different remote sensing processing chains such as change detection, image classification and digital elevation model generation from stereo images. The spectral similarity between shadow areas, water bodies, and some dark asphalt roads makes the development of robust shadow detection algorithms challenging. In addition, most of the existing methods work on pixel-level and neglect the contextual information contained in neighboring pixels. In this paper, a new object-based shadow detection framework is introduced. In the proposed method a pixel-level shadow mask is built by extending established thresholding methods with a new C4 index which enables to solve the ambiguity of shadow and water bodies. Then the pixel-based results are further processed in an object-based majority analysis to detect the final shadow objects. Four different high resolution satellite images are used to validate this new approach. The result shows the superiority of the proposed method over some state-of-the-art shadow detection method with an average of 96% in F-measure.

Accessible maps for the color vision deficient observers: past and present knowledge and future possibilities

NASA Astrophysics Data System (ADS)

Kvitle, Anne Kristin

2018-05-01

Color is part of the visual variables in map, serving an aesthetic part and as a guide of attention. Impaired color vision affects the ability to distinguish colors, which makes the task of decoding the map colors difficult. Map reading is reported as a challenging task for these observers, especially when the size of stimuli is small. The aim of this study is to review existing methods for map design for color vision deficient users. A systematic review of research literature and case studies of map design for CVD observers has been conducted in order to give an overview of current knowledge and future research challenges. In addition, relevant research on simulations of CVD and color image enhancement for these observers from other fields of industry is included. The study identified two main approaches: pre-processing by using accessible colors and post-processing by using enhancement methods. Some of the methods may be applied for maps, but requires tailoring of test images according to map types.

Topical Review: Unique Contributions of Magnetic Resonance Imaging to Pediatric Psychology Research.

PubMed

Jensen, Chad D; Duraccio, Kara M; Carbine, Kaylie M; Kirwan, C Brock

2016-03-01

This review aims to provide a brief introduction of the utility of magnetic resonance imaging (MRI) methods in pediatric psychology research, describe several exemplar studies that highlight the unique benefits of MRI techniques for pediatric psychology research, and detail methods for addressing several challenges inherent to pediatric MRI research. Literature review. Numerous useful applications of MRI research in pediatric psychology have been illustrated in published research. MRI methods yield information that cannot be obtained using neuropsychological or behavioral measures. Using MRI in pediatric psychology research may facilitate examination of neural structures and processes that underlie health behaviors. Challenges inherent to conducting MRI research with pediatric research participants (e.g., head movement) may be addressed using evidence-based strategies. We encourage pediatric psychology researchers to consider adopting MRI techniques to answer research questions relevant to pediatric health and illness. © The Author 2015. Published by Oxford University Press on behalf of the Society of Pediatric Psychology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Segmentation of the pectoral muscle in breast MR images using structure tensor and deformable model

NASA Astrophysics Data System (ADS)

Lee, Myungeun; Kim, Jong Hyo

2012-02-01

Recently, breast MR images have been used in wider clinical area including diagnosis, treatment planning, and treatment response evaluation, which requests quantitative analysis and breast tissue segmentation. Although several methods have been proposed for segmenting MR images, segmenting out breast tissues robustly from surrounding structures in a wide range of anatomical diversity still remains challenging. Therefore, in this paper, we propose a practical and general-purpose approach for segmenting the pectoral muscle boundary based on the structure tensor and deformable model. The segmentation work flow comprises four key steps: preprocessing, detection of the region of interest (ROI) within the breast region, segmenting the pectoral muscle and finally extracting and refining the pectoral muscle boundary. From experimental results we show that the proposed method can segment the pectoral muscle robustly in diverse patient cases. In addition, the proposed method will allow the application of the quantification research for various breast images.

Dynamic SPECT reconstruction from few projections: a sparsity enforced matrix factorization approach

NASA Astrophysics Data System (ADS)

Ding, Qiaoqiao; Zan, Yunlong; Huang, Qiu; Zhang, Xiaoqun

2015-02-01

The reconstruction of dynamic images from few projection data is a challenging problem, especially when noise is present and when the dynamic images are vary fast. In this paper, we propose a variational model, sparsity enforced matrix factorization (SEMF), based on low rank matrix factorization of unknown images and enforced sparsity constraints for representing both coefficients and bases. The proposed model is solved via an alternating iterative scheme for which each subproblem is convex and involves the efficient alternating direction method of multipliers (ADMM). The convergence of the overall alternating scheme for the nonconvex problem relies upon the Kurdyka-Łojasiewicz property, recently studied by Attouch et al (2010 Math. Oper. Res. 35 438) and Attouch et al (2013 Math. Program. 137 91). Finally our proof-of-concept simulation on 2D dynamic images shows the advantage of the proposed method compared to conventional methods.

Dissociations between behavioural and functional magnetic resonance imaging-based evaluations of cognitive function after brain injury

PubMed Central

Bardin, Jonathan C.; Fins, Joseph J.; Katz, Douglas I.; Hersh, Jennifer; Heier, Linda A.; Tabelow, Karsten; Dyke, Jonathan P.; Ballon, Douglas J.; Schiff, Nicholas D.

2011-01-01

Functional neuroimaging methods hold promise for the identification of cognitive function and communication capacity in some severely brain-injured patients who may not retain sufficient motor function to demonstrate their abilities. We studied seven severely brain-injured patients and a control group of 14 subjects using a novel hierarchical functional magnetic resonance imaging assessment utilizing mental imagery responses. Whereas the control group showed consistent and accurate (for communication) blood-oxygen-level-dependent responses without exception, the brain-injured subjects showed a wide variation in the correlation of blood-oxygen-level-dependent responses and overt behavioural responses. Specifically, the brain-injured subjects dissociated bedside and functional magnetic resonance imaging-based command following and communication capabilities. These observations reveal significant challenges in developing validated functional magnetic resonance imaging-based methods for clinical use and raise interesting questions about underlying brain function assayed using these methods in brain-injured subjects. PMID:21354974

Vessel Segmentation in Retinal Images Using Multi-scale Line Operator and K-Means Clustering.

PubMed

Saffarzadeh, Vahid Mohammadi; Osareh, Alireza; Shadgar, Bita

2014-04-01

Detecting blood vessels is a vital task in retinal image analysis. The task is more challenging with the presence of bright and dark lesions in retinal images. Here, a method is proposed to detect vessels in both normal and abnormal retinal fundus images based on their linear features. First, the negative impact of bright lesions is reduced by using K-means segmentation in a perceptive space. Then, a multi-scale line operator is utilized to detect vessels while ignoring some of the dark lesions, which have intensity structures different from the line-shaped vessels in the retina. The proposed algorithm is tested on two publicly available STARE and DRIVE databases. The performance of the method is measured by calculating the area under the receiver operating characteristic curve and the segmentation accuracy. The proposed method achieves 0.9483 and 0.9387 localization accuracy against STARE and DRIVE respectively.

A multi-resolution strategy for a multi-objective deformable image registration framework that accommodates large anatomical differences

NASA Astrophysics Data System (ADS)

Alderliesten, Tanja; Bosman, Peter A. N.; Sonke, Jan-Jakob; Bel, Arjan

2014-03-01

Currently, two major challenges dominate the field of deformable image registration. The first challenge is related to the tuning of the developed methods to specific problems (i.e. how to best combine different objectives such as similarity measure and transformation effort). This is one of the reasons why, despite significant progress, clinical implementation of such techniques has proven to be difficult. The second challenge is to account for large anatomical differences (e.g. large deformations, (dis)appearing structures) that occurred between image acquisitions. In this paper, we study a framework based on multi-objective optimization to improve registration robustness and to simplify tuning for specific applications. Within this framework we specifically consider the use of an advanced model-based evolutionary algorithm for optimization and a dual-dynamic transformation model (i.e. two "non-fixed" grids: one for the source- and one for the target image) to accommodate for large anatomical differences. The framework computes and presents multiple outcomes that represent efficient trade-offs between the different objectives (a so-called Pareto front). In image processing it is common practice, for reasons of robustness and accuracy, to use a multi-resolution strategy. This is, however, only well-established for single-objective registration methods. Here we describe how such a strategy can be realized for our multi-objective approach and compare its results with a single-resolution strategy. For this study we selected the case of prone-supine breast MRI registration. Results show that the well-known advantages of a multi-resolution strategy are successfully transferred to our multi-objective approach, resulting in superior (i.e. Pareto-dominating) outcomes.

A systematic review and checklist presenting the main challenges for health economic modeling in personalized medicine: towards implementing patient-level models.

PubMed

Degeling, Koen; Koffijberg, Hendrik; IJzerman, Maarten J

2017-02-01

The ongoing development of genomic medicine and the use of molecular and imaging markers in personalized medicine (PM) has arguably challenged the field of health economic modeling (HEM). This study aims to provide detailed insights into the current status of HEM in PM, in order to identify if and how modeling methods are used to address the challenges described in literature. Areas covered: A review was performed on studies that simulate health economic outcomes for personalized clinical pathways. Decision tree modeling and Markov modeling were the most observed methods. Not all identified challenges were frequently found, challenges regarding companion diagnostics, diagnostic performance, and evidence gaps were most often found. However, the extent to which challenges were addressed varied considerably between studies. Expert commentary: Challenges for HEM in PM are not yet routinely addressed which may indicate that either (1) their impact is less severe than expected, (2) they are hard to address and therefore not managed appropriately, or (3) HEM in PM is still in an early stage. As evidence on the impact of these challenges is still lacking, we believe that more concrete examples are needed to illustrate the identified challenges and to demonstrate methods to handle them.

An edge-directed interpolation method for fetal spine MR images.

PubMed

Yu, Shaode; Zhang, Rui; Wu, Shibin; Hu, Jiani; Xie, Yaoqin

2013-10-10

Fetal spinal magnetic resonance imaging (MRI) is a prenatal routine for proper assessment of fetus development, especially when suspected spinal malformations occur while ultrasound fails to provide details. Limited by hardware, fetal spine MR images suffer from its low resolution.High-resolution MR images can directly enhance readability and improve diagnosis accuracy. Image interpolation for higher resolution is required in clinical situations, while many methods fail to preserve edge structures. Edge carries heavy structural messages of objects in visual scenes for doctors to detect suspicions, classify malformations and make correct diagnosis. Effective interpolation with well-preserved edge structures is still challenging. In this paper, we propose an edge-directed interpolation (EDI) method and apply it on a group of fetal spine MR images to evaluate its feasibility and performance. This method takes edge messages from Canny edge detector to guide further pixel modification. First, low-resolution (LR) images of fetal spine are interpolated into high-resolution (HR) images with targeted factor by bi-linear method. Then edge information from LR and HR images is put into a twofold strategy to sharpen or soften edge structures. Finally a HR image with well-preserved edge structures is generated. The HR images obtained from proposed method are validated and compared with that from other four EDI methods. Performances are evaluated from six metrics, and subjective analysis of visual quality is based on regions of interest (ROI). All these five EDI methods are able to generate HR images with enriched details. From quantitative analysis of six metrics, the proposed method outperforms the other four from signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), structure similarity index (SSIM), feature similarity index (FSIM) and mutual information (MI) with seconds-level time consumptions (TC). Visual analysis of ROI shows that the proposed method maintains better consistency in edge structures with the original images. The proposed method classifies edge orientations into four categories and well preserves structures. It generates convincing HR images with fine details and is suitable in real-time situations. Iterative curvature-based interpolation (ICBI) method may result in crisper edges, while the other three methods are sensitive to noise and artifacts.

Hybrid region merging method for segmentation of high-resolution remote sensing images

NASA Astrophysics Data System (ADS)

Zhang, Xueliang; Xiao, Pengfeng; Feng, Xuezhi; Wang, Jiangeng; Wang, Zuo

2014-12-01

Image segmentation remains a challenging problem for object-based image analysis. In this paper, a hybrid region merging (HRM) method is proposed to segment high-resolution remote sensing images. HRM integrates the advantages of global-oriented and local-oriented region merging strategies into a unified framework. The globally most-similar pair of regions is used to determine the starting point of a growing region, which provides an elegant way to avoid the problem of starting point assignment and to enhance the optimization ability for local-oriented region merging. During the region growing procedure, the merging iterations are constrained within the local vicinity, so that the segmentation is accelerated and can reflect the local context, as compared with the global-oriented method. A set of high-resolution remote sensing images is used to test the effectiveness of the HRM method, and three region-based remote sensing image segmentation methods are adopted for comparison, including the hierarchical stepwise optimization (HSWO) method, the local-mutual best region merging (LMM) method, and the multiresolution segmentation (MRS) method embedded in eCognition Developer software. Both the supervised evaluation and visual assessment show that HRM performs better than HSWO and LMM by combining both their advantages. The segmentation results of HRM and MRS are visually comparable, but HRM can describe objects as single regions better than MRS, and the supervised and unsupervised evaluation results further prove the superiority of HRM.

Individualized statistical learning from medical image databases: application to identification of brain lesions.

PubMed

Erus, Guray; Zacharaki, Evangelia I; Davatzikos, Christos

2014-04-01

This paper presents a method for capturing statistical variation of normal imaging phenotypes, with emphasis on brain structure. The method aims to estimate the statistical variation of a normative set of images from healthy individuals, and identify abnormalities as deviations from normality. A direct estimation of the statistical variation of the entire volumetric image is challenged by the high-dimensionality of images relative to smaller sample sizes. To overcome this limitation, we iteratively sample a large number of lower dimensional subspaces that capture image characteristics ranging from fine and localized to coarser and more global. Within each subspace, a "target-specific" feature selection strategy is applied to further reduce the dimensionality, by considering only imaging characteristics present in a test subject's images. Marginal probability density functions of selected features are estimated through PCA models, in conjunction with an "estimability" criterion that limits the dimensionality of estimated probability densities according to available sample size and underlying anatomy variation. A test sample is iteratively projected to the subspaces of these marginals as determined by PCA models, and its trajectory delineates potential abnormalities. The method is applied to segmentation of various brain lesion types, and to simulated data on which superiority of the iterative method over straight PCA is demonstrated. Copyright © 2014 Elsevier B.V. All rights reserved.

Individualized Statistical Learning from Medical Image Databases: Application to Identification of Brain Lesions

PubMed Central

Erus, Guray; Zacharaki, Evangelia I.; Davatzikos, Christos

2014-01-01

This paper presents a method for capturing statistical variation of normal imaging phenotypes, with emphasis on brain structure. The method aims to estimate the statistical variation of a normative set of images from healthy individuals, and identify abnormalities as deviations from normality. A direct estimation of the statistical variation of the entire volumetric image is challenged by the high-dimensionality of images relative to smaller sample sizes. To overcome this limitation, we iteratively sample a large number of lower dimensional subspaces that capture image characteristics ranging from fine and localized to coarser and more global. Within each subspace, a “target-specific” feature selection strategy is applied to further reduce the dimensionality, by considering only imaging characteristics present in a test subject’s images. Marginal probability density functions of selected features are estimated through PCA models, in conjunction with an “estimability” criterion that limits the dimensionality of estimated probability densities according to available sample size and underlying anatomy variation. A test sample is iteratively projected to the subspaces of these marginals as determined by PCA models, and its trajectory delineates potential abnormalities. The method is applied to segmentation of various brain lesion types, and to simulated data on which superiority of the iterative method over straight PCA is demonstrated. PMID:24607564

High-Throughput Histopathological Image Analysis via Robust Cell Segmentation and Hashing

PubMed Central

Zhang, Xiaofan; Xing, Fuyong; Su, Hai; Yang, Lin; Zhang, Shaoting

2015-01-01

Computer-aided diagnosis of histopathological images usually requires to examine all cells for accurate diagnosis. Traditional computational methods may have efficiency issues when performing cell-level analysis. In this paper, we propose a robust and scalable solution to enable such analysis in a real-time fashion. Specifically, a robust segmentation method is developed to delineate cells accurately using Gaussian-based hierarchical voting and repulsive balloon model. A large-scale image retrieval approach is also designed to examine and classify each cell of a testing image by comparing it with a massive database, e.g., half-million cells extracted from the training dataset. We evaluate this proposed framework on a challenging and important clinical use case, i.e., differentiation of two types of lung cancers (the adenocarcinoma and squamous carcinoma), using thousands of lung microscopic tissue images extracted from hundreds of patients. Our method has achieved promising accuracy and running time by searching among half-million cells. PMID:26599156

Quantitative x-ray phase-contrast imaging using a single grating of comparable pitch to sample feature size.

PubMed

Morgan, Kaye S; Paganin, David M; Siu, Karen K W

2011-01-01

The ability to quantitatively retrieve transverse phase maps during imaging by using coherent x rays often requires a precise grating or analyzer-crystal-based setup. Imaging of live animals presents further challenges when these methods require multiple exposures for image reconstruction. We present a simple method of single-exposure, single-grating quantitative phase contrast for a regime in which the grating period is much greater than the effective pixel size. A grating is used to create a high-visibility reference pattern incident on the sample, which is distorted according to the complex refractive index and thickness of the sample. The resolution, along a line parallel to the grating, is not restricted by the grating spacing, and the detector resolution becomes the primary determinant of the spatial resolution. We present a method of analysis that maps the displacement of interrogation windows in order to retrieve a quantitative phase map. Application of this analysis to the imaging of known phantoms shows excellent correspondence.

Learning to rank for blind image quality assessment.

PubMed

Gao, Fei; Tao, Dacheng; Gao, Xinbo; Li, Xuelong

2015-10-01

Blind image quality assessment (BIQA) aims to predict perceptual image quality scores without access to reference images. State-of-the-art BIQA methods typically require subjects to score a large number of images to train a robust model. However, subjective quality scores are imprecise, biased, and inconsistent, and it is challenging to obtain a large-scale database, or to extend existing databases, because of the inconvenience of collecting images, training the subjects, conducting subjective experiments, and realigning human quality evaluations. To combat these limitations, this paper explores and exploits preference image pairs (PIPs) such as the quality of image Ia is better than that of image Ib for training a robust BIQA model. The preference label, representing the relative quality of two images, is generally precise and consistent, and is not sensitive to image content, distortion type, or subject identity; such PIPs can be generated at a very low cost. The proposed BIQA method is one of learning to rank. We first formulate the problem of learning the mapping from the image features to the preference label as one of classification. In particular, we investigate the utilization of a multiple kernel learning algorithm based on group lasso to provide a solution. A simple but effective strategy to estimate perceptual image quality scores is then presented. Experiments show that the proposed BIQA method is highly effective and achieves a performance comparable with that of state-of-the-art BIQA algorithms. Moreover, the proposed method can be easily extended to new distortion categories.

A comparison of peripheral imaging technologies for bone and muscle quantification: a technical review of image acquisition

PubMed Central

Wong, A.K.O.

2016-01-01

The choice of an appropriate imaging technique to quantify bone, muscle, or muscle adiposity needs to be guided by a thorough understanding of its competitive advantages over other modalities balanced by its limitations. This review details the technical machinery and methods behind peripheral quantitative computed tomography (pQCT), high-resolution (HR)-pQCT, and magnetic resonance imaging (MRI) that drive successful depiction of bone and muscle morphometry, densitometry, and structure. It discusses a number of image acquisition settings, the challenges associated with using one versus another, and compares the risk-benefits across the different modalities. Issues related to all modalities including partial volume artifact, beam hardening, calibration, and motion assessment are also detailed. The review further provides data and images to illustrate differences between methods to better guide the reader in selecting an imaging method strategically. Overall, investigators should be cautious of the impact of imaging parameters on image signal or contrast-to-noise-ratios, and the need to report these settings in future publications. The effect of motion should be assessed on images and a decision made to exclude prior to segmentation. A more standardized approach to imaging bone and muscle on pQCT and MRI could enhance comparability across studies and could improve the quality of meta-analyses. PMID:27973379

A comparison of peripheral imaging technologies for bone and muscle quantification: a technical review of image acquisition.

PubMed

Wong, A K

2016-12-14

The choice of an appropriate imaging technique to quantify bone, muscle, or muscle adiposity needs to be guided by a thorough understanding of its competitive advantages over other modalities balanced by its limitations. This review details the technical machinery and methods behind peripheral quantitative computed tomography (pQCT), high-resolution (HR)-pQCT, and magnetic resonance imaging (MRI) that drive successful depiction of bone and muscle morphometry, densitometry, and structure. It discusses a number of image acquisition settings, the challenges associated with using one versus another, and compares the risk-benefits across the different modalities. Issues related to all modalities including partial volume artifact, beam hardening, calibration, and motion assessment are also detailed. The review further provides data and images to illustrate differences between methods to better guide the reader in selecting an imaging method strategically. Overall, investigators should be cautious of the impact of imaging parameters on image signal or contrast-to-noise-ratios, and the need to report these settings in future publications. The effect of motion should be assessed on images and a decision made to exclude prior to segmentation. A more standardized approach to imaging bone and muscle on pQCT and MRI could enhance comparability across studies and could improve the quality of meta-analyses.

A demonstration of position angle-only weak lensing shear estimators on the GREAT3 simulations

NASA Astrophysics Data System (ADS)

Whittaker, Lee; Brown, Michael L.; Battye, Richard A.

2015-12-01

We develop and apply the position angle-only shear estimator of Whittaker, Brown & Battye to realistic galaxy images. This is done by demonstrating the method on the simulations of the third GRavitational lEnsing Accuracy Testing (GREAT3) challenge, which include contributions from anisotropic point spread functions (PSFs). We measure the position angles of the galaxies using three distinct methods - the integrated light method, quadrupole moments of surface brightness, and using model-based ellipticity measurements provided by IM3SHAPE. A weighting scheme is adopted to address biases in the position angle measurements which arise in the presence of an anisotropic PSF. Biases on the shear estimates, due to measurement errors on the position angles and correlations between the measurement errors and the true position angles, are corrected for using simulated galaxy images and an iterative procedure. The properties of the simulations are estimated using the deep field images provided as part of the challenge. A method is developed to match the distributions of galaxy fluxes and half-light radii from the deep fields to the corresponding distributions in the field of interest. We recover angle-only shear estimates with a performance close to current well-established model and moments-based methods for all three angle measurement techniques. The Q-values for all three methods are found to be Q ˜ 400. The code is freely available online at http://www.jb.man.ac.uk/mbrown/angle_only_shear/.

Thin plate spline feature point matching for organ surfaces in minimally invasive surgery imaging

NASA Astrophysics Data System (ADS)

Lin, Bingxiong; Sun, Yu; Qian, Xiaoning

2013-03-01

Robust feature point matching for images with large view angle changes in Minimally Invasive Surgery (MIS) is a challenging task due to low texture and specular reflections in these images. This paper presents a new approach that can improve feature matching performance by exploiting the inherent geometric property of the organ surfaces. Recently, intensity based template image tracking using a Thin Plate Spline (TPS) model has been extended for 3D surface tracking with stereo cameras. The intensity based tracking is also used here for 3D reconstruction of internal organ surfaces. To overcome the small displacement requirement of intensity based tracking, feature point correspondences are used for proper initialization of the nonlinear optimization in the intensity based method. Second, we generate simulated images from the reconstructed 3D surfaces under all potential view positions and orientations, and then extract feature points from these simulated images. The obtained feature points are then filtered and re-projected to the common reference image. The descriptors of the feature points under different view angles are stored to ensure that the proposed method can tolerate a large range of view angles. We evaluate the proposed method with silicon phantoms and in vivo images. The experimental results show that our method is much more robust with respect to the view angle changes than other state-of-the-art methods.

Imaging in focus: Imaging the dynamics of endocytosis.

PubMed

Rosendale, Morgane; Perrais, David

2017-12-01

Endocytosis, the formation of membrane vesicles from the plasma membrane, is an essential feature of eukaryotic cell biology. Intense research effort has been dedicated to developing methods that can detect endocytosis events with the highest resolution. We have classified these methods into four families. They exploit the physical properties of endocytosis, namely: 1. Distinguishing extracellular from internalised cargo in fixed samples, 2. Monitoring endosomal acidification, 3. Measuring the turnover of endocytic zones and 4. Detecting vesicle scission. The last three families, all based on fluorescence imaging, are used to study endocytosis in living cells. We discuss the advantages and limitations of these methods and conclude on the future developments required to tackle the upcoming challenges in this fundamental field of cell biology. Copyright © 2017. Published by Elsevier Ltd.

Live visualization of genomic loci with BiFC-TALE

PubMed Central

Hu, Huan; Zhang, Hongmin; Wang, Sheng; Ding, Miao; An, Hui; Hou, Yingping; Yang, Xiaojing; Wei, Wensheng; Sun, Yujie; Tang, Chao

2017-01-01

Tracking the dynamics of genomic loci is important for understanding the mechanisms of fundamental intracellular processes. However, fluorescent labeling and imaging of such loci in live cells have been challenging. One of the major reasons is the low signal-to-background ratio (SBR) of images mainly caused by the background fluorescence from diffuse full-length fluorescent proteins (FPs) in the living nucleus, hampering the application of live cell genomic labeling methods. Here, combining bimolecular fluorescence complementation (BiFC) and transcription activator-like effector (TALE) technologies, we developed a novel method for labeling genomic loci (BiFC-TALE), which largely reduces the background fluorescence level. Using BiFC-TALE, we demonstrated a significantly improved SBR by imaging telomeres and centromeres in living cells in comparison with the methods using full-length FP. PMID:28074901

Live visualization of genomic loci with BiFC-TALE.

PubMed

Hu, Huan; Zhang, Hongmin; Wang, Sheng; Ding, Miao; An, Hui; Hou, Yingping; Yang, Xiaojing; Wei, Wensheng; Sun, Yujie; Tang, Chao

2017-01-11

Tracking the dynamics of genomic loci is important for understanding the mechanisms of fundamental intracellular processes. However, fluorescent labeling and imaging of such loci in live cells have been challenging. One of the major reasons is the low signal-to-background ratio (SBR) of images mainly caused by the background fluorescence from diffuse full-length fluorescent proteins (FPs) in the living nucleus, hampering the application of live cell genomic labeling methods. Here, combining bimolecular fluorescence complementation (BiFC) and transcription activator-like effector (TALE) technologies, we developed a novel method for labeling genomic loci (BiFC-TALE), which largely reduces the background fluorescence level. Using BiFC-TALE, we demonstrated a significantly improved SBR by imaging telomeres and centromeres in living cells in comparison with the methods using full-length FP.

Filtering of high noise breast thermal images using fast non-local means.

PubMed

Suganthi, S S; Ramakrishnan, S

2014-01-01

Analyses of breast thermograms are still a challenging task primarily due to the limitations such as low contrast, low signal to noise ratio and absence of clear edges. Therefore, always there is a requirement for preprocessing techniques before performing any quantitative analysis. In this work, a noise removal framework using fast non-local means algorithm, method noise and median filter was used to denoise breast thermograms. The images considered were subjected to Anscombe transformation to convert the distribution from Poisson to Gaussian. The pre-denoised image was obtained by subjecting the transformed image to fast non-local means filtering. The method noise which is the difference between the original and pre-denoised image was observed with the noise component merged in few structures and fine detail of the image. The image details presented in the method noise was extracted by smoothing the noise part using the median filter. The retrieved image part was added to the pre-denoised image to obtain the final denoised image. The performance of this technique was compared with that of Wiener and SUSAN filters. The results show that all the filters considered are able to remove the noise component. The performance of the proposed denoising framework is found to be good in preserving detail and removing noise. Further, the method noise is observed with negligible image details. Similarly, denoised image with no noise and smoothed edges are observed using Wiener filter and its method noise is contained with few structures and image details. The performance results of SUSAN filter is found to be blurred denoised image with little noise and also method noise with extensive structure and image details. Hence, it appears that the proposed denoising framework is able to preserve the edge information and generate clear image that could help in enhancing the diagnostic relevance of breast thermograms. In this paper, the introduction, objectives, materials and methods, results and discussion and conclusions are presented in detail.

MIMoSA: An Automated Method for Intermodal Segmentation Analysis of Multiple Sclerosis Brain Lesions.

PubMed

Valcarcel, Alessandra M; Linn, Kristin A; Vandekar, Simon N; Satterthwaite, Theodore D; Muschelli, John; Calabresi, Peter A; Pham, Dzung L; Martin, Melissa Lynne; Shinohara, Russell T

2018-03-08

Magnetic resonance imaging (MRI) is crucial for in vivo detection and characterization of white matter lesions (WMLs) in multiple sclerosis. While WMLs have been studied for over two decades using MRI, automated segmentation remains challenging. Although the majority of statistical techniques for the automated segmentation of WMLs are based on single imaging modalities, recent advances have used multimodal techniques for identifying WMLs. Complementary modalities emphasize different tissue properties, which help identify interrelated features of lesions. Method for Inter-Modal Segmentation Analysis (MIMoSA), a fully automatic lesion segmentation algorithm that utilizes novel covariance features from intermodal coupling regression in addition to mean structure to model the probability lesion is contained in each voxel, is proposed. MIMoSA was validated by comparison with both expert manual and other automated segmentation methods in two datasets. The first included 98 subjects imaged at Johns Hopkins Hospital in which bootstrap cross-validation was used to compare the performance of MIMoSA against OASIS and LesionTOADS, two popular automatic segmentation approaches. For a secondary validation, a publicly available data from a segmentation challenge were used for performance benchmarking. In the Johns Hopkins study, MIMoSA yielded average Sørensen-Dice coefficient (DSC) of .57 and partial AUC of .68 calculated with false positive rates up to 1%. This was superior to performance using OASIS and LesionTOADS. The proposed method also performed competitively in the segmentation challenge dataset. MIMoSA resulted in statistically significant improvements in lesion segmentation performance compared with LesionTOADS and OASIS, and performed competitively in an additional validation study. Copyright © 2018 by the American Society of Neuroimaging.

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.

Edge-illumination x-ray phase contrast imaging with Pt-based metallic glass masks

NASA Astrophysics Data System (ADS)

Saghamanesh, Somayeh; Aghamiri, Seyed Mahmoud-Reza; Olivo, Alessandro; Sadeghilarijani, Maryam; Kato, Hidemi; Kamali-Asl, Alireza; Yashiro, Wataru

2017-06-01

Edge-illumination x-ray phase contrast imaging (EI XPCI) is a non-interferometric phase-sensitive method where two absorption masks are employed. These masks are fabricated through a photolithography process followed by electroplating which is challenging in terms of yield as well as time- and cost-effectiveness. We report on the first implementation of EI XPCI with Pt-based metallic glass masks fabricated by an imprinting method. The new tested alloy exhibits good characteristics including high workability beside high x-ray attenuation. The fabrication process is easy and cheap, and can produce large-size masks for high x-ray energies within minutes. Imaging experiments show a good quality phase image, which confirms the potential of these masks to make the EI XPCI technique widely available and affordable.

Time-of-flight depth image enhancement using variable integration time

NASA Astrophysics Data System (ADS)

Kim, Sun Kwon; Choi, Ouk; Kang, Byongmin; Kim, James Dokyoon; Kim, Chang-Yeong

2013-03-01

Time-of-Flight (ToF) cameras are used for a variety of applications because it delivers depth information at a high frame rate. These cameras, however, suffer from challenging problems such as noise and motion artifacts. To increase signal-to-noise ratio (SNR), the camera should calculate a distance based on a large amount of infra-red light, which needs to be integrated over a long time. On the other hand, the integration time should be short enough to suppress motion artifacts. We propose a ToF depth imaging method to combine advantages of short and long integration times exploiting an imaging fusion scheme proposed for color imaging. To calibrate depth differences due to the change of integration times, a depth transfer function is estimated by analyzing the joint histogram of depths in the two images of different integration times. The depth images are then transformed into wavelet domains and fused into a depth image with suppressed noise and low motion artifacts. To evaluate the proposed method, we captured a moving bar of a metronome with different integration times. The experiment shows the proposed method could effectively remove the motion artifacts while preserving high SNR comparable to the depth images acquired during long integration time.

Intervertebral disc detection in X-ray images using faster R-CNN.

PubMed

Ruhan Sa; Owens, William; Wiegand, Raymond; Studin, Mark; Capoferri, Donald; Barooha, Kenneth; Greaux, Alexander; Rattray, Robert; Hutton, Adam; Cintineo, John; Chaudhary, Vipin

2017-07-01

Automatic identification of specific osseous landmarks on the spinal radiograph can be used to automate calculations for correcting ligament instability and injury, which affect 75% of patients injured in motor vehicle accidents. In this work, we propose to use deep learning based object detection method as the first step towards identifying landmark points in lateral lumbar X-ray images. The significant breakthrough of deep learning technology has made it a prevailing choice for perception based applications, however, the lack of large annotated training dataset has brought challenges to utilizing the technology in medical image processing field. In this work, we propose to fine tune a deep network, Faster-RCNN, a state-of-the-art deep detection network in natural image domain, using small annotated clinical datasets. In the experiment we show that, by using only 81 lateral lumbar X-Ray training images, one can achieve much better performance compared to traditional sliding window detection method on hand crafted features. Furthermore, we fine-tuned the network using 974 training images and tested on 108 images, which achieved average precision of 0.905 with average computation time of 3 second per image, which greatly outperformed traditional methods in terms of accuracy and efficiency.

Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing.

PubMed

Rahim, Sarni Suhaila; Palade, Vasile; Shuttleworth, James; Jayne, Chrisina

2016-12-01

Digital retinal imaging is a challenging screening method for which effective, robust and cost-effective approaches are still to be developed. Regular screening for diabetic retinopathy and diabetic maculopathy diseases is necessary in order to identify the group at risk of visual impairment. This paper presents a novel automatic detection of diabetic retinopathy and maculopathy in eye fundus images by employing fuzzy image processing techniques. The paper first introduces the existing systems for diabetic retinopathy screening, with an emphasis on the maculopathy detection methods. The proposed medical decision support system consists of four parts, namely: image acquisition, image preprocessing including four retinal structures localisation, feature extraction and the classification of diabetic retinopathy and maculopathy. A combination of fuzzy image processing techniques, the Circular Hough Transform and several feature extraction methods are implemented in the proposed system. The paper also presents a novel technique for the macula region localisation in order to detect the maculopathy. In addition to the proposed detection system, the paper highlights a novel online dataset and it presents the dataset collection, the expert diagnosis process and the advantages of our online database compared to other public eye fundus image databases for diabetic retinopathy purposes.

Methods for 2-D and 3-D Endobronchial Ultrasound Image Segmentation.

PubMed

Zang, Xiaonan; Bascom, Rebecca; Gilbert, Christopher; Toth, Jennifer; Higgins, William

2016-07-01

Endobronchial ultrasound (EBUS) is now commonly used for cancer-staging bronchoscopy. Unfortunately, EBUS is challenging to use and interpreting EBUS video sequences is difficult. Other ultrasound imaging domains, hampered by related difficulties, have benefited from computer-based image-segmentation methods. Yet, so far, no such methods have been proposed for EBUS. We propose image-segmentation methods for 2-D EBUS frames and 3-D EBUS sequences. Our 2-D method adapts the fast-marching level-set process, anisotropic diffusion, and region growing to the problem of segmenting 2-D EBUS frames. Our 3-D method builds upon the 2-D method while also incorporating the geodesic level-set process for segmenting EBUS sequences. Tests with lung-cancer patient data showed that the methods ran fully automatically for nearly 80% of test cases. For the remaining cases, the only user-interaction required was the selection of a seed point. When compared to ground-truth segmentations, the 2-D method achieved an overall Dice index = 90.0% ±4.9%, while the 3-D method achieved an overall Dice index = 83.9 ± 6.0%. In addition, the computation time (2-D, 0.070 s/frame; 3-D, 0.088 s/frame) was two orders of magnitude faster than interactive contour definition. Finally, we demonstrate the potential of the methods for EBUS localization in a multimodal image-guided bronchoscopy system.

Towards precision medicine: from quantitative imaging to radiomics

PubMed Central

Acharya, U. Rajendra; Hagiwara, Yuki; Sudarshan, Vidya K.; Chan, Wai Yee; Ng, Kwan Hoong

2018-01-01

Radiology (imaging) and imaging-guided interventions, which provide multi-parametric morphologic and functional information, are playing an increasingly significant role in precision medicine. Radiologists are trained to understand the imaging phenotypes, transcribe those observations (phenotypes) to correlate with underlying diseases and to characterize the images. However, in order to understand and characterize the molecular phenotype (to obtain genomic information) of solid heterogeneous tumours, the advanced sequencing of those tissues using biopsy is required. Thus, radiologists image the tissues from various views and angles in order to have the complete image phenotypes, thereby acquiring a huge amount of data. Deriving meaningful details from all these radiological data becomes challenging and raises the big data issues. Therefore, interest in the application of radiomics has been growing in recent years as it has the potential to provide significant interpretive and predictive information for decision support. Radiomics is a combination of conventional computer-aided diagnosis, deep learning methods, and human skills, and thus can be used for quantitative characterization of tumour phenotypes. This paper discusses the overview of radiomics workflow, the results of various radiomics-based studies conducted using various radiological images such as computed tomography (CT), magnetic resonance imaging (MRI), and positron-emission tomography (PET), the challenges we are facing, and the potential contribution of radiomics towards precision medicine. PMID:29308604

3D optic disc reconstruction via a global fundus stereo algorithm.

PubMed

Bansal, M; Sizintsev, M; Eledath, J; Sawhney, H; Pearson, D J; Stone, R A

2013-01-01

This paper presents a novel method to recover 3D structure of the optic disc in the retina from two uncalibrated fundus images. Retinal images are commonly uncalibrated when acquired clinically, creating rectification challenges as well as significant radiometric and blur differences within the stereo pair. By exploiting structural peculiarities of the retina, we modified the Graph Cuts computational stereo method (one of current state-of-the-art methods) to yield a high quality algorithm for fundus stereo reconstruction. Extensive qualitative and quantitative experimental evaluation (where OCT scans are used as 3D ground truth) on our and publicly available datasets shows the superiority of the proposed method in comparison to other alternatives.

Multi-scale Morphological Image Enhancement of Chest Radiographs by a Hybrid Scheme.

PubMed

Alavijeh, Fatemeh Shahsavari; Mahdavi-Nasab, Homayoun

2015-01-01

Chest radiography is a common diagnostic imaging test, which contains an enormous amount of information about a patient. However, its interpretation is highly challenging. The accuracy of the diagnostic process is greatly influenced by image processing algorithms; hence enhancement of the images is indispensable in order to improve visibility of the details. This paper aims at improving radiograph parameters such as contrast, sharpness, noise level, and brightness to enhance chest radiographs, making use of a triangulation method. Here, contrast limited adaptive histogram equalization technique and noise suppression are simultaneously performed in wavelet domain in a new scheme, followed by morphological top-hat and bottom-hat filtering. A unique implementation of morphological filters allows for adjustment of the image brightness and significant enhancement of the contrast. The proposed method is tested on chest radiographs from Japanese Society of Radiological Technology database. The results are compared with conventional enhancement techniques such as histogram equalization, contrast limited adaptive histogram equalization, Retinex, and some recently proposed methods to show its strengths. The experimental results reveal that the proposed method can remarkably improve the image contrast while keeping the sensitive chest tissue information so that radiologists might have a more precise interpretation.

Multi-scale Morphological Image Enhancement of Chest Radiographs by a Hybrid Scheme

PubMed Central

Alavijeh, Fatemeh Shahsavari; Mahdavi-Nasab, Homayoun

2015-01-01

Chest radiography is a common diagnostic imaging test, which contains an enormous amount of information about a patient. However, its interpretation is highly challenging. The accuracy of the diagnostic process is greatly influenced by image processing algorithms; hence enhancement of the images is indispensable in order to improve visibility of the details. This paper aims at improving radiograph parameters such as contrast, sharpness, noise level, and brightness to enhance chest radiographs, making use of a triangulation method. Here, contrast limited adaptive histogram equalization technique and noise suppression are simultaneously performed in wavelet domain in a new scheme, followed by morphological top-hat and bottom-hat filtering. A unique implementation of morphological filters allows for adjustment of the image brightness and significant enhancement of the contrast. The proposed method is tested on chest radiographs from Japanese Society of Radiological Technology database. The results are compared with conventional enhancement techniques such as histogram equalization, contrast limited adaptive histogram equalization, Retinex, and some recently proposed methods to show its strengths. The experimental results reveal that the proposed method can remarkably improve the image contrast while keeping the sensitive chest tissue information so that radiologists might have a more precise interpretation. PMID:25709942

Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images

NASA Astrophysics Data System (ADS)

Alshehhi, Rasha; Marpu, Prashanth Reddy

2017-04-01

Extraction of road networks in urban areas from remotely sensed imagery plays an important role in many urban applications (e.g. road navigation, geometric correction of urban remote sensing images, updating geographic information systems, etc.). It is normally difficult to accurately differentiate road from its background due to the complex geometry of the buildings and the acquisition geometry of the sensor. In this paper, we present a new method for extracting roads from high-resolution imagery based on hierarchical graph-based image segmentation. The proposed method consists of: 1. Extracting features (e.g., using Gabor and morphological filtering) to enhance the contrast between road and non-road pixels, 2. Graph-based segmentation consisting of (i) Constructing a graph representation of the image based on initial segmentation and (ii) Hierarchical merging and splitting of image segments based on color and shape features, and 3. Post-processing to remove irregularities in the extracted road segments. Experiments are conducted on three challenging datasets of high-resolution images to demonstrate the proposed method and compare with other similar approaches. The results demonstrate the validity and superior performance of the proposed method for road extraction in urban areas.

Underwater Inherent Optical Properties Estimation Using a Depth Aided Deep Neural Network.

PubMed

Yu, Zhibin; Wang, Yubo; Zheng, Bing; Zheng, Haiyong; Wang, Nan; Gu, Zhaorui

2017-01-01

Underwater inherent optical properties (IOPs) are the fundamental clues to many research fields such as marine optics, marine biology, and underwater vision. Currently, beam transmissometers and optical sensors are considered as the ideal IOPs measuring methods. But these methods are inflexible and expensive to be deployed. To overcome this problem, we aim to develop a novel measuring method using only a single underwater image with the help of deep artificial neural network. The power of artificial neural network has been proved in image processing and computer vision fields with deep learning technology. However, image-based IOPs estimation is a quite different and challenging task. Unlike the traditional applications such as image classification or localization, IOP estimation looks at the transparency of the water between the camera and the target objects to estimate multiple optical properties simultaneously. In this paper, we propose a novel Depth Aided (DA) deep neural network structure for IOPs estimation based on a single RGB image that is even noisy. The imaging depth information is considered as an aided input to help our model make better decision.

Four dimensional hybrid ultrasound and optoacoustic imaging via passive element optical excitation in a hand-held probe

NASA Astrophysics Data System (ADS)

Fehm, Thomas Felix; Deán-Ben, Xosé Luís; Razansky, Daniel

2014-10-01

Ultrasonography and optoacoustic imaging share powerful advantages related to the natural aptitude for real-time image rendering with high resolution, the hand-held operation, and lack of ionizing radiation. The two methods also possess very different yet highly complementary advantages of the mechanical and optical contrast in living tissues. Nonetheless, efficient integration of these modalities remains challenging owing to the fundamental differences in the underlying physical contrast, optimal signal acquisition, and image reconstruction approaches. We report on a method for hybrid acquisition and reconstruction of three-dimensional pulse-echo ultrasound and optoacoustic images in real time based on passive ultrasound generation with an optical absorber, thus avoiding the hardware complexity of active ultrasound generation. In this way, complete hybrid datasets are generated with a single laser interrogation pulse, resulting in simultaneous rendering of ultrasound and optoacoustic images at an unprecedented rate of 10 volumetric frames per second. Performance is subsequently showcased in phantom experiments and in-vivo measurements from a healthy human volunteer, confirming general clinical applicability of the method.

Breast Histopathological Image Retrieval Based on Latent Dirichlet Allocation.

PubMed

Ma, Yibing; Jiang, Zhiguo; Zhang, Haopeng; Xie, Fengying; Zheng, Yushan; Shi, Huaqiang; Zhao, Yu

2017-07-01

In the field of pathology, whole slide image (WSI) has become the major carrier of visual and diagnostic information. Content-based image retrieval among WSIs can aid the diagnosis of an unknown pathological image by finding its similar regions in WSIs with diagnostic information. However, the huge size and complex content of WSI pose several challenges for retrieval. In this paper, we propose an unsupervised, accurate, and fast retrieval method for a breast histopathological image. Specifically, the method presents a local statistical feature of nuclei for morphology and distribution of nuclei, and employs the Gabor feature to describe the texture information. The latent Dirichlet allocation model is utilized for high-level semantic mining. Locality-sensitive hashing is used to speed up the search. Experiments on a WSI database with more than 8000 images from 15 types of breast histopathology demonstrate that our method achieves about 0.9 retrieval precision as well as promising efficiency. Based on the proposed framework, we are developing a search engine for an online digital slide browsing and retrieval platform, which can be applied in computer-aided diagnosis, pathology education, and WSI archiving and management.

Four dimensional hybrid ultrasound and optoacoustic imaging via passive element optical excitation in a hand-held probe

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

Fehm, Thomas Felix; Razansky, Daniel, E-mail: dr@tum.de; Faculty of Medicine, Technische Universität München, Munich

2014-10-27

Ultrasonography and optoacoustic imaging share powerful advantages related to the natural aptitude for real-time image rendering with high resolution, the hand-held operation, and lack of ionizing radiation. The two methods also possess very different yet highly complementary advantages of the mechanical and optical contrast in living tissues. Nonetheless, efficient integration of these modalities remains challenging owing to the fundamental differences in the underlying physical contrast, optimal signal acquisition, and image reconstruction approaches. We report on a method for hybrid acquisition and reconstruction of three-dimensional pulse-echo ultrasound and optoacoustic images in real time based on passive ultrasound generation with an opticalmore » absorber, thus avoiding the hardware complexity of active ultrasound generation. In this way, complete hybrid datasets are generated with a single laser interrogation pulse, resulting in simultaneous rendering of ultrasound and optoacoustic images at an unprecedented rate of 10 volumetric frames per second. Performance is subsequently showcased in phantom experiments and in-vivo measurements from a healthy human volunteer, confirming general clinical applicability of the method.« less

On the importance of mathematical methods for analysis of MALDI-imaging mass spectrometry data.

PubMed

Trede, Dennis; Kobarg, Jan Hendrik; Oetjen, Janina; Thiele, Herbert; Maass, Peter; Alexandrov, Theodore

2012-03-21

In the last decade, matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS), also called as MALDI-imaging, has proven its potential in proteomics and was successfully applied to various types of biomedical problems, in particular to histopathological label-free analysis of tissue sections. In histopathology, MALDI-imaging is used as a general analytic tool revealing the functional proteomic structure of tissue sections, and as a discovery tool for detecting new biomarkers discriminating a region annotated by an experienced histologist, in particular, for cancer studies. A typical MALDI-imaging data set contains 10⁸ to 10⁹ intensity values occupying more than 1 GB. Analysis and interpretation of such huge amount of data is a mathematically, statistically and computationally challenging problem. In this paper we overview some computational methods for analysis of MALDI-imaging data sets. We discuss the importance of data preprocessing, which typically includes normalization, baseline removal and peak picking, and hightlight the importance of image denoising when visualizing IMS data.

On the Importance of Mathematical Methods for Analysis of MALDI-Imaging Mass Spectrometry Data.

PubMed

Trede, Dennis; Kobarg, Jan Hendrik; Oetjen, Janina; Thiele, Herbert; Maass, Peter; Alexandrov, Theodore

2012-03-01

In the last decade, matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS), also called as MALDI-imaging, has proven its potential in proteomics and was successfully applied to various types of biomedical problems, in particular to histopathological label-free analysis of tissue sections. In histopathology, MALDI-imaging is used as a general analytic tool revealing the functional proteomic structure of tissue sections, and as a discovery tool for detecting new biomarkers discriminating a region annotated by an experienced histologist, in particular, for cancer studies. A typical MALDI-imaging data set contains 108 to 109 intensity values occupying more than 1 GB. Analysis and interpretation of such huge amount of data is a mathematically, statistically and computationally challenging problem. In this paper we overview some computational methods for analysis of MALDI-imaging data sets. We discuss the importance of data preprocessing, which typically includes normalization, baseline removal and peak picking, and hightlight the importance of image denoising when visualizing IMS data.

Three-dimensional multispectral hand-held optoacoustic imaging with microsecond-level delayed laser pulses

NASA Astrophysics Data System (ADS)

Deán-Ben, X. L.; Bay, Erwin; Razansky, Daniel

2015-03-01

Three-dimensional hand-held optoacoustic imaging comes with important advantages that prompt the clinical translation of this modality, with applications envisioned in cardiovascular and peripheral vascular disease, disorders of the lymphatic system, breast cancer, arthritis or inflammation. Of particular importance is the multispectral acquisition of data by exciting the tissue at several wavelengths, which enables functional imaging applications. However, multispectral imaging of entire three-dimensional regions is significantly challenged by motion artefacts in concurrent acquisitions at different wavelengths. A method based on acquisition of volumetric datasets having a microsecond-level delay between pulses at different wavelengths is described in this work. This method can avoid image artefacts imposed by a scanning velocity greater than 2 m/s, thus, does not only facilitate imaging influenced by respiratory, cardiac or other intrinsic fast movements in living tissues, but can achieve artifact-free imaging in the presence of more significant motion, e.g., abrupt displacements during handheld-mode operation in a clinical environment.

Robust digital image inpainting algorithm in the wireless environment

NASA Astrophysics Data System (ADS)

Karapetyan, G.; Sarukhanyan, H. G.; Agaian, S. S.

2014-05-01

Image or video inpainting is the process/art of retrieving missing portions of an image without introducing undesirable artifacts that are undetectable by an ordinary observer. An image/video can be damaged due to a variety of factors, such as deterioration due to scratches, laser dazzling effects, wear and tear, dust spots, loss of data when transmitted through a channel, etc. Applications of inpainting include image restoration (removing laser dazzling effects, dust spots, date, text, time, etc.), image synthesis (texture synthesis), completing panoramas, image coding, wireless transmission (recovery of the missing blocks), digital culture protection, image de-noising, fingerprint recognition, and film special effects and production. Most inpainting methods can be classified in two key groups: global and local methods. Global methods are used for generating large image regions from samples while local methods are used for filling in small image gaps. Each method has its own advantages and limitations. For example, the global inpainting methods perform well on textured image retrieval, whereas the classical local methods perform poorly. In addition, some of the techniques are computationally intensive; exceeding the capabilities of most currently used mobile devices. In general, the inpainting algorithms are not suitable for the wireless environment. This paper presents a new and efficient scheme that combines the advantages of both local and global methods into a single algorithm. Particularly, it introduces a blind inpainting model to solve the above problems by adaptively selecting support area for the inpainting scheme. The proposed method is applied to various challenging image restoration tasks, including recovering old photos, recovering missing data on real and synthetic images, and recovering the specular reflections in endoscopic images. A number of computer simulations demonstrate the effectiveness of our scheme and also illustrate the main properties and implementation steps of the presented algorithm. Furthermore, the simulation results show that the presented method is among the state-of-the-art and compares favorably against many available methods in the wireless environment. Robustness in the wireless environment with respect to the shape of the manually selected "marked" region is also illustrated. Currently, we are working on the expansion of this work to video and 3-D data.

DCS-SVM: a novel semi-automated method for human brain MR image segmentation.

PubMed

Ahmadvand, Ali; Daliri, Mohammad Reza; Hajiali, Mohammadtaghi

2017-11-27

In this paper, a novel method is proposed which appropriately segments magnetic resonance (MR) brain images into three main tissues. This paper proposes an extension of our previous work in which we suggested a combination of multiple classifiers (CMC)-based methods named dynamic classifier selection-dynamic local training local Tanimoto index (DCS-DLTLTI) for MR brain image segmentation into three main cerebral tissues. This idea is used here and a novel method is developed that tries to use more complex and accurate classifiers like support vector machine (SVM) in the ensemble. This work is challenging because the CMC-based methods are time consuming, especially on huge datasets like three-dimensional (3D) brain MR images. Moreover, SVM is a powerful method that is used for modeling datasets with complex feature space, but it also has huge computational cost for big datasets, especially those with strong interclass variability problems and with more than two classes such as 3D brain images; therefore, we cannot use SVM in DCS-DLTLTI. Therefore, we propose a novel approach named "DCS-SVM" to use SVM in DCS-DLTLTI to improve the accuracy of segmentation results. The proposed method is applied on well-known datasets of the Internet Brain Segmentation Repository (IBSR) and promising results are obtained.

WE-EF-BRD-04: MR in the OR: The Growth and Applications of MRI for Interventional Radiology and Surgery

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

Fahrig, R.

MRI-guided treatment is a growing area of medicine, particularly in radiotherapy and surgery. The exquisite soft tissue anatomic contrast offered by MRI, along with functional imaging, makes the use of MRI during therapeutic procedures very attractive. Challenging the utility of MRI in the therapy room are many issues including the physics of MRI and the impact on the environment and therapeutic instruments, the impact of the room and instruments on the MRI; safety, space, design and cost. In this session, the applications and challenges of MRI-guided treatment will be described. The session format is: Past, present and future: MRI-guided radiotherapymore » from 2005 to 2025: Jan Lagendijk Battling Maxwell’s equations: Physics challenges and solutions for hybrid MRI systems: Paul Keall I want it now!: Advances in MRI acquisition, reconstruction and the use of priors to enable fast anatomic and physiologic imaging to inform guidance and adaptation decisions: Yanle Hu MR in the OR: The growth and applications of MRI for interventional radiology and surgery: Rebecca Fahrig Learning Objectives: To understand the history and trajectory of MRI-guided radiotherapy To understand the challenges of integrating MR imaging systems with linear accelerators To understand the latest in fast MRI methods to enable the visualisation of anatomy and physiology on radiotherapy treatment timescales To understand the growing role and challenges of MRI for image-guided surgical procedures My disclosures are publicly available and updated at: http://sydney.edu.au/medicine/radiation-physics/about-us/disclosures.php.« less

WE-EF-BRD-01: Past, Present and Future: MRI-Guided Radiotherapy From 2005 to 2025

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

Lagendijk, J.

MRI-guided treatment is a growing area of medicine, particularly in radiotherapy and surgery. The exquisite soft tissue anatomic contrast offered by MRI, along with functional imaging, makes the use of MRI during therapeutic procedures very attractive. Challenging the utility of MRI in the therapy room are many issues including the physics of MRI and the impact on the environment and therapeutic instruments, the impact of the room and instruments on the MRI; safety, space, design and cost. In this session, the applications and challenges of MRI-guided treatment will be described. The session format is: Past, present and future: MRI-guided radiotherapymore » from 2005 to 2025: Jan Lagendijk Battling Maxwell’s equations: Physics challenges and solutions for hybrid MRI systems: Paul Keall I want it now!: Advances in MRI acquisition, reconstruction and the use of priors to enable fast anatomic and physiologic imaging to inform guidance and adaptation decisions: Yanle Hu MR in the OR: The growth and applications of MRI for interventional radiology and surgery: Rebecca Fahrig Learning Objectives: To understand the history and trajectory of MRI-guided radiotherapy To understand the challenges of integrating MR imaging systems with linear accelerators To understand the latest in fast MRI methods to enable the visualisation of anatomy and physiology on radiotherapy treatment timescales To understand the growing role and challenges of MRI for image-guided surgical procedures My disclosures are publicly available and updated at: http://sydney.edu.au/medicine/radiation-physics/about-us/disclosures.php.« less

Novel Multistatic Adaptive Microwave Imaging Methods for Early Breast Cancer Detection

NASA Astrophysics Data System (ADS)

Xie, Yao; Guo, Bin; Li, Jian; Stoica, Petre

2006-12-01

Multistatic adaptive microwave imaging (MAMI) methods are presented and compared for early breast cancer detection. Due to the significant contrast between the dielectric properties of normal and malignant breast tissues, developing microwave imaging techniques for early breast cancer detection has attracted much interest lately. MAMI is one of the microwave imaging modalities and employs multiple antennas that take turns to transmit ultra-wideband (UWB) pulses while all antennas are used to receive the reflected signals. MAMI can be considered as a special case of the multi-input multi-output (MIMO) radar with the multiple transmitted waveforms being either UWB pulses or zeros. Since the UWB pulses transmitted by different antennas are displaced in time, the multiple transmitted waveforms are orthogonal to each other. The challenge to microwave imaging is to improve resolution and suppress strong interferences caused by the breast skin, nipple, and so forth. The MAMI methods we investigate herein utilize the data-adaptive robust Capon beamformer (RCB) to achieve high resolution and interference suppression. We will demonstrate the effectiveness of our proposed methods for breast cancer detection via numerical examples with data simulated using the finite-difference time-domain method based on a 3D realistic breast model.

A Unified Framework for Brain Segmentation in MR Images

PubMed Central

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

2015-01-01

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

A Coarse-to-Fine Geometric Scale-Invariant Feature Transform for Large Size High Resolution Satellite Image Registration

PubMed Central

Chang, Xueli; Du, Siliang; Li, Yingying; Fang, Shenghui

2018-01-01

Large size high resolution (HR) satellite image matching is a challenging task due to local distortion, repetitive structures, intensity changes and low efficiency. In this paper, a novel matching approach is proposed for the large size HR satellite image registration, which is based on coarse-to-fine strategy and geometric scale-invariant feature transform (SIFT). In the coarse matching step, a robust matching method scale restrict (SR) SIFT is implemented at low resolution level. The matching results provide geometric constraints which are then used to guide block division and geometric SIFT in the fine matching step. The block matching method can overcome the memory problem. In geometric SIFT, with area constraints, it is beneficial for validating the candidate matches and decreasing searching complexity. To further improve the matching efficiency, the proposed matching method is parallelized using OpenMP. Finally, the sensing image is rectified to the coordinate of reference image via Triangulated Irregular Network (TIN) transformation. Experiments are designed to test the performance of the proposed matching method. The experimental results show that the proposed method can decrease the matching time and increase the number of matching points while maintaining high registration accuracy. PMID:29702589

Dark-field microscopic image stitching method for surface defects evaluation of large fine optics.

PubMed

Liu, Dong; Wang, Shitong; Cao, Pin; Li, Lu; Cheng, Zhongtao; Gao, Xin; Yang, Yongying

2013-03-11

One of the challenges in surface defects evaluation of large fine optics is to detect defects of microns on surfaces of tens or hundreds of millimeters. Sub-aperture scanning and stitching is considered to be a practical and efficient method. But since there are usually few defects on the large aperture fine optics, resulting in no defects or only one run-through line feature in many sub-aperture images, traditional stitching methods encounter with mismatch problem. In this paper, a feature-based multi-cycle image stitching algorithm is proposed to solve the problem. The overlapping areas of sub-apertures are categorized based on the features they contain. Different types of overlapping areas are then stitched in different cycles with different methods. The stitching trace is changed to follow the one that determined by the features. The whole stitching procedure is a region-growing like process. Sub-aperture blocks grow bigger after each cycle and finally the full aperture image is obtained. Comparison experiment shows that the proposed method is very suitable to stitch sub-apertures that very few feature information exists in the overlapping areas and can stitch the dark-field microscopic sub-aperture images very well.

Configuration of automatic exposure control on mammography units for computed radiography to match patient dose of screen film systems

NASA Astrophysics Data System (ADS)

Yang, Chang-Ying Joseph; Huang, Weidong

2009-02-01

Computed radiography (CR) is considered a drop-in addition or replacement for traditional screen-film (SF) systems in digital mammography. Unlike other technologies, CR has the advantage of being compatible with existing mammography units. One of the challenges, however, is to properly configure the automatic exposure control (AEC) on existing mammography units for CR use. Unlike analogue systems, the capture and display of digital CR images is decoupled. The function of AEC is changed from ensuring proper and consistent optical density of the captured image on film to balancing image quality with patient dose needed for CR. One of the preferences when acquiring CR images under AEC is to use the same patient dose as SF systems. The challenge is whether the existing AEC design and calibration process-most of them proprietary from the X-ray systems manufacturers and tailored specifically for SF response properties-can be adapted for CR cassettes, in order to compensate for their response and attenuation differences. This paper describes the methods for configuring the AEC of three different mammography units models to match the patient dose used for CR with those that are used for a KODAK MIN-R 2000 SF System. Based on phantom test results, these methods provide the dose level under AEC for the CR systems to match with the dose of SF systems. These methods can be used in clinical environments that require the acquisition of CR images under AEC at the same dose levels as those used for SF systems.

A Method Based on Wavelet Transforms for Source Detection in Photon-counting Detector Images. II. Application to ROSAT PSPC Images

NASA Astrophysics Data System (ADS)

Damiani, F.; Maggio, A.; Micela, G.; Sciortino, S.

1997-07-01

We apply to the specific case of images taken with the ROSAT PSPC detector our wavelet-based X-ray source detection algorithm presented in a companion paper. Such images are characterized by the presence of detector ``ribs,'' strongly varying point-spread function, and vignetting, so that their analysis provides a challenge for any detection algorithm. First, we apply the algorithm to simulated images of a flat background, as seen with the PSPC, in order to calibrate the number of spurious detections as a function of significance threshold and to ascertain that the spatial distribution of spurious detections is uniform, i.e., unaffected by the ribs; this goal was achieved using the exposure map in the detection procedure. Then, we analyze simulations of PSPC images with a realistic number of point sources; the results are used to determine the efficiency of source detection and the accuracy of output quantities such as source count rate, size, and position, upon a comparison with input source data. It turns out that sources with 10 photons or less may be confidently detected near the image center in medium-length (~104 s), background-limited PSPC exposures. The positions of sources detected near the image center (off-axis angles < 15') are accurate to within a few arcseconds. Output count rates and sizes are in agreement with the input quantities, within a factor of 2 in 90% of the cases. The errors on position, count rate, and size increase with off-axis angle and for detections of lower significance. We have also checked that the upper limits computed with our method are consistent with the count rates of undetected input sources. Finally, we have tested the algorithm by applying it on various actual PSPC images, among the most challenging for automated detection procedures (crowded fields, extended sources, and nonuniform diffuse emission). The performance of our method in these images is satisfactory and outperforms those of other current X-ray detection techniques, such as those employed to produce the MPE and WGA catalogs of PSPC sources, in terms of both detection reliability and efficiency. We have also investigated the theoretical limit for point-source detection, with the result that even sources with only 2-3 photons may be reliably detected using an efficient method in images with sufficiently high resolution and low background.

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

Islam, Md. Shafiqul, E-mail: shafique@eng.ukm.my; Hannan, M.A., E-mail: hannan@eng.ukm.my; Basri, Hassan

Highlights: • Solid waste bin level detection using Dynamic Time Warping (DTW). • Gabor wavelet filter is used to extract the solid waste image features. • Multi-Layer Perceptron classifier network is used for bin image classification. • The classification performance evaluated by ROC curve analysis. - Abstract: The increasing requirement for Solid Waste Management (SWM) has become a significant challenge for municipal authorities. A number of integrated systems and methods have introduced to overcome this challenge. Many researchers have aimed to develop an ideal SWM system, including approaches involving software-based routing, Geographic Information Systems (GIS), Radio-frequency Identification (RFID), or sensormore » intelligent bins. Image processing solutions for the Solid Waste (SW) collection have also been developed; however, during capturing the bin image, it is challenging to position the camera for getting a bin area centralized image. As yet, there is no ideal system which can correctly estimate the amount of SW. This paper briefly discusses an efficient image processing solution to overcome these problems. Dynamic Time Warping (DTW) was used for detecting and cropping the bin area and Gabor wavelet (GW) was introduced for feature extraction of the waste bin image. Image features were used to train the classifier. A Multi-Layer Perceptron (MLP) classifier was used to classify the waste bin level and estimate the amount of waste inside the bin. The area under the Receiver Operating Characteristic (ROC) curves was used to statistically evaluate classifier performance. The results of this developed system are comparable to previous image processing based system. The system demonstration using DTW with GW for feature extraction and an MLP classifier led to promising results with respect to the accuracy of waste level estimation (98.50%). The application can be used to optimize the routing of waste collection based on the estimated bin level.« less

Volumetric Echocardiographic Particle Image Velocimetry (V-Echo-PIV)

NASA Astrophysics Data System (ADS)

Falahatpisheh, Ahmad; Kheradvar, Arash

2015-11-01

Measurement of 3D flow field inside the cardiac chambers has proven to be a challenging task. Current laser-based 3D PIV methods estimate the third component of the velocity rather than directly measuring it and also cannot be used to image the opaque heart chambers. Modern echocardiography systems are equipped with 3D probes that enable imaging the entire 3D opaque field. However, this feature has not yet been employed for 3D vector characterization of blood flow. For the first time, we introduce a method that generates velocity vector field in 4D based on volumetric echocardiographic images. By assuming the conservation of brightness in 3D, blood speckles are tracked. A hierarchical 3D PIV method is used to account for large particle displacement. The discretized brightness transport equation is solved in a least square sense in interrogation windows of size 163 voxels. We successfully validate the method in analytical and experimental cases. Volumetric echo data of a left ventricle is then processed in the systolic phase. The expected velocity fields were successfully predicted by V-Echo-PIV. In this work, we showed a method to image blood flow in 3D based on volumetric images of human heart using no contrast agent.

Geoelectrical Tomography for landslide monitoring: state-of-the-art and future challenges.

NASA Astrophysics Data System (ADS)

Lapenna, V.; Perrone, A.; Piscitelli, S.

2011-12-01

Recently, novel algorithms for tomographic data inversion, robust models for describing the hydrogeophysical processes and new sensor networks for the field data acquisition have rapidly transformed the geoelectrical methods in a powerful and cost-effective tool for geo-hazard monitoring. These technological and methodological improvements disclose the way for a wide spectra of interesting and challenging applications in geo-hazards monitoring: reconstruction of landslide geometry; identification of fluid and gas uprising in volcanic areas; electrical imaging of seismic faults etc.. We briefly resume the current state-of-the-art of the geoelectrical methods in landslide monitoring and introduce new and emerging applications of the geoelectrical tomographic methods. An overview of the more interesting results obtained in different areas of Italian territory affected by wide and diffuse hydrogeological instability phenomena will be presented and discussed. We will focus the attention to some recent results obtained in the frame of national and international projects (Morfeo, Eurorisk/Preview, DORIS). One of the key challenges for the future will be the integration of active (Resistivity) and passive (Self-Potential) measurements for obtaining 2D, 3D and 4D (time-lapse) electrical tomographies able to follow the spatial and temporal dynamics of electrical parameters (i.e. resistivity, self-potential) inside the landslide body. The resistivity imaging can be applied for illuminating the sliding surfaces and for mapping the time-dependent changes of water content in vadose zones, while the Self Potential imaging could give a significant contribute for delineating the groundwater circulation patterns and to the early identification of triggering factors.

ScatterType: a reading CAPTCHA resistant to segmentation attack

NASA Astrophysics Data System (ADS)

Baird, Henry S.; Riopka, Terry P.

2004-12-01

A reading-based CAPTCHA designed to resist character-segmentation attacks, called 'ScatterType,' is described. Its challenges are pseudorandomly synthesized images of text strings rendered in machine-print typefaces: within each image, characters are fragmented using horizontal and vertical cuts, and the fragments are scattered by vertical and horizontal displacements. This scattering is designed to defeat all methods known to us for automatic segmentation into characters. As in the BaffleText CAPTCHA, English-like but unspellable text-strings are used to defend against known-dictionary attacks. In contrast to the PessimalPrint and BaffleText CAPTCHAs (and others), no physics-based image degradations, occlusions, or extraneous patterns are employed. We report preliminary results from a human legibility trial with 57 volunteers that yielded 4275 CAPTCHA challenges and responses. ScatterType human legibility remains remarkably high even on extremely degraded cases. We speculate that this is due to Gestalt perception abilities assisted by style-specific (here, typeface-specific) consistency among primitive shape features of character fragments. Although recent efforts to automate style-consistent perceptual skills have reported progress, the best known methods do not yet pose a threat to ScatterType. The experimental data also show that subjective rating of difficulty is strongly (and usefully) correlated with illegibility. In addition, we present early insights emerging from these data as we explore the ScatterType design space -- choice of typefaces, 'words', cut positioning, and displacements -- with the goal of locating regimes in which ScatterType challenges remain comfortably legible to almost all people but strongly resist mahine-vision methods for automatic segmentation into characters.

ScatterType: a reading CAPTCHA resistant to segmentation attack

NASA Astrophysics Data System (ADS)

Baird, Henry S.; Riopka, Terry P.

2005-01-01

A reading-based CAPTCHA designed to resist character-segmentation attacks, called 'ScatterType,' is described. Its challenges are pseudorandomly synthesized images of text strings rendered in machine-print typefaces: within each image, characters are fragmented using horizontal and vertical cuts, and the fragments are scattered by vertical and horizontal displacements. This scattering is designed to defeat all methods known to us for automatic segmentation into characters. As in the BaffleText CAPTCHA, English-like but unspellable text-strings are used to defend against known-dictionary attacks. In contrast to the PessimalPrint and BaffleText CAPTCHAs (and others), no physics-based image degradations, occlusions, or extraneous patterns are employed. We report preliminary results from a human legibility trial with 57 volunteers that yielded 4275 CAPTCHA challenges and responses. ScatterType human legibility remains remarkably high even on extremely degraded cases. We speculate that this is due to Gestalt perception abilities assisted by style-specific (here, typeface-specific) consistency among primitive shape features of character fragments. Although recent efforts to automate style-consistent perceptual skills have reported progress, the best known methods do not yet pose a threat to ScatterType. The experimental data also show that subjective rating of difficulty is strongly (and usefully) correlated with illegibility. In addition, we present early insights emerging from these data as we explore the ScatterType design space -- choice of typefaces, 'words', cut positioning, and displacements -- with the goal of locating regimes in which ScatterType challenges remain comfortably legible to almost all people but strongly resist mahine-vision methods for automatic segmentation into characters.

Computational characterization of ordered nanostructured surfaces

NASA Astrophysics Data System (ADS)

Mohieddin Abukhdeir, Nasser

2016-08-01

A vital and challenging task for materials researchers is to determine relationships between material characteristics and desired properties. While the measurement and assessment of material properties can be complex, quantitatively characterizing their structure is frequently a more challenging task. This issue is magnified for materials researchers in the areas of nanoscience and nanotechnology, where material structure is further complicated by phenomena such as self-assembly, collective behavior, and measurement uncertainty. Recent progress has been made in this area for both self-assembled and nanostructured surfaces due to increasing accessibility of imaging techniques at the nanoscale. In this context, recent advances in nanomaterial surface structure characterization are reviewed including the development of new theory and image processing methods.

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

Brewer, Brendon J.; Foreman-Mackey, Daniel; Hogg, David W., E-mail: bj.brewer@auckland.ac.nz

We present and implement a probabilistic (Bayesian) method for producing catalogs from images of stellar fields. The method is capable of inferring the number of sources N in the image and can also handle the challenges introduced by noise, overlapping sources, and an unknown point-spread function. The luminosity function of the stars can also be inferred, even when the precise luminosity of each star is uncertain, via the use of a hierarchical Bayesian model. The computational feasibility of the method is demonstrated on two simulated images with different numbers of stars. We find that our method successfully recovers the inputmore » parameter values along with principled uncertainties even when the field is crowded. We also compare our results with those obtained from the SExtractor software. While the two approaches largely agree about the fluxes of the bright stars, the Bayesian approach provides more accurate inferences about the faint stars and the number of stars, particularly in the crowded case.« less

Concurrent application of TMS and near-infrared optical imaging: methodological considerations and potential artifacts

PubMed Central

Parks, Nathan A.

2013-01-01

The simultaneous application of transcranial magnetic stimulation (TMS) with non-invasive neuroimaging provides a powerful method for investigating functional connectivity in the human brain and the causal relationships between areas in distributed brain networks. TMS has been combined with numerous neuroimaging techniques including, electroencephalography (EEG), functional magnetic resonance imaging (fMRI), and positron emission tomography (PET). Recent work has also demonstrated the feasibility and utility of combining TMS with non-invasive near-infrared optical imaging techniques, functional near-infrared spectroscopy (fNIRS) and the event-related optical signal (EROS). Simultaneous TMS and optical imaging affords a number of advantages over other neuroimaging methods but also involves a unique set of methodological challenges and considerations. This paper describes the methodology of concurrently performing optical imaging during the administration of TMS, focusing on experimental design, potential artifacts, and approaches to controlling for these artifacts. PMID:24065911

Single-shot ultrafast tomographic imaging by spectral multiplexing

NASA Astrophysics Data System (ADS)

Matlis, N. H.; Axley, A.; Leemans, W. P.

2012-10-01

Computed tomography has profoundly impacted science, medicine and technology by using projection measurements scanned over multiple angles to permit cross-sectional imaging of an object. The application of computed tomography to moving or dynamically varying objects, however, has been limited by the temporal resolution of the technique, which is set by the time required to complete the scan. For objects that vary on ultrafast timescales, traditional scanning methods are not an option. Here we present a non-scanning method capable of resolving structure on femtosecond timescales by using spectral multiplexing of a single laser beam to perform tomographic imaging over a continuous range of angles simultaneously. We use this technique to demonstrate the first single-shot ultrafast computed tomography reconstructions and obtain previously inaccessible structure and position information for laser-induced plasma filaments. This development enables real-time tomographic imaging for ultrafast science, and offers a potential solution to the challenging problem of imaging through scattering surfaces.

An Unsupervised Approach for Extraction of Blood Vessels from Fundus Images.

PubMed

Dash, Jyotiprava; Bhoi, Nilamani

2018-04-26

Pathological disorders may happen due to small changes in retinal blood vessels which may later turn into blindness. Hence, the accurate segmentation of blood vessels is becoming a challenging task for pathological analysis. This paper offers an unsupervised recursive method for extraction of blood vessels from ophthalmoscope images. First, a vessel-enhanced image is generated with the help of gamma correction and contrast-limited adaptive histogram equalization (CLAHE). Next, the vessels are extracted iteratively by applying an adaptive thresholding technique. At last, a final vessel segmented image is produced by applying a morphological cleaning operation. Evaluations are accompanied on the publicly available digital retinal images for vessel extraction (DRIVE) and Child Heart And Health Study in England (CHASE_DB1) databases using nine different measurements. The proposed method achieves average accuracies of 0.957 and 0.952 on DRIVE and CHASE_DB1 databases respectively.

Estimation of color filter array data from JPEG images for improved demosaicking

NASA Astrophysics Data System (ADS)

Feng, Wei; Reeves, Stanley J.

2006-02-01

On-camera demosaicking algorithms are necessarily simple and therefore do not yield the best possible images. However, off-camera demosaicking algorithms face the additional challenge that the data has been compressed and therefore corrupted by quantization noise. We propose a method to estimate the original color filter array (CFA) data from JPEG-compressed images so that more sophisticated (and better) demosaicking schemes can be applied to get higher-quality images. The JPEG image formation process, including simple demosaicking, color space transformation, chrominance channel decimation and DCT, is modeled as a series of matrix operations followed by quantization on the CFA data, which is estimated by least squares. An iterative method is used to conserve memory and speed computation. Our experiments show that the mean square error (MSE) with respect to the original CFA data is reduced significantly using our algorithm, compared to that of unprocessed JPEG and deblocked JPEG data.

Pornographic image recognition and filtering using incremental learning in compressed domain

NASA Astrophysics Data System (ADS)

Zhang, Jing; Wang, Chao; Zhuo, Li; Geng, Wenhao

2015-11-01

With the rapid development and popularity of the network, the openness, anonymity, and interactivity of networks have led to the spread and proliferation of pornographic images on the Internet, which have done great harm to adolescents' physical and mental health. With the establishment of image compression standards, pornographic images are mainly stored with compressed formats. Therefore, how to efficiently filter pornographic images is one of the challenging issues for information security. A pornographic image recognition and filtering method in the compressed domain is proposed by using incremental learning, which includes the following steps: (1) low-resolution (LR) images are first reconstructed from the compressed stream of pornographic images, (2) visual words are created from the LR image to represent the pornographic image, and (3) incremental learning is adopted to continuously adjust the classification rules to recognize the new pornographic image samples after the covering algorithm is utilized to train and recognize the visual words in order to build the initial classification model of pornographic images. The experimental results show that the proposed pornographic image recognition method using incremental learning has a higher recognition rate as well as costing less recognition time in the compressed domain.

Workflow Challenges of Enterprise Imaging: HIMSS-SIIM Collaborative White Paper.

PubMed

Towbin, Alexander J; Roth, Christopher J; Bronkalla, Mark; Cram, Dawn

2016-10-01

With the advent of digital cameras, there has been an explosion in the number of medical specialties using images to diagnose or document disease and guide interventions. In many specialties, these images are not added to the patient's electronic medical record and are not distributed so that other providers caring for the patient can view them. As hospitals begin to develop enterprise imaging strategies, they have found that there are multiple challenges preventing the implementation of systems to manage image capture, image upload, and image management. This HIMSS-SIIM white paper will describe the key workflow challenges related to enterprise imaging and offer suggestions for potential solutions to these challenges.

Magnetic Resonance Microscopy of the Lung

NASA Astrophysics Data System (ADS)

Johnson, G. Allan

1999-11-01

The lung presents both challenges and opportunities for study by magnetic resonance imaging (MRI). The technical challenges arise from respiratory and cardiac motion, limited signal from the tissues, and unique physical structure of the lung. These challenges are heightened in magnetic resonance microscopy (MRM) where the spatial resolution may be up to a million times higher than that of conventional MRI. The development of successful techniques for MRM of the lung present enormous opportunities for basic studies of lung structure and function, toxicology, environmental stress, and drug discovery by permitting investigators to study this most essential organ nondestructively in the live animal. Over the last 15 years, scientists at the Duke Center for In Vivo Microscopy have developed techniques for MRM in the live animal through an interdisciplinary program of biology, physics, chemistry, electrical engineering, and computer science. This talk will focus on the development of specialized radiofrequency coils for lung imaging, projection encoding methods to limit susceptibility losses, specialized support structures to control and monitor physiologic motion, and the most recent development of hyperpolarized gas imaging with ^3He and ^129Xe.

A Two-Stream Deep Fusion Framework for High-Resolution Aerial Scene Classification

PubMed Central

Liu, Fuxian

2018-01-01

One of the challenging problems in understanding high-resolution remote sensing images is aerial scene classification. A well-designed feature representation method and classifier can improve classification accuracy. In this paper, we construct a new two-stream deep architecture for aerial scene classification. First, we use two pretrained convolutional neural networks (CNNs) as feature extractor to learn deep features from the original aerial image and the processed aerial image through saliency detection, respectively. Second, two feature fusion strategies are adopted to fuse the two different types of deep convolutional features extracted by the original RGB stream and the saliency stream. Finally, we use the extreme learning machine (ELM) classifier for final classification with the fused features. The effectiveness of the proposed architecture is tested on four challenging datasets: UC-Merced dataset with 21 scene categories, WHU-RS dataset with 19 scene categories, AID dataset with 30 scene categories, and NWPU-RESISC45 dataset with 45 challenging scene categories. The experimental results demonstrate that our architecture gets a significant classification accuracy improvement over all state-of-the-art references. PMID:29581722

A Two-Stream Deep Fusion Framework for High-Resolution Aerial Scene Classification.

PubMed

Yu, Yunlong; Liu, Fuxian

2018-01-01

One of the challenging problems in understanding high-resolution remote sensing images is aerial scene classification. A well-designed feature representation method and classifier can improve classification accuracy. In this paper, we construct a new two-stream deep architecture for aerial scene classification. First, we use two pretrained convolutional neural networks (CNNs) as feature extractor to learn deep features from the original aerial image and the processed aerial image through saliency detection, respectively. Second, two feature fusion strategies are adopted to fuse the two different types of deep convolutional features extracted by the original RGB stream and the saliency stream. Finally, we use the extreme learning machine (ELM) classifier for final classification with the fused features. The effectiveness of the proposed architecture is tested on four challenging datasets: UC-Merced dataset with 21 scene categories, WHU-RS dataset with 19 scene categories, AID dataset with 30 scene categories, and NWPU-RESISC45 dataset with 45 challenging scene categories. The experimental results demonstrate that our architecture gets a significant classification accuracy improvement over all state-of-the-art references.

Methods for multiple-telescope beam imaging and guiding in the near-infrared

NASA Astrophysics Data System (ADS)

Anugu, N.; Amorim, A.; Gordo, P.; Eisenhauer, F.; Pfuhl, O.; Haug, M.; Wieprecht, E.; Wiezorrek, E.; Lima, J.; Perrin, G.; Brandner, W.; Straubmeier, C.; Le Bouquin, J.-B.; Garcia, P. J. V.

2018-05-01

Atmospheric turbulence and precise measurement of the astrometric baseline vector between any two telescopes are two major challenges in implementing phase-referenced interferometric astrometry and imaging. They limit the performance of a fibre-fed interferometer by degrading the instrument sensitivity and the precision of astrometric measurements and by introducing image reconstruction errors due to inaccurate phases. A multiple-beam acquisition and guiding camera was built to meet these challenges for a recently commissioned four-beam combiner instrument, GRAVITY, at the European Southern Observatory Very Large Telescope Interferometer. For each telescope beam, it measures (a) field tip-tilts by imaging stars in the sky, (b) telescope pupil shifts by imaging pupil reference laser beacons installed on each telescope using a 2 × 2 lenslet and (c) higher-order aberrations using a 9 × 9 Shack-Hartmann. The telescope pupils are imaged to provide visual monitoring while observing. These measurements enable active field and pupil guiding by actuating a train of tip-tilt mirrors placed in the pupil and field planes, respectively. The Shack-Hartmann measured quasi-static aberrations are used to focus the auxiliary telescopes and allow the possibility of correcting the non-common path errors between the adaptive optics systems of the unit telescopes and GRAVITY. The guiding stabilizes the light injection into single-mode fibres, increasing sensitivity and reducing the astrometric and image reconstruction errors. The beam guiding enables us to achieve an astrometric error of less than 50 μas. Here, we report on the data reduction methods and laboratory tests of the multiple-beam acquisition and guiding camera and its performance on-sky.

Semiautomatic tumor segmentation with multimodal images in a conditional random field framework.

PubMed

Hu, Yu-Chi; Grossberg, Michael; Mageras, Gikas

2016-04-01

Volumetric medical images of a single subject can be acquired using different imaging modalities, such as computed tomography, magnetic resonance imaging (MRI), and positron emission tomography. In this work, we present a semiautomatic segmentation algorithm that can leverage the synergies between different image modalities while integrating interactive human guidance. The algorithm provides a statistical segmentation framework partly automating the segmentation task while still maintaining critical human oversight. The statistical models presented are trained interactively using simple brush strokes to indicate tumor and nontumor tissues and using intermediate results within a patient's image study. To accomplish the segmentation, we construct the energy function in the conditional random field (CRF) framework. For each slice, the energy function is set using the estimated probabilities from both user brush stroke data and prior approved segmented slices within a patient study. The progressive segmentation is obtained using a graph-cut-based minimization. Although no similar semiautomated algorithm is currently available, we evaluated our method with an MRI data set from Medical Image Computing and Computer Assisted Intervention Society multimodal brain segmentation challenge (BRATS 2012 and 2013) against a similar fully automatic method based on CRF and a semiautomatic method based on grow-cut, and our method shows superior performance.

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

PubMed Central

2014-01-01

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

Automatic multi-label annotation of abdominal CT images using CBIR

NASA Astrophysics Data System (ADS)

Xue, Zhiyun; Antani, Sameer; Long, L. Rodney; Thoma, George R.

2017-03-01

We present a technique to annotate multiple organs shown in 2-D abdominal/pelvic CT images using CBIR. This annotation task is motivated by our research interests in visual question-answering (VQA). We aim to apply results from this effort in Open-iSM, a multimodal biomedical search engine developed by the National Library of Medicine (NLM). Understanding visual content of biomedical images is a necessary step for VQA. Though sufficient annotational information about an image may be available in related textual metadata, not all may be useful as descriptive tags, particularly for anatomy on the image. In this paper, we develop and evaluate a multi-label image annotation method using CBIR. We evaluate our method on two 2-D CT image datasets we generated from 3-D volumetric data obtained from a multi-organ segmentation challenge hosted in MICCAI 2015. Shape and spatial layout information is used to encode visual characteristics of the anatomy. We adapt a weighted voting scheme to assign multiple labels to the query image by combining the labels of the images identified as similar by the method. Key parameters that may affect the annotation performance, such as the number of images used in the label voting and the threshold for excluding labels that have low weights, are studied. The method proposes a coarse-to-fine retrieval strategy which integrates the classification with the nearest-neighbor search. Results from our evaluation (using the MICCAI CT image datasets as well as figures from Open-i) are presented.

Improved Seam-Line Searching Algorithm for UAV Image Mosaic with Optical Flow

PubMed Central

Zhang, Weilong; Guo, Bingxuan; Liao, Xuan; Li, Wenzhuo

2018-01-01

Ghosting and seams are two major challenges in creating unmanned aerial vehicle (UAV) image mosaic. In response to these problems, this paper proposes an improved method for UAV image seam-line searching. First, an image matching algorithm is used to extract and match the features of adjacent images, so that they can be transformed into the same coordinate system. Then, the gray scale difference, the gradient minimum, and the optical flow value of pixels in adjacent image overlapped area in a neighborhood are calculated, which can be applied to creating an energy function for seam-line searching. Based on that, an improved dynamic programming algorithm is proposed to search the optimal seam-lines to complete the UAV image mosaic. This algorithm adopts a more adaptive energy aggregation and traversal strategy, which can find a more ideal splicing path for adjacent UAV images and avoid the ground objects better. The experimental results show that the proposed method can effectively solve the problems of ghosting and seams in the panoramic UAV images. PMID:29659526

Lane identification and path planning for autonomous mobile robots

NASA Astrophysics Data System (ADS)

McKeon, Robert T.; Paulik, Mark; Krishnan, Mohan

2006-10-01

This work has been performed in conjunction with the University of Detroit Mercy's (UDM) ECE Department autonomous vehicle entry in the 2006 Intelligent Ground Vehicle Competition (www.igvc.org). The IGVC challenges engineering students to design autonomous vehicles and compete in a variety of unmanned mobility competitions. The course to be traversed in the competition consists of a lane demarcated by painted lines on grass with the possibility of one of the two lines being deliberately left out over segments of the course. The course also consists of other challenging artifacts such as sandpits, ramps, potholes, and colored tarps that alter the color composition of scenes, and obstacles set up using orange and white construction barrels. This paper describes a composite lane edge detection approach that uses three algorithms to implement noise filters enabling increased removal of noise prior to the application of image thresholding. The first algorithm uses a row-adaptive statistical filter to establish an intensity floor followed by a global threshold based on a reverse cumulative intensity histogram and a priori knowledge about lane thickness and separation. The second method first improves the contrast of the image by implementing an arithmetic combination of the blue plane (RGB format) and a modified saturation plane (HSI format). A global threshold is then applied based on the mean of the intensity image and a user-defined offset. The third method applies the horizontal component of the Sobel mask to a modified gray scale of the image, followed by a thresholding method similar to the one used in the second method. The Hough transform is applied to each of the resulting binary images to select the most probable line candidates. Finally, a heuristics-based confidence interval is determined, and the results sent on to a separate fuzzy polar-based navigation algorithm, which fuses the image data with that produced by a laser scanner (for obstacle detection).

Split Bregman multicoil accelerated reconstruction technique: A new framework for rapid reconstruction of cardiac perfusion MRI

PubMed Central

Kamesh Iyer, Srikant; Tasdizen, Tolga; Likhite, Devavrat; DiBella, Edward

2016-01-01

Purpose: Rapid reconstruction of undersampled multicoil MRI data with iterative constrained reconstruction method is a challenge. The authors sought to develop a new substitution based variable splitting algorithm for faster reconstruction of multicoil cardiac perfusion MRI data. Methods: The new method, split Bregman multicoil accelerated reconstruction technique (SMART), uses a combination of split Bregman based variable splitting and iterative reweighting techniques to achieve fast convergence. Total variation constraints are used along the spatial and temporal dimensions. The method is tested on nine ECG-gated dog perfusion datasets, acquired with a 30-ray golden ratio radial sampling pattern and ten ungated human perfusion datasets, acquired with a 24-ray golden ratio radial sampling pattern. Image quality and reconstruction speed are evaluated and compared to a gradient descent (GD) implementation and to multicoil k-t SLR, a reconstruction technique that uses a combination of sparsity and low rank constraints. Results: Comparisons based on blur metric and visual inspection showed that SMART images had lower blur and better texture as compared to the GD implementation. On average, the GD based images had an ∼18% higher blur metric as compared to SMART images. Reconstruction of dynamic contrast enhanced (DCE) cardiac perfusion images using the SMART method was ∼6 times faster than standard gradient descent methods. k-t SLR and SMART produced images with comparable image quality, though SMART was ∼6.8 times faster than k-t SLR. Conclusions: The SMART method is a promising approach to reconstruct good quality multicoil images from undersampled DCE cardiac perfusion data rapidly. PMID:27036592

Diffusion Imaging of Auditory and Auditory-Limbic Connectivity in Tinnitus: Preliminary Evidence and Methodological Challenges

PubMed Central

Seydell-Greenwald, Anna; Raven, Erika P.; Leaver, Amber M.; Turesky, Ted K.; Rauschecker, Josef P.

2014-01-01

Subjective tinnitus, or “ringing in the ears,” is perceived by 10 to 15 percent of the adult population and causes significant suffering in a subset of patients. While it was originally thought of as a purely auditory phenomenon, there is increasing evidence that the limbic system influences whether and how tinnitus is perceived, far beyond merely determining the patient's emotional reaction to the phantom sound. Based on functional imaging and electrophysiological data, recent articles frame tinnitus as a “network problem” arising from abnormalities in auditory-limbic interactions. Diffusion-weighted magnetic resonance imaging is a noninvasive method for investigating anatomical connections in vivo. It thus has the potential to provide anatomical evidence for the proposed changes in auditory-limbic connectivity. However, the few diffusion imaging studies of tinnitus performed to date have inconsistent results. In the present paper, we briefly summarize the results of previous studies, aiming to reconcile their results. After detailing analysis methods, we then report findings from a new dataset. We conclude that while there is some evidence for tinnitus-related increases in auditory and auditory-limbic connectivity that counteract hearing-loss related decreases in auditory connectivity, these results should be considered preliminary until several technical challenges have been overcome. PMID:25050181

Indexing amyloid peptide diffraction from serial femtosecond crystallography: new algorithms for sparse patterns

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

Brewster, Aaron S.; Sawaya, Michael R.; University of California, Los Angeles, CA 90095-1570

2015-02-01

Special methods are required to interpret sparse diffraction patterns collected from peptide crystals at X-ray free-electron lasers. Bragg spots can be indexed from composite-image powder rings, with crystal orientations then deduced from a very limited number of spot positions. Still diffraction patterns from peptide nanocrystals with small unit cells are challenging to index using conventional methods owing to the limited number of spots and the lack of crystal orientation information for individual images. New indexing algorithms have been developed as part of the Computational Crystallography Toolbox (cctbx) to overcome these challenges. Accurate unit-cell information derived from an aggregate data setmore » from thousands of diffraction patterns can be used to determine a crystal orientation matrix for individual images with as few as five reflections. These algorithms are potentially applicable not only to amyloid peptides but also to any set of diffraction patterns with sparse properties, such as low-resolution virus structures or high-throughput screening of still images captured by raster-scanning at synchrotron sources. As a proof of concept for this technique, successful integration of X-ray free-electron laser (XFEL) data to 2.5 Å resolution for the amyloid segment GNNQQNY from the Sup35 yeast prion is presented.« less

Combining image processing and modeling to generate traces of beta-strands from cryo-EM density images of beta-barrels.

PubMed

Si, Dong; He, Jing

2014-01-01

Electron cryo-microscopy (Cryo-EM) technique produces 3-dimensional (3D) density images of proteins. When resolution of the images is not high enough to resolve the molecular details, it is challenging for image processing methods to enhance the molecular features. β-barrel is a particular structure feature that is formed by multiple β-strands in a barrel shape. There is no existing method to derive β-strands from the 3D image of a β-barrel at medium resolutions. We propose a new method, StrandRoller, to generate a small set of possible β-traces from the density images at medium resolutions of 5-10Å. StrandRoller has been tested using eleven β-barrel images simulated to 10Å resolution and one image isolated from the experimentally derived cryo-EM density image at 6.7Å resolution. StrandRoller was able to detect 81.84% of the β-strands with an overall 1.5Å 2-way distance between the detected and the observed β-traces, if the best of fifteen detections is considered. Our results suggest that it is possible to derive a small set of possible β-traces from the β-barrel cryo-EM image at medium resolutions even when no separation of the β-strands is visible in the images.

Enhanced facial texture illumination normalization for face recognition.

PubMed

Luo, Yong; Guan, Ye-Peng

2015-08-01

An uncontrolled lighting condition is one of the most critical challenges for practical face recognition applications. An enhanced facial texture illumination normalization method is put forward to resolve this challenge. An adaptive relighting algorithm is developed to improve the brightness uniformity of face images. Facial texture is extracted by using an illumination estimation difference algorithm. An anisotropic histogram-stretching algorithm is proposed to minimize the intraclass distance of facial skin and maximize the dynamic range of facial texture distribution. Compared with the existing methods, the proposed method can more effectively eliminate the redundant information of facial skin and illumination. Extensive experiments show that the proposed method has superior performance in normalizing illumination variation and enhancing facial texture features for illumination-insensitive face recognition.

Measuring the circular motion of small objects using laser stroboscopic images.

PubMed

Wang, Hairong; Fu, Y; Du, R

2008-01-01

Measuring the circular motion of a small object, including its displacement, speed, and acceleration, is a challenging task. This paper presents a new method for measuring repetitive and/or nonrepetitive, constant speed and/or variable speed circular motion using laser stroboscopic images. Under stroboscopic illumination, each image taken by an ordinary camera records multioutlines of an object in motion; hence, processing the stroboscopic image will be able to extract the motion information. We built an experiment apparatus consisting of a laser as the light source, a stereomicroscope to magnify the image, and a normal complementary metal oxide semiconductor camera to record the image. As the object is in motion, the stroboscopic illumination generates a speckle pattern on the object that can be recorded by the camera and analyzed by a computer. Experimental results indicate that the stroboscopic imaging is stable under various conditions. Moreover, the characteristics of the motion, including the displacement, the velocity, and the acceleration can be calculated based on the width of speckle marks, the illumination intensity, the duty cycle, and the sampling frequency. Compared with the popular high-speed camera method, the presented method may achieve the same measuring accuracy, but with much reduced cost and complexity.

Salt-and-pepper noise removal using modified mean filter and total variation minimization

NASA Astrophysics Data System (ADS)

Aghajarian, Mickael; McInroy, John E.; Wright, Cameron H. G.

2018-01-01

The search for effective noise removal algorithms is still a real challenge in the field of image processing. An efficient image denoising method is proposed for images that are corrupted by salt-and-pepper noise. Salt-and-pepper noise takes either the minimum or maximum intensity, so the proposed method restores the image by processing the pixels whose values are either 0 or 255 (assuming an 8-bit/pixel image). For low levels of noise corruption (less than or equal to 50% noise density), the method employs the modified mean filter (MMF), while for heavy noise corruption, noisy pixels values are replaced by the weighted average of the MMF and the total variation of corrupted pixels, which is minimized using convex optimization. Two fuzzy systems are used to determine the weights for taking average. To evaluate the performance of the algorithm, several test images with different noise levels are restored, and the results are quantitatively measured by peak signal-to-noise ratio and mean absolute error. The results show that the proposed scheme gives considerable noise suppression up to a noise density of 90%, while almost completely maintaining edges and fine details of the original image.

Metadata-assisted nonuniform atmospheric scattering model of image haze removal for medium-altitude unmanned aerial vehicle

NASA Astrophysics Data System (ADS)

Liu, Chunlei; Ding, Wenrui; Li, Hongguang; Li, Jiankun

2017-09-01

Haze removal is a nontrivial work for medium-altitude unmanned aerial vehicle (UAV) image processing because of the effects of light absorption and scattering. The challenges are attributed mainly to image distortion and detail blur during the long-distance and large-scale imaging process. In our work, a metadata-assisted nonuniform atmospheric scattering model is proposed to deal with the aforementioned problems of medium-altitude UAV. First, to better describe the real atmosphere, we propose a nonuniform atmospheric scattering model according to the aerosol distribution, which directly benefits the image distortion correction. Second, considering the characteristics of long-distance imaging, we calculate the depth map, which is an essential clue to modeling, on the basis of UAV metadata information. An accurate depth map reduces the color distortion compared with the depth of field obtained by other existing methods based on priors or assumptions. Furthermore, we use an adaptive median filter to address the problem of fuzzy details caused by the global airlight value. Experimental results on both real flight and synthetic images demonstrate that our proposed method outperforms four other existing haze removal methods.

Prostate segmentation in MRI using fused T2-weighted and elastography images

NASA Astrophysics Data System (ADS)

Nir, Guy; Sahebjavaher, Ramin S.; Baghani, Ali; Sinkus, Ralph; Salcudean, Septimiu E.

2014-03-01

Segmentation of the prostate in medical imaging is a challenging and important task for surgical planning and delivery of prostate cancer treatment. Automatic prostate segmentation can improve speed, reproducibility and consistency of the process. In this work, we propose a method for automatic segmentation of the prostate in magnetic resonance elastography (MRE) images. The method utilizes the complementary property of the elastogram and the corresponding T2-weighted image, which are obtained from the phase and magnitude components of the imaging signal, respectively. It follows a variational approach to propagate an active contour model based on the combination of region statistics in the elastogram and the edge map of the T2-weighted image. The method is fast and does not require prior shape information. The proposed algorithm is tested on 35 clinical image pairs from five MRE data sets, and is evaluated in comparison with manual contouring. The mean absolute distance between the automatic and manual contours is 1.8mm, with a maximum distance of 5.6mm. The relative area error is 7.6%, and the duration of the segmentation process is 2s per slice.

High resolution through-the-wall radar image based on beamspace eigenstructure subspace methods

NASA Astrophysics Data System (ADS)

Yoon, Yeo-Sun; Amin, Moeness G.

2008-04-01

Through-the-wall imaging (TWI) is a challenging problem, even if the wall parameters and characteristics are known to the system operator. Proper target classification and correct imaging interpretation require the application of high resolution techniques using limited array size. In inverse synthetic aperture radar (ISAR), signal subspace methods such as Multiple Signal Classification (MUSIC) are used to obtain high resolution imaging. In this paper, we adopt signal subspace methods and apply them to the 2-D spectrum obtained from the delay-andsum beamforming image. This is in contrast to ISAR, where raw data, in frequency and angle, is directly used to form the estimate of the covariance matrix and array response vector. Using beams rather than raw data has two main advantages, namely, it improves the signal-to-noise ratio (SNR) and can correctly image typical indoor extended targets, such as tables and cabinets, as well as point targets. The paper presents both simulated and experimental results using synthesized and real data. It compares the performance of beam-space MUSIC and Capon beamformer. The experimental data is collected at the test facility in the Radar Imaging Laboratory, Villanova University.

Tri-Clustered Tensor Completion for Social-Aware Image Tag Refinement.

PubMed

Tang, Jinhui; Shu, Xiangbo; Qi, Guo-Jun; Li, Zechao; Wang, Meng; Yan, Shuicheng; Jain, Ramesh

2017-08-01

Social image tag refinement, which aims to improve tag quality by automatically completing the missing tags and rectifying the noise-corrupted ones, is an essential component for social image search. Conventional approaches mainly focus on exploring the visual and tag information, without considering the user information, which often reveals important hints on the (in)correct tags of social images. Towards this end, we propose a novel tri-clustered tensor completion framework to collaboratively explore these three kinds of information to improve the performance of social image tag refinement. Specifically, the inter-relations among users, images and tags are modeled by a tensor, and the intra-relations between users, images and tags are explored by three regularizations respectively. To address the challenges of the super-sparse and large-scale tensor factorization that demands expensive computing and memory cost, we propose a novel tri-clustering method to divide the tensor into a certain number of sub-tensors by simultaneously clustering users, images and tags into a bunch of tri-clusters. And then we investigate two strategies to complete these sub-tensors by considering (in)dependence between the sub-tensors. Experimental results on a real-world social image database demonstrate the superiority of the proposed method compared with the state-of-the-art methods.

Computation of mass-density images from x-ray refraction-angle images.

PubMed

Wernick, Miles N; Yang, Yongyi; Mondal, Indrasis; Chapman, Dean; Hasnah, Moumen; Parham, Christopher; Pisano, Etta; Zhong, Zhong

2006-04-07

In this paper, we investigate the possibility of computing quantitatively accurate images of mass density variations in soft tissue. This is a challenging task, because density variations in soft tissue, such as the breast, can be very subtle. Beginning from an image of refraction angle created by either diffraction-enhanced imaging (DEI) or multiple-image radiography (MIR), we estimate the mass-density image using a constrained least squares (CLS) method. The CLS algorithm yields accurate density estimates while effectively suppressing noise. Our method improves on an analytical method proposed by Hasnah et al (2005 Med. Phys. 32 549-52), which can produce significant artefacts when even a modest level of noise is present. We present a quantitative evaluation study to determine the accuracy with which mass density can be determined in the presence of noise. Based on computer simulations, we find that the mass-density estimation error can be as low as a few per cent for typical density variations found in the breast. Example images computed from less-noisy real data are also shown to illustrate the feasibility of the technique. We anticipate that density imaging may have application in assessment of water content of cartilage resulting from osteoarthritis, in evaluation of bone density, and in mammographic interpretation.

Nanoparticles for Biomedical Imaging

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

Nune, Satish K.; Gunda, Padmaja; Thallapally, Praveen K.

2009-11-01

Background: Synthetic nanoparticles are emerging as versatile tools in biomedical applications, particularly in the area of biomedical imaging. Nanoparticles 1 to 100 nm in diameter possess dimensions comparable to biological functional units. Diverse surface chemistries, unique magnetic properties, tunable absorption and emission properties, and recent advances in the synthesis and engineering of various nanoparticles suggest their potential as probes for early detection of diseases such as cancer. Surface functionalization has further expanded the potential of nanoparticles as probes for molecular imaging. Objective: To summarize emerging research of nanoparticles for biomedical imaging with increased selectivity and reduced non-specific uptake with increasedmore » spatial resolution containing stabilizers conjugated with targeting ligands. Methods: This review summarizes recent technological advances in the synthesis of various nanoparticle probes, and surveys methods to improve the targeting of nanoparticles for their applications in biomedical imaging. Conclusion: Structural design of nanomaterials for biomedical imaging continues to expand and diversify. Synthetic methods have aimed to control the size and surface characteristics of nanoparticles to control distribution, half-life and elimination. Although molecular imaging applications using nanoparticles are advancing into clinical applications, challenges such as storage stability and long-term toxicology should continue to be addressed. Keywords: nanoparticle synthesis, surface modification, targeting, molecular imaging, and biomedical imaging.« less

Coastline detection with time series of SAR images

NASA Astrophysics Data System (ADS)

Ao, Dongyang; Dumitru, Octavian; Schwarz, Gottfried; Datcu, Mihai

2017-10-01

For maritime remote sensing, coastline detection is a vital task. With continuous coastline detection results from satellite image time series, the actual shoreline, the sea level, and environmental parameters can be observed to support coastal management and disaster warning. Established coastline detection methods are often based on SAR images and wellknown image processing approaches. These methods involve a lot of complicated data processing, which is a big challenge for remote sensing time series. Additionally, a number of SAR satellites operating with polarimetric capabilities have been launched in recent years, and many investigations of target characteristics in radar polarization have been performed. In this paper, a fast and efficient coastline detection method is proposed which comprises three steps. First, we calculate a modified correlation coefficient of two SAR images of different polarization. This coefficient differs from the traditional computation where normalization is needed. Through this modified approach, the separation between sea and land becomes more prominent. Second, we set a histogram-based threshold to distinguish between sea and land within the given image. The histogram is derived from the statistical distribution of the polarized SAR image pixel amplitudes. Third, we extract continuous coastlines using a Canny image edge detector that is rather immune to speckle noise. Finally, the individual coastlines derived from time series of .SAR images can be checked for changes.

Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images.

PubMed

Marchetti, Michael A; Codella, Noel C F; Dusza, Stephen W; Gutman, David A; Helba, Brian; Kalloo, Aadi; Mishra, Nabin; Carrera, Cristina; Celebi, M Emre; DeFazio, Jennifer L; Jaimes, Natalia; Marghoob, Ashfaq A; Quigley, Elizabeth; Scope, Alon; Yélamos, Oriol; Halpern, Allan C

2018-02-01

Computer vision may aid in melanoma detection. We sought to compare melanoma diagnostic accuracy of computer algorithms to dermatologists using dermoscopic images. We conducted a cross-sectional study using 100 randomly selected dermoscopic images (50 melanomas, 44 nevi, and 6 lentigines) from an international computer vision melanoma challenge dataset (n = 379), along with individual algorithm results from 25 teams. We used 5 methods (nonlearned and machine learning) to combine individual automated predictions into "fusion" algorithms. In a companion study, 8 dermatologists classified the lesions in the 100 images as either benign or malignant. The average sensitivity and specificity of dermatologists in classification was 82% and 59%. At 82% sensitivity, dermatologist specificity was similar to the top challenge algorithm (59% vs. 62%, P = .68) but lower than the best-performing fusion algorithm (59% vs. 76%, P = .02). Receiver operating characteristic area of the top fusion algorithm was greater than the mean receiver operating characteristic area of dermatologists (0.86 vs. 0.71, P = .001). The dataset lacked the full spectrum of skin lesions encountered in clinical practice, particularly banal lesions. Readers and algorithms were not provided clinical data (eg, age or lesion history/symptoms). Results obtained using our study design cannot be extrapolated to clinical practice. Deep learning computer vision systems classified melanoma dermoscopy images with accuracy that exceeded some but not all dermatologists. Copyright © 2017 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

Expanding the Caring Lens: Nursing and Medical Students Reflecting on Images of Older People.

PubMed

Brand, Gabrielle; Miller, Karen; Saunders, Rosemary; Dugmore, Helen; Etherton-Beer, Christopher

2016-01-01

In changing higher education environments, health profession's educators have been increasingly challenged to prepare future health professionals to care for aging populations. This article reports on an exploratory, mixed-method research study that used an innovative photo-elicitation technique and interprofessional small-group work in the classroom to enhance the reflective learning experience of medical and nursing students. Data were collected from pre- and postquestionnaires and focus groups to explore shifts in perceptions toward older persons following the reflective learning session. The qualitative data revealed how using visual images of older persons provides a valuable learning space for reflection. Students found meaning in their own learning by creating shared storylines that challenged their perceptions of older people and themselves as future health professionals. These data support the use of visual methodologies to enhance engagement, reflection, and challenge students to explore and deepen their understanding in gerontology.

Review of spectral imaging technology in biomedical engineering: achievements and challenges.

PubMed

Li, Qingli; He, Xiaofu; Wang, Yiting; Liu, Hongying; Xu, Dongrong; Guo, Fangmin

2013-10-01

Spectral imaging is a technology that integrates conventional imaging and spectroscopy to get both spatial and spectral information from an object. Although this technology was originally developed for remote sensing, it has been extended to the biomedical engineering field as a powerful analytical tool for biological and biomedical research. This review introduces the basics of spectral imaging, imaging methods, current equipment, and recent advances in biomedical applications. The performance and analytical capabilities of spectral imaging systems for biological and biomedical imaging are discussed. In particular, the current achievements and limitations of this technology in biomedical engineering are presented. The benefits and development trends of biomedical spectral imaging are highlighted to provide the reader with an insight into the current technological advances and its potential for biomedical research.

Automatic Lumbar Spondylolisthesis Measurement in CT Images.

PubMed

Liao, Shu; Zhan, Yiqiang; Dong, Zhongxing; Yan, Ruyi; Gong, Liyan; Zhou, Xiang Sean; Salganicoff, Marcos; Fei, Jun

2016-07-01

Lumbar spondylolisthesis is one of the most common spinal diseases. It is caused by the anterior shift of a lumbar vertebrae relative to subjacent vertebrae. In current clinical practices, staging of spondylolisthesis is often conducted in a qualitative way. Although meyerding grading opens the door to stage spondylolisthesis in a more quantitative way, it relies on the manual measurement, which is time consuming and irreproducible. Thus, an automatic measurement algorithm becomes desirable for spondylolisthesis diagnosis and staging. However, there are two challenges. 1) Accurate detection of the most anterior and posterior points on the superior and inferior surfaces of each lumbar vertebrae. Due to the small size of the vertebrae, slight errors of detection may lead to significant measurement errors, hence, wrong disease stages. 2) Automatic localize and label each lumbar vertebrae is required to provide the semantic meaning of the measurement. It is difficult since different lumbar vertebraes have high similarity of both shape and image appearance. To resolve these challenges, a new auto measurement framework is proposed with two major contributions: First, a learning based spine labeling method that integrates both the image appearance and spine geometry information is designed to detect lumbar vertebrae. Second, a hierarchical method using both the population information from atlases and domain-specific information in the target image is proposed for most anterior and posterior points positioning. Validated on 258 CT spondylolisthesis patients, our method shows very similar results to manual measurements by radiologists and significantly increases the measurement efficiency.

Scintillometer networks for calibration and validation of energy balance and soil moisture remote sensing algorithms

NASA Astrophysics Data System (ADS)

Hendrickx, Jan M. H.; Kleissl, Jan; Gómez Vélez, Jesús D.; Hong, Sung-ho; Fábrega Duque, José R.; Vega, David; Moreno Ramírez, Hernán A.; Ogden, Fred L.

2007-04-01

Accurate estimation of sensible and latent heat fluxes as well as soil moisture from remotely sensed satellite images poses a great challenge. Yet, it is critical to face this challenge since the estimation of spatial and temporal distributions of these parameters over large areas is impossible using only ground measurements. A major difficulty for the calibration and validation of operational remote sensing methods such as SEBAL, METRIC, and ALEXI is the ground measurement of sensible heat fluxes at a scale similar to the spatial resolution of the remote sensing image. While the spatial length scale of remote sensing images covers a range from 30 m (LandSat) to 1000 m (MODIS) direct methods to measure sensible heat fluxes such as eddy covariance (EC) only provide point measurements at a scale that may be considerably smaller than the estimate obtained from a remote sensing method. The Large Aperture scintillometer (LAS) flux footprint area is larger (up to 5000 m long) and its spatial extent better constraint than that of EC systems. Therefore, scintillometers offer the unique possibility of measuring the vertical flux of sensible heat averaged over areas comparable with several pixels of a satellite image (up to about 40 Landsat thermal pixels or about 5 MODIS thermal pixels). The objective of this paper is to present our experiences with an existing network of seven scintillometers in New Mexico and a planned network of three scintillometers in the humid tropics of Panama and Colombia.

Spread spectrum phase modulation for coherent X-ray diffraction imaging.

PubMed

Zhang, Xuesong; Jiang, Jing; Xiangli, Bin; Arce, Gonzalo R

2015-09-21

High dynamic range, phase ambiguity and radiation limited resolution are three challenging issues in coherent X-ray diffraction imaging (CXDI), which limit the achievable imaging resolution. This paper proposes a spread spectrum phase modulation (SSPM) method to address the aforementioned problems in a single strobe. The requirements on phase modulator parameters are presented, and a practical implementation of SSPM is discussed via ray optics analysis. Numerical experiments demonstrate the performance of SSPM under the constraint of available X-ray optics fabrication accuracy, showing its potential to real CXDI applications.

Constraining Convection Properties with VLTI

NASA Astrophysics Data System (ADS)

Paladini, Claudia

2018-04-01

We recently imaged the stellar surface of the asymptotic giant branch (AGB) star pi1 Gruis using the PIONIER instrument mounted on the Very Large Telescope Interferometer. The three images are very little contaminated by molecular and dust opacity, and show a stellar surface characterized by large convective granulation. In this contribution I will describe the method used to derive the size of the granulation pattern, the challenges of image reconstruction, and our results. I will conclude describing shortly what the next generation of interferometric instruments will bring to our study.

Cancer heterogeneity and imaging.

PubMed

O'Connor, James P B

2017-04-01

There is interest in identifying and quantifying tumor heterogeneity at the genomic, tissue pathology and clinical imaging scales, as this may help better understand tumor biology and may yield useful biomarkers for guiding therapy-based decision making. This review focuses on the role and value of using x-ray, CT, MRI and PET based imaging methods that identify, measure and map tumor heterogeneity. In particular we highlight the potential value of these techniques and the key challenges required to validate and qualify these biomarkers for clinical use. Copyright © 2016. Published by Elsevier Ltd.

Analysis of x-ray tomography data of an extruded low density styrenic foam: an image analysis study

NASA Astrophysics Data System (ADS)

Lin, Jui-Ching; Heeschen, William

2016-10-01

Extruded styrenic foams are low density foams that are widely used for thermal insulation. It is difficult to precisely characterize the structure of the cells in low density foams by traditional cross-section viewing due to the frailty of the walls of the cells. X-ray computed tomography (CT) is a non-destructive, three dimensional structure characterization technique that has great potential for structure characterization of styrenic foams. Unfortunately the intrinsic artifacts of the data and the artifacts generated during image reconstruction are often comparable in size and shape to the thin walls of the foam, making robust and reliable analysis of cell sizes challenging. We explored three different image processing methods to clean up artifacts in the reconstructed images, thus allowing quantitative three dimensional determination of cell size in a low density styrenic foam. Three image processing approaches - an intensity based approach, an intensity variance based approach, and a machine learning based approach - are explored in this study, and the machine learning image feature classification method was shown to be the best. Individual cells are segmented within the images after the images were cleaned up using the three different methods and the cell sizes are measured and compared in the study. Although the collected data with the image analysis methods together did not yield enough measurements for a good statistic of the measurement of cell sizes, the problem can be resolved by measuring multiple samples or increasing imaging field of view.

A deep learning model integrating FCNNs and CRFs for brain tumor segmentation.

PubMed

Zhao, Xiaomei; Wu, Yihong; Song, Guidong; Li, Zhenye; Zhang, Yazhuo; Fan, Yong

2018-01-01

Accurate and reliable brain tumor segmentation is a critical component in cancer diagnosis, treatment planning, and treatment outcome evaluation. Build upon successful deep learning techniques, a novel brain tumor segmentation method is developed by integrating fully convolutional neural networks (FCNNs) and Conditional Random Fields (CRFs) in a unified framework to obtain segmentation results with appearance and spatial consistency. We train a deep learning based segmentation model using 2D image patches and image slices in following steps: 1) training FCNNs using image patches; 2) training CRFs as Recurrent Neural Networks (CRF-RNN) using image slices with parameters of FCNNs fixed; and 3) fine-tuning the FCNNs and the CRF-RNN using image slices. Particularly, we train 3 segmentation models using 2D image patches and slices obtained in axial, coronal and sagittal views respectively, and combine them to segment brain tumors using a voting based fusion strategy. Our method could segment brain images slice-by-slice, much faster than those based on image patches. We have evaluated our method based on imaging data provided by the Multimodal Brain Tumor Image Segmentation Challenge (BRATS) 2013, BRATS 2015 and BRATS 2016. The experimental results have demonstrated that our method could build a segmentation model with Flair, T1c, and T2 scans and achieve competitive performance as those built with Flair, T1, T1c, and T2 scans. Copyright © 2017 Elsevier B.V. All rights reserved.

3D/2D model-to-image registration by imitation learning for cardiac procedures.

PubMed

Toth, Daniel; Miao, Shun; Kurzendorfer, Tanja; Rinaldi, Christopher A; Liao, Rui; Mansi, Tommaso; Rhode, Kawal; Mountney, Peter

2018-05-12

In cardiac interventions, such as cardiac resynchronization therapy (CRT), image guidance can be enhanced by involving preoperative models. Multimodality 3D/2D registration for image guidance, however, remains a significant research challenge for fundamentally different image data, i.e., MR to X-ray. Registration methods must account for differences in intensity, contrast levels, resolution, dimensionality, field of view. Furthermore, same anatomical structures may not be visible in both modalities. Current approaches have focused on developing modality-specific solutions for individual clinical use cases, by introducing constraints, or identifying cross-modality information manually. Machine learning approaches have the potential to create more general registration platforms. However, training image to image methods would require large multimodal datasets and ground truth for each target application. This paper proposes a model-to-image registration approach instead, because it is common in image-guided interventions to create anatomical models for diagnosis, planning or guidance prior to procedures. An imitation learning-based method, trained on 702 datasets, is used to register preoperative models to intraoperative X-ray images. Accuracy is demonstrated on cardiac models and artificial X-rays generated from CTs. The registration error was [Formula: see text] on 1000 test cases, superior to that of manual ([Formula: see text]) and gradient-based ([Formula: see text]) registration. High robustness is shown in 19 clinical CRT cases. Besides the proposed methods feasibility in a clinical environment, evaluation has shown good accuracy and high robustness indicating that it could be applied in image-guided interventions.

An Active Patch Model for Real World Texture and Appearance Classification

PubMed Central

Mao, Junhua; Zhu, Jun; Yuille, Alan L.

2014-01-01

This paper addresses the task of natural texture and appearance classification. Our goal is to develop a simple and intuitive method that performs at state of the art on datasets ranging from homogeneous texture (e.g., material texture), to less homogeneous texture (e.g., the fur of animals), and to inhomogeneous texture (the appearance patterns of vehicles). Our method uses a bag-of-words model where the features are based on a dictionary of active patches. Active patches are raw intensity patches which can undergo spatial transformations (e.g., rotation and scaling) and adjust themselves to best match the image regions. The dictionary of active patches is required to be compact and representative, in the sense that we can use it to approximately reconstruct the images that we want to classify. We propose a probabilistic model to quantify the quality of image reconstruction and design a greedy learning algorithm to obtain the dictionary. We classify images using the occurrence frequency of the active patches. Feature extraction is fast (about 100 ms per image) using the GPU. The experimental results show that our method improves the state of the art on a challenging material texture benchmark dataset (KTH-TIPS2). To test our method on less homogeneous or inhomogeneous images, we construct two new datasets consisting of appearance image patches of animals and vehicles cropped from the PASCAL VOC dataset. Our method outperforms competing methods on these datasets. PMID:25531013

Dual-Level Security based Cyclic18 Steganographic Method and its Application for Secure Transmission of Keyframes during Wireless Capsule Endoscopy.

PubMed

Muhammad, Khan; Sajjad, Muhammad; Baik, Sung Wook

2016-05-01

In this paper, the problem of secure transmission of sensitive contents over the public network Internet is addressed by proposing a novel data hiding method in encrypted images with dual-level security. The secret information is divided into three blocks using a specific pattern, followed by an encryption mechanism based on the three-level encryption algorithm (TLEA). The input image is scrambled using a secret key, and the encrypted sub-message blocks are then embedded in the scrambled image by cyclic18 least significant bit (LSB) substitution method, utilizing LSBs and intermediate LSB planes. Furthermore, the cover image and its planes are rotated at different angles using a secret key prior to embedding, deceiving the attacker during data extraction. The usage of message blocks division, TLEA, image scrambling, and the cyclic18 LSB method results in an advanced security system, maintaining the visual transparency of resultant images and increasing the security of embedded data. In addition, employing various secret keys for image scrambling, data encryption, and data hiding using the cyclic18 LSB method makes the data recovery comparatively more challenging for attackers. Experimental results not only validate the effectiveness of the proposed framework in terms of visual quality and security compared to other state-of-the-art methods, but also suggest its feasibility for secure transmission of diagnostically important keyframes to healthcare centers and gastroenterologists during wireless capsule endoscopy.

Fault detection and isolation in the challenging Tennessee Eastman process by using image processing techniques.

PubMed

Hajihosseini, Payman; Anzehaee, Mohammad Mousavi; Behnam, Behzad

2018-05-22

The early fault detection and isolation in industrial systems is a critical factor in preventing equipment damage. In the proposed method, instead of using the time signals of sensors, the 2D image obtained by placing these signals next to each other in a matrix has been used; and then a novel fault detection and isolation procedure has been carried out based on image processing techniques. Different features including texture, wavelet transform, mean and standard deviation of the image accompanied with MLP and RBF neural networks based classifiers have been used for this purpose. Obtained results indicate the notable efficacy and success of the proposed method in detecting and isolating faults of the Tennessee Eastman benchmark process and its superiority over previous techniques. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Challenges and Opportunities for Extracting Cardiovascular Risk Biomarkers from Imaging Data

NASA Astrophysics Data System (ADS)

Kakadiaris, I. A.; Mendizabal-Ruiz, E. G.; Kurkure, U.; Naghavi, M.

Complications attributed to cardiovascular diseases (CDV) are the leading cause of death worldwide. In the United States, sudden heart attack remains the number one cause of death and accounts for the majority of the 280 billion burden of cardiovascular diseases. In spite of the advancements in cardiovascular imaging techniques, the rate of deaths due to unpredicted heart attack remains high. Thus, novel computational tools are of critical need, in order to mine quantitative parameters from the imaging data for early detection of persons with a high likelihood of developing a heart attack in the near future (vulnerable patients). In this paper, we present our progress in the research of computational methods for the extraction of cardiovascular risk biomarkers from cardiovascular imaging data. In particular, we focus on the methods developed for the analysis of intravascular ultrasound (IVUS) data.

Investigation into the need for ingesting foreign imaging exams into local systems and evaluation of the design challenges of Foreign Exam Management (FEM)

NASA Astrophysics Data System (ADS)

Milovanovic, Lazar; Agrawal, Arun; Bak, Peter; Bender, Duane; Koff, David

2015-03-01

The deployment of regional and national Electronic Health Record solutions has been a focus of many countries throughout the past decade. Most of these deployments have taken the approach of "sharing" imaging exams via portals and web-based viewers. The motivation of portal/web-based access is driven by a) the perception that review of imaging exams via portal methods is satisfactory to all users and b) the perceived complexity of ingesting foreign exams into local systems. This research project set out to objectively evaluate who really needs foreign exams within their local systems, what those systems might be and how often this is required. Working on the belief that Foreign Exam Management (FEM) is required to support clinical workflow, the project implemented a FEM capability within an XDSI. b domain to identify the design challenges and nuances associated with FEM.

Interferometric Imaging Directly with Closure Phases and Closure Amplitudes

NASA Astrophysics Data System (ADS)

Chael, Andrew A.; Johnson, Michael D.; Bouman, Katherine L.; Blackburn, Lindy L.; Akiyama, Kazunori; Narayan, Ramesh

2018-04-01

Interferometric imaging now achieves angular resolutions as fine as ∼10 μas, probing scales that are inaccessible to single telescopes. Traditional synthesis imaging methods require calibrated visibilities; however, interferometric calibration is challenging, especially at high frequencies. Nevertheless, most studies present only a single image of their data after a process of “self-calibration,” an iterative procedure where the initial image and calibration assumptions can significantly influence the final image. We present a method for efficient interferometric imaging directly using only closure amplitudes and closure phases, which are immune to station-based calibration errors. Closure-only imaging provides results that are as noncommittal as possible and allows for reconstructing an image independently from separate amplitude and phase self-calibration. While closure-only imaging eliminates some image information (e.g., the total image flux density and the image centroid), this information can be recovered through a small number of additional constraints. We demonstrate that closure-only imaging can produce high-fidelity results, even for sparse arrays such as the Event Horizon Telescope, and that the resulting images are independent of the level of systematic amplitude error. We apply closure imaging to VLBA and ALMA data and show that it is capable of matching or exceeding the performance of traditional self-calibration and CLEAN for these data sets.

An online database for plant image analysis software tools.

PubMed

Lobet, Guillaume; Draye, Xavier; Périlleux, Claire

2013-10-09

Recent years have seen an increase in methods for plant phenotyping using image analyses. These methods require new software solutions for data extraction and treatment. These solutions are instrumental in supporting various research pipelines, ranging from the localisation of cellular compounds to the quantification of tree canopies. However, due to the variety of existing tools and the lack of central repository, it is challenging for researchers to identify the software that is best suited for their research. We present an online, manually curated, database referencing more than 90 plant image analysis software solutions. The website, plant-image-analysis.org, presents each software in a uniform and concise manner enabling users to identify the available solutions for their experimental needs. The website also enables user feedback, evaluations and new software submissions. The plant-image-analysis.org database provides an overview of existing plant image analysis software. The aim of such a toolbox is to help users to find solutions, and to provide developers a way to exchange and communicate about their work.

Parallel ptychographic reconstruction

DOE PAGES

Nashed, Youssef S. G.; Vine, David J.; Peterka, Tom; ...

2014-12-19

Ptychography is an imaging method whereby a coherent beam is scanned across an object, and an image is obtained by iterative phasing of the set of diffraction patterns. It is able to be used to image extended objects at a resolution limited by scattering strength of the object and detector geometry, rather than at an optics-imposed limit. As technical advances allow larger fields to be imaged, computational challenges arise for reconstructing the correspondingly larger data volumes, yet at the same time there is also a need to deliver reconstructed images immediately so that one can evaluate the next steps tomore » take in an experiment. Here we present a parallel method for real-time ptychographic phase retrieval. It uses a hybrid parallel strategy to divide the computation between multiple graphics processing units (GPUs) and then employs novel techniques to merge sub-datasets into a single complex phase and amplitude image. Results are shown on a simulated specimen and a real dataset from an X-ray experiment conducted at a synchrotron light source.« less

Determination of toxigenic fungi and aflatoxins in nuts and dried fruits using imaging and spectroscopic techniques.

PubMed

Wu, Qifang; Xie, Lijuan; Xu, Huirong

2018-06-30

Nuts and dried fruits contain rich nutrients and are thus highly vulnerable to contamination with toxigenic fungi and aflatoxins because of poor weather, processing and storage conditions. Imaging and spectroscopic techniques have proven to be potential alternative tools to wet chemistry methods for efficient and non-destructive determination of contamination with fungi and toxins. Thus, this review provides an overview of the current developments and applications in frequently used food safety testing techniques, including near infrared spectroscopy (NIRS), mid-infrared spectroscopy (MIRS), conventional imaging techniques (colour imaging (CI) and hyperspectral imaging (HSI)), and fluorescence spectroscopy and imaging (FS/FI). Interesting classification and determination results can be found in both static and on/in-line real-time detection for contaminated nuts and dried fruits. Although these techniques offer many benefits over conventional methods, challenges remain in terms of heterogeneous distribution of toxins, background constituent interference, model robustness, detection limits, sorting efficiency, as well as instrument development. Copyright © 2018 Elsevier Ltd. All rights reserved.

A New Feedback-Based Method for Parameter Adaptation in Image Processing Routines.

PubMed

Khan, Arif Ul Maula; Mikut, Ralf; Reischl, Markus

2016-01-01

The parametrization of automatic image processing routines is time-consuming if a lot of image processing parameters are involved. An expert can tune parameters sequentially to get desired results. This may not be productive for applications with difficult image analysis tasks, e.g. when high noise and shading levels in an image are present or images vary in their characteristics due to different acquisition conditions. Parameters are required to be tuned simultaneously. We propose a framework to improve standard image segmentation methods by using feedback-based automatic parameter adaptation. Moreover, we compare algorithms by implementing them in a feedforward fashion and then adapting their parameters. This comparison is proposed to be evaluated by a benchmark data set that contains challenging image distortions in an increasing fashion. This promptly enables us to compare different standard image segmentation algorithms in a feedback vs. feedforward implementation by evaluating their segmentation quality and robustness. We also propose an efficient way of performing automatic image analysis when only abstract ground truth is present. Such a framework evaluates robustness of different image processing pipelines using a graded data set. This is useful for both end-users and experts.

Effectiveness of image features and similarity measures in cluster-based approaches for content-based image retrieval

NASA Astrophysics Data System (ADS)

Du, Hongbo; Al-Jubouri, Hanan; Sellahewa, Harin

2014-05-01

Content-based image retrieval is an automatic process of retrieving images according to image visual contents instead of textual annotations. It has many areas of application from automatic image annotation and archive, image classification and categorization to homeland security and law enforcement. The key issues affecting the performance of such retrieval systems include sensible image features that can effectively capture the right amount of visual contents and suitable similarity measures to find similar and relevant images ranked in a meaningful order. Many different approaches, methods and techniques have been developed as a result of very intensive research in the past two decades. Among many existing approaches, is a cluster-based approach where clustering methods are used to group local feature descriptors into homogeneous regions, and search is conducted by comparing the regions of the query image against those of the stored images. This paper serves as a review of works in this area. The paper will first summarize the existing work reported in the literature and then present the authors' own investigations in this field. The paper intends to highlight not only achievements made by recent research but also challenges and difficulties still remaining in this area.

A New Feedback-Based Method for Parameter Adaptation in Image Processing Routines

PubMed Central

Mikut, Ralf; Reischl, Markus

2016-01-01

The parametrization of automatic image processing routines is time-consuming if a lot of image processing parameters are involved. An expert can tune parameters sequentially to get desired results. This may not be productive for applications with difficult image analysis tasks, e.g. when high noise and shading levels in an image are present or images vary in their characteristics due to different acquisition conditions. Parameters are required to be tuned simultaneously. We propose a framework to improve standard image segmentation methods by using feedback-based automatic parameter adaptation. Moreover, we compare algorithms by implementing them in a feedforward fashion and then adapting their parameters. This comparison is proposed to be evaluated by a benchmark data set that contains challenging image distortions in an increasing fashion. This promptly enables us to compare different standard image segmentation algorithms in a feedback vs. feedforward implementation by evaluating their segmentation quality and robustness. We also propose an efficient way of performing automatic image analysis when only abstract ground truth is present. Such a framework evaluates robustness of different image processing pipelines using a graded data set. This is useful for both end-users and experts. PMID:27764213

White blood cell segmentation by color-space-based k-means clustering.

PubMed

Zhang, Congcong; Xiao, Xiaoyan; Li, Xiaomei; Chen, Ying-Jie; Zhen, Wu; Chang, Jun; Zheng, Chengyun; Liu, Zhi

2014-09-01

White blood cell (WBC) segmentation, which is important for cytometry, is a challenging issue because of the morphological diversity of WBCs and the complex and uncertain background of blood smear images. This paper proposes a novel method for the nucleus and cytoplasm segmentation of WBCs for cytometry. A color adjustment step was also introduced before segmentation. Color space decomposition and k-means clustering were combined for segmentation. A database including 300 microscopic blood smear images were used to evaluate the performance of our method. The proposed segmentation method achieves 95.7% and 91.3% overall accuracy for nucleus segmentation and cytoplasm segmentation, respectively. Experimental results demonstrate that the proposed method can segment WBCs effectively with high accuracy.

Deep visual-semantic for crowded video understanding

NASA Astrophysics Data System (ADS)

Deng, Chunhua; Zhang, Junwen

2018-03-01

Visual-semantic features play a vital role for crowded video understanding. Convolutional Neural Networks (CNNs) have experienced a significant breakthrough in learning representations from images. However, the learning of visualsemantic features, and how it can be effectively extracted for video analysis, still remains a challenging task. In this study, we propose a novel visual-semantic method to capture both appearance and dynamic representations. In particular, we propose a spatial context method, based on the fractional Fisher vector (FV) encoding on CNN features, which can be regarded as our main contribution. In addition, to capture temporal context information, we also applied fractional encoding method on dynamic images. Experimental results on the WWW crowed video dataset demonstrate that the proposed method outperform the state of the art.

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.

Image retrieval for identifying house plants

NASA Astrophysics Data System (ADS)

Kebapci, Hanife; Yanikoglu, Berrin; Unal, Gozde

2010-02-01

We present a content-based image retrieval system for plant identification which is intended for providing users with a simple method to locate information about their house plants. A plant image consists of a collection of overlapping leaves and possibly flowers, which makes the problem challenging. We studied the suitability of various well-known color, texture and shape features for this problem, as well as introducing some new ones. The features are extracted from the general plant region that is segmented from the background using the max-flow min-cut technique. Results on a database of 132 different plant images show promise (in about 72% of the queries, the correct plant image is retrieved among the top-15 results).

Detection of foreign body using fast thermoacoustic tomography with a multielement linear transducer array

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

Nie Liming; Xing Da; Yang Diwu

2007-04-23

Current imaging modalities face challenges in clinical applications due to limitations in resolution or contrast. Microwave-induced thermoacoustic imaging may provide a complementary modality for medical imaging, particularly for detecting foreign objects due to their different absorption of electromagnetic radiation at specific frequencies. A thermoacoustic tomography system with a multielement linear transducer array was developed and used to detect foreign objects in tissue. Radiography and thermoacoustic images of objects with different electromagnetic properties, including glass, sand, and iron, were compared. The authors' results demonstrate that thermoacoustic imaging has the potential to become a fast method for surgical localization of occult foreignmore » objects.« less

Multiscale hidden Markov models for photon-limited imaging

NASA Astrophysics Data System (ADS)

Nowak, Robert D.

1999-06-01

Photon-limited image analysis is often hindered by low signal-to-noise ratios. A novel Bayesian multiscale modeling and analysis method is developed in this paper to assist in these challenging situations. In addition to providing a very natural and useful framework for modeling an d processing images, Bayesian multiscale analysis is often much less computationally demanding compared to classical Markov random field models. This paper focuses on a probabilistic graph model called the multiscale hidden Markov model (MHMM), which captures the key inter-scale dependencies present in natural image intensities. The MHMM framework presented here is specifically designed for photon-limited imagin applications involving Poisson statistics, and applications to image intensity analysis are examined.

Progressive Band Selection

NASA Technical Reports Server (NTRS)

Fisher, Kevin; Chang, Chein-I

2009-01-01

Progressive band selection (PBS) reduces spectral redundancy without significant loss of information, thereby reducing hyperspectral image data volume and processing time. Used onboard a spacecraft, it can also reduce image downlink time. PBS prioritizes an image's spectral bands according to priority scores that measure their significance to a specific application. Then it uses one of three methods to select an appropriate number of the most useful bands. Key challenges for PBS include selecting an appropriate criterion to generate band priority scores, and determining how many bands should be retained in the reduced image. The image's Virtual Dimensionality (VD), once computed, is a reasonable estimate of the latter. We describe the major design details of PBS and test PBS in a land classification experiment.

Color engineering in the age of digital convergence

NASA Astrophysics Data System (ADS)

MacDonald, Lindsay W.

1998-09-01

Digital color imaging has developed over the past twenty years from specialized scientific applications into the mainstream of computing. In addition to the phenomenal growth of computer processing power and storage capacity, great advances have been made in the capabilities and cost-effectiveness of color imaging peripherals. The majority of imaging applications, including the graphic arts, video and film have made the transition from analogue to digital production methods. Digital convergence of computing, communications and television now heralds new possibilities for multimedia publishing and mobile lifestyles. Color engineering, the application of color science to the design of imaging products, is an emerging discipline that poses exciting challenges to the international color imaging community for training, research and standards.

Cellular image segmentation using n-agent cooperative game theory

NASA Astrophysics Data System (ADS)

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

2016-03-01

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

First in vivo traumatic brain injury imaging via magnetic particle imaging

NASA Astrophysics Data System (ADS)

Orendorff, Ryan; Peck, Austin J.; Zheng, Bo; Shirazi, Shawn N.; Ferguson, R. Matthew; Khandhar, Amit P.; Kemp, Scott J.; Goodwill, Patrick; Krishnan, Kannan M.; Brooks, George A.; Kaufer, Daniela; Conolly, Steven

2017-05-01

Emergency room visits due to traumatic brain injury (TBI) is common, but classifying the severity of the injury remains an open challenge. Some subjective methods such as the Glasgow Coma Scale attempt to classify traumatic brain injuries, as well as some imaging based modalities such as computed tomography and magnetic resonance imaging. However, to date it is still difficult to detect and monitor mild to moderate injuries. In this report, we demonstrate that the magnetic particle imaging (MPI) modality can be applied to imaging TBI events with excellent contrast. MPI can monitor injected iron nanoparticles over long time scales without signal loss, allowing researchers and clinicians to monitor the change in blood pools as the wound heals.

Effective evaluation of privacy protection techniques in visible and thermal imagery

NASA Astrophysics Data System (ADS)

Nawaz, Tahir; Berg, Amanda; Ferryman, James; Ahlberg, Jörgen; Felsberg, Michael

2017-09-01

Privacy protection may be defined as replacing the original content in an image region with a (less intrusive) content having modified target appearance information to make it less recognizable by applying a privacy protection technique. Indeed, the development of privacy protection techniques also needs to be complemented with an established objective evaluation method to facilitate their assessment and comparison. Generally, existing evaluation methods rely on the use of subjective judgments or assume a specific target type in image data and use target detection and recognition accuracies to assess privacy protection. An annotation-free evaluation method that is neither subjective nor assumes a specific target type is proposed. It assesses two key aspects of privacy protection: "protection" and "utility." Protection is quantified as an appearance similarity, and utility is measured as a structural similarity between original and privacy-protected image regions. We performed an extensive experimentation using six challenging datasets (having 12 video sequences), including a new dataset (having six sequences) that contains visible and thermal imagery. The new dataset is made available online for the community. We demonstrate effectiveness of the proposed method by evaluating six image-based privacy protection techniques and also show comparisons of the proposed method over existing methods.

Laser speckle imaging of rat retinal blood flow with hybrid temporal and spatial analysis method

NASA Astrophysics Data System (ADS)

Cheng, Haiying; Yan, Yumei; Duong, Timothy Q.

2009-02-01

Noninvasive monitoring of blood flow in retinal circulation will reveal the progression and treatment of ocular disorders, such as diabetic retinopathy, age-related macular degeneration and glaucoma. A non-invasive and direct BF measurement technique with high spatial-temporal resolution is needed for retinal imaging. Laser speckle imaging (LSI) is such a method. Currently, there are two analysis methods for LSI: spatial statistics LSI (SS-LSI) and temporal statistical LSI (TS-LSI). Comparing these two analysis methods, SS-LSI has higher signal to noise ratio (SNR) and TSLSI is less susceptible to artifacts from stationary speckle. We proposed a hybrid temporal and spatial analysis method (HTS-LSI) to measure the retinal blood flow. Gas challenge experiment was performed and images were analyzed by HTS-LSI. Results showed that HTS-LSI can not only remove the stationary speckle but also increase the SNR. Under 100% O2, retinal BF decreased by 20-30%. This was consistent with the results observed with laser Doppler technique. As retinal blood flow is a critical physiological parameter and its perturbation has been implicated in the early stages of many retinal diseases, HTS-LSI will be an efficient method in early detection of retina diseases.

SWT voting-based color reduction for text detection in natural scene images

NASA Astrophysics Data System (ADS)

Ikica, Andrej; Peer, Peter

2013-12-01

In this article, we propose a novel stroke width transform (SWT) voting-based color reduction method for detecting text in natural scene images. Unlike other text detection approaches that mostly rely on either text structure or color, the proposed method combines both by supervising text-oriented color reduction process with additional SWT information. SWT pixels mapped to color space vote in favor of the color they correspond to. Colors receiving high SWT vote most likely belong to text areas and are blocked from being mean-shifted away. Literature does not explicitly address SWT search direction issue; thus, we propose an adaptive sub-block method for determining correct SWT direction. Both SWT voting-based color reduction and SWT direction determination methods are evaluated on binary (text/non-text) images obtained from a challenging Computer Vision Lab optical character recognition database. SWT voting-based color reduction method outperforms the state-of-the-art text-oriented color reduction approach.

A segmentation and classification scheme for single tooth in MicroCT images based on 3D level set and k-means+.

PubMed

Wang, Liansheng; Li, Shusheng; Chen, Rongzhen; Liu, Sze-Yu; Chen, Jyh-Cheng

2017-04-01

Accurate classification of different anatomical structures of teeth from medical images provides crucial information for the stress analysis in dentistry. Usually, the anatomical structures of teeth are manually labeled by experienced clinical doctors, which is time consuming. However, automatic segmentation and classification is a challenging task because the anatomical structures and surroundings of the tooth in medical images are rather complex. Therefore, in this paper, we propose an effective framework which is designed to segment the tooth with a Selective Binary and Gaussian Filtering Regularized Level Set (GFRLS) method improved by fully utilizing 3 dimensional (3D) information, and classify the tooth by employing unsupervised learning i.e., k-means++ method. In order to evaluate the proposed method, the experiments are conducted on the sufficient and extensive datasets of mandibular molars. The experimental results show that our method can achieve higher accuracy and robustness compared to other three clustering methods. Copyright © 2016 Elsevier Ltd. All rights reserved.

Image segmentation and dynamic lineage analysis in single-cell fluorescence microscopy.

PubMed

Wang, Quanli; Niemi, Jarad; Tan, Chee-Meng; You, Lingchong; West, Mike

2010-01-01

An increasingly common component of studies in synthetic and systems biology is analysis of dynamics of gene expression at the single-cell level, a context that is heavily dependent on the use of time-lapse movies. Extracting quantitative data on the single-cell temporal dynamics from such movies remains a major challenge. Here, we describe novel methods for automating key steps in the analysis of single-cell, fluorescent images-segmentation and lineage reconstruction-to recognize and track individual cells over time. The automated analysis iteratively combines a set of extended morphological methods for segmentation, and uses a neighborhood-based scoring method for frame-to-frame lineage linking. Our studies with bacteria, budding yeast and human cells, demonstrate the portability and usability of these methods, whether using phase, bright field or fluorescent images. These examples also demonstrate the utility of our integrated approach in facilitating analyses of engineered and natural cellular networks in diverse settings. The automated methods are implemented in freely available, open-source software.

Semi-automatic breast ultrasound image segmentation based on mean shift and graph cuts.

PubMed

Zhou, Zhuhuang; Wu, Weiwei; Wu, Shuicai; Tsui, Po-Hsiang; Lin, Chung-Chih; Zhang, Ling; Wang, Tianfu

2014-10-01

Computerized tumor segmentation on breast ultrasound (BUS) images remains a challenging task. In this paper, we proposed a new method for semi-automatic tumor segmentation on BUS images using Gaussian filtering, histogram equalization, mean shift, and graph cuts. The only interaction required was to select two diagonal points to determine a region of interest (ROI) on an input image. The ROI image was shrunken by a factor of 2 using bicubic interpolation to reduce computation time. The shrunken image was smoothed by a Gaussian filter and then contrast-enhanced by histogram equalization. Next, the enhanced image was filtered by pyramid mean shift to improve homogeneity. The object and background seeds for graph cuts were automatically generated on the filtered image. Using these seeds, the filtered image was then segmented by graph cuts into a binary image containing the object and background. Finally, the binary image was expanded by a factor of 2 using bicubic interpolation, and the expanded image was processed by morphological opening and closing to refine the tumor contour. The method was implemented with OpenCV 2.4.3 and Visual Studio 2010 and tested for 38 BUS images with benign tumors and 31 BUS images with malignant tumors from different ultrasound scanners. Experimental results showed that our method had a true positive rate (TP) of 91.7%, a false positive (FP) rate of 11.9%, and a similarity (SI) rate of 85.6%. The mean run time on Intel Core 2.66 GHz CPU and 4 GB RAM was 0.49 ± 0.36 s. The experimental results indicate that the proposed method may be useful in BUS image segmentation. © The Author(s) 2014.

Automatic bone outer contour extraction from B-modes ultrasound images based on local phase symmetry and quadratic polynomial fitting

NASA Astrophysics Data System (ADS)

Karlita, Tita; Yuniarno, Eko Mulyanto; Purnama, I. Ketut Eddy; Purnomo, Mauridhi Hery

2017-06-01

Analyzing ultrasound (US) images to get the shapes and structures of particular anatomical regions is an interesting field of study since US imaging is a non-invasive method to capture internal structures of a human body. However, bone segmentation of US images is still challenging because it is strongly influenced by speckle noises and it has poor image quality. This paper proposes a combination of local phase symmetry and quadratic polynomial fitting methods to extract bone outer contour (BOC) from two dimensional (2D) B-modes US image as initial steps of three-dimensional (3D) bone surface reconstruction. By using local phase symmetry, the bone is initially extracted from US images. BOC is then extracted by scanning one pixel on the bone boundary in each column of the US images using first phase features searching method. Quadratic polynomial fitting is utilized to refine and estimate the pixel location that fails to be detected during the extraction process. Hole filling method is then applied by utilize the polynomial coefficients to fill the gaps with new pixel. The proposed method is able to estimate the new pixel position and ensures smoothness and continuity of the contour path. Evaluations are done using cow and goat bones by comparing the resulted BOCs with the contours produced by manual segmentation and contours produced by canny edge detection. The evaluation shows that our proposed methods produces an excellent result with average MSE before and after hole filling at the value of 0.65.

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

PubMed

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

2018-04-15

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

Temporal Coding of Volumetric Imagery

NASA Astrophysics Data System (ADS)

Llull, Patrick Ryan

'Image volumes' refer to realizations of images in other dimensions such as time, spectrum, and focus. Recent advances in scientific, medical, and consumer applications demand improvements in image volume capture. Though image volume acquisition continues to advance, it maintains the same sampling mechanisms that have been used for decades; every voxel must be scanned and is presumed independent of its neighbors. Under these conditions, improving performance comes at the cost of increased system complexity, data rates, and power consumption. This dissertation explores systems and methods capable of efficiently improving sensitivity and performance for image volume cameras, and specifically proposes several sampling strategies that utilize temporal coding to improve imaging system performance and enhance our awareness for a variety of dynamic applications. Video cameras and camcorders sample the video volume (x,y,t) at fixed intervals to gain understanding of the volume's temporal evolution. Conventionally, one must reduce the spatial resolution to increase the framerate of such cameras. Using temporal coding via physical translation of an optical element known as a coded aperture, the compressive temporal imaging (CACTI) camera emonstrates a method which which to embed the temporal dimension of the video volume into spatial (x,y) measurements, thereby greatly improving temporal resolution with minimal loss of spatial resolution. This technique, which is among a family of compressive sampling strategies developed at Duke University, temporally codes the exposure readout functions at the pixel level. Since video cameras nominally integrate the remaining image volume dimensions (e.g. spectrum and focus) at capture time, spectral (x,y,t,lambda) and focal (x,y,t,z) image volumes are traditionally captured via sequential changes to the spectral and focal state of the system, respectively. The CACTI camera's ability to embed video volumes into images leads to exploration of other information within that video; namely, focal and spectral information. The next part of the thesis demonstrates derivative works of CACTI: compressive extended depth of field and compressive spectral-temporal imaging. These works successfully show the technique's extension of temporal coding to improve sensing performance in these other dimensions. Geometrical optics-related tradeoffs, such as the classic challenges of wide-field-of-view and high resolution photography, have motivated the development of mulitscale camera arrays. The advent of such designs less than a decade ago heralds a new era of research- and engineering-related challenges. One significant challenge is that of managing the focal volume (x,y,z ) over wide fields of view and resolutions. The fourth chapter shows advances on focus and image quality assessment for a class of multiscale gigapixel cameras developed at Duke. Along the same line of work, we have explored methods for dynamic and adaptive addressing of focus via point spread function engineering. We demonstrate another form of temporal coding in the form of physical translation of the image plane from its nominal focal position. We demonstrate this technique's capability to generate arbitrary point spread functions.

Cell segmentation in phase contrast microscopy images via semi-supervised classification over optics-related features.

PubMed

Su, Hang; Yin, Zhaozheng; Huh, Seungil; Kanade, Takeo

2013-10-01

Phase-contrast microscopy is one of the most common and convenient imaging modalities to observe long-term multi-cellular processes, which generates images by the interference of lights passing through transparent specimens and background medium with different retarded phases. Despite many years of study, computer-aided phase contrast microscopy analysis on cell behavior is challenged by image qualities and artifacts caused by phase contrast optics. Addressing the unsolved challenges, the authors propose (1) a phase contrast microscopy image restoration method that produces phase retardation features, which are intrinsic features of phase contrast microscopy, and (2) a semi-supervised learning based algorithm for cell segmentation, which is a fundamental task for various cell behavior analysis. Specifically, the image formation process of phase contrast microscopy images is first computationally modeled with a dictionary of diffraction patterns; as a result, each pixel of a phase contrast microscopy image is represented by a linear combination of the bases, which we call phase retardation features. Images are then partitioned into phase-homogeneous atoms by clustering neighboring pixels with similar phase retardation features. Consequently, cell segmentation is performed via a semi-supervised classification technique over the phase-homogeneous atoms. Experiments demonstrate that the proposed approach produces quality segmentation of individual cells and outperforms previous approaches. Copyright © 2013 Elsevier B.V. All rights reserved.

A Bayesian Framework for Human Body Pose Tracking from Depth Image Sequences

PubMed Central

Zhu, Youding; Fujimura, Kikuo

2010-01-01

This paper addresses the problem of accurate and robust tracking of 3D human body pose from depth image sequences. Recovering the large number of degrees of freedom in human body movements from a depth image sequence is challenging due to the need to resolve the depth ambiguity caused by self-occlusions and the difficulty to recover from tracking failure. Human body poses could be estimated through model fitting using dense correspondences between depth data and an articulated human model (local optimization method). Although it usually achieves a high accuracy due to dense correspondences, it may fail to recover from tracking failure. Alternately, human pose may be reconstructed by detecting and tracking human body anatomical landmarks (key-points) based on low-level depth image analysis. While this method (key-point based method) is robust and recovers from tracking failure, its pose estimation accuracy depends solely on image-based localization accuracy of key-points. To address these limitations, we present a flexible Bayesian framework for integrating pose estimation results obtained by methods based on key-points and local optimization. Experimental results are shown and performance comparison is presented to demonstrate the effectiveness of the proposed approach. PMID:22399933

Effective Clipart Image Vectorization through Direct Optimization of Bezigons.

PubMed

Yang, Ming; Chao, Hongyang; Zhang, Chi; Guo, Jun; Yuan, Lu; Sun, Jian

2016-02-01

Bezigons, i.e., closed paths composed of Bézier curves, have been widely employed to describe shapes in image vectorization results. However, most existing vectorization techniques infer the bezigons by simply approximating an intermediate vector representation (such as polygons). Consequently, the resultant bezigons are sometimes imperfect due to accumulated errors, fitting ambiguities, and a lack of curve priors, especially for low-resolution images. In this paper, we describe a novel method for vectorizing clipart images. In contrast to previous methods, we directly optimize the bezigons rather than using other intermediate representations; therefore, the resultant bezigons are not only of higher fidelity compared with the original raster image but also more reasonable because they were traced by a proficient expert. To enable such optimization, we have overcome several challenges and have devised a differentiable data energy as well as several curve-based prior terms. To improve the efficiency of the optimization, we also take advantage of the local control property of bezigons and adopt an overlapped piecewise optimization strategy. The experimental results show that our method outperforms both the current state-of-the-art method and commonly used commercial software in terms of bezigon quality.

Science, Technical Innovation and Applications in Bioacoustics: Summary of a Workshop

DTIC Science & Technology

2004-07-01

binaural processing have been neglected. From a signal-processing standpoint, we should avoid complex computational methods and instead use massively...design and/or build transducers or arrays with anywhere near the performance and, most importantly, environmental adaptability of animal binaural ...shell Small animal imaging Cardiac Imaging in Mice The Challenge Mouse heart • 7mm diameter • 8 beats /sec Mouse Heart L16-28MHzL5-10MHz Laptop

Advanced Cell Classifier: User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging Data.

PubMed

Piccinini, Filippo; Balassa, Tamas; Szkalisity, Abel; Molnar, Csaba; Paavolainen, Lassi; Kujala, Kaisa; Buzas, Krisztina; Sarazova, Marie; Pietiainen, Vilja; Kutay, Ulrike; Smith, Kevin; Horvath, Peter

2017-06-28

High-content, imaging-based screens now routinely generate data on a scale that precludes manual verification and interrogation. Software applying machine learning has become an essential tool to automate analysis, but these methods require annotated examples to learn from. Efficiently exploring large datasets to find relevant examples remains a challenging bottleneck. Here, we present Advanced Cell Classifier (ACC), a graphical software package for phenotypic analysis that addresses these difficulties. ACC applies machine-learning and image-analysis methods to high-content data generated by large-scale, cell-based experiments. It features methods to mine microscopic image data, discover new phenotypes, and improve recognition performance. We demonstrate that these features substantially expedite the training process, successfully uncover rare phenotypes, and improve the accuracy of the analysis. ACC is extensively documented, designed to be user-friendly for researchers without machine-learning expertise, and distributed as a free open-source tool at www.cellclassifier.org. Copyright © 2017 Elsevier Inc. All rights reserved.

Imaging Intratumor Heterogeneity: Role in Therapy Response, Resistance, and Clinical Outcome

PubMed Central

O’Connor, James P.B.; Rose, Chris J.; Waterton, John C.; Carano, Richard A.D.; Parker, Geoff J.M.; Jackson, Alan

2014-01-01

Tumors exhibit genomic and phenotypic heterogeneity which has prognostic significance and may influence response to therapy. Imaging can quantify the spatial variation in architecture and function of individual tumors through quantifying basic biophysical parameters such as density or MRI signal relaxation rate; through measurements of blood flow, hypoxia, metabolism, cell death and other phenotypic features; and through mapping the spatial distribution of biochemical pathways and cell signaling networks. These methods can establish whether one tumor is more or less heterogeneous than another and can identify sub-regions with differing biology. In this article we review the image analysis methods currently used to quantify spatial heterogeneity within tumors. We discuss how analysis of intratumor heterogeneity can provide benefit over more simple biomarkers such as tumor size and average function. We consider how imaging methods can be integrated with genomic and pathology data, rather than be developed in isolation. Finally, we identify the challenges that must be overcome before measurements of intratumoral heterogeneity can be used routinely to guide patient care. PMID:25421725

Discriminative feature representation: an effective postprocessing solution to low dose CT imaging

NASA Astrophysics Data System (ADS)

Chen, Yang; Liu, Jin; Hu, Yining; Yang, Jian; Shi, Luyao; Shu, Huazhong; Gui, Zhiguo; Coatrieux, Gouenou; Luo, Limin

2017-03-01

This paper proposes a concise and effective approach termed discriminative feature representation (DFR) for low dose computerized tomography (LDCT) image processing, which is currently a challenging problem in medical imaging field. This DFR method assumes LDCT images as the superposition of desirable high dose CT (HDCT) 3D features and undesirable noise-artifact 3D features (the combined term of noise and artifact features induced by low dose scan protocols), and the decomposed HDCT features are used to provide the processed LDCT images with higher quality. The target HDCT features are solved via the DFR algorithm using a featured dictionary composed by atoms representing HDCT features and noise-artifact features. In this study, the featured dictionary is efficiently built using physical phantom images collected from the same CT scanner as the target clinical LDCT images to process. The proposed DFR method also has good robustness in parameter setting for different CT scanner types. This DFR method can be directly applied to process DICOM formatted LDCT images, and has good applicability to current CT systems. Comparative experiments with abdomen LDCT data validate the good performance of the proposed approach. This research was supported by National Natural Science Foundation under grants (81370040, 81530060), the Fundamental Research Funds for the Central Universities, and the Qing Lan Project in Jiangsu Province.

Probabilistic image modeling with an extended chain graph for human activity recognition and image segmentation.

PubMed

Zhang, Lei; Zeng, Zhi; Ji, Qiang

2011-09-01

Chain graph (CG) is a hybrid probabilistic graphical model (PGM) capable of modeling heterogeneous relationships among random variables. So far, however, its application in image and video analysis is very limited due to lack of principled learning and inference methods for a CG of general topology. To overcome this limitation, we introduce methods to extend the conventional chain-like CG model to CG model with more general topology and the associated methods for learning and inference in such a general CG model. Specifically, we propose techniques to systematically construct a generally structured CG, to parameterize this model, to derive its joint probability distribution, to perform joint parameter learning, and to perform probabilistic inference in this model. To demonstrate the utility of such an extended CG, we apply it to two challenging image and video analysis problems: human activity recognition and image segmentation. The experimental results show improved performance of the extended CG model over the conventional directed or undirected PGMs. This study demonstrates the promise of the extended CG for effective modeling and inference of complex real-world problems.

OCT despeckling via weighted nuclear norm constrained non-local low-rank representation

NASA Astrophysics Data System (ADS)

Tang, Chang; Zheng, Xiao; Cao, Lijuan

2017-10-01

As a non-invasive imaging modality, optical coherence tomography (OCT) plays an important role in medical sciences. However, OCT images are always corrupted by speckle noise, which can mask image features and pose significant challenges for medical analysis. In this work, we propose an OCT despeckling method by using non-local, low-rank representation with weighted nuclear norm constraint. Unlike previous non-local low-rank representation based OCT despeckling methods, we first generate a guidance image to improve the non-local group patches selection quality, then a low-rank optimization model with a weighted nuclear norm constraint is formulated to process the selected group patches. The corrupted probability of each pixel is also integrated into the model as a weight to regularize the representation error term. Note that each single patch might belong to several groups, hence different estimates of each patch are aggregated to obtain its final despeckled result. Both qualitative and quantitative experimental results on real OCT images show the superior performance of the proposed method compared with other state-of-the-art speckle removal techniques.

Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome.

PubMed

O'Connor, James P B; Rose, Chris J; Waterton, John C; Carano, Richard A D; Parker, Geoff J M; Jackson, Alan

2015-01-15

Tumors exhibit genomic and phenotypic heterogeneity, which has prognostic significance and may influence response to therapy. Imaging can quantify the spatial variation in architecture and function of individual tumors through quantifying basic biophysical parameters such as CT density or MRI signal relaxation rate; through measurements of blood flow, hypoxia, metabolism, cell death, and other phenotypic features; and through mapping the spatial distribution of biochemical pathways and cell signaling networks using PET, MRI, and other emerging molecular imaging techniques. These methods can establish whether one tumor is more or less heterogeneous than another and can identify subregions with differing biology. In this article, we review the image analysis methods currently used to quantify spatial heterogeneity within tumors. We discuss how analysis of intratumor heterogeneity can provide benefit over more simple biomarkers such as tumor size and average function. We consider how imaging methods can be integrated with genomic and pathology data, instead of being developed in isolation. Finally, we identify the challenges that must be overcome before measurements of intratumoral heterogeneity can be used routinely to guide patient care. ©2014 American Association for Cancer Research.

QR code based noise-free optical encryption and decryption of a gray scale image

NASA Astrophysics Data System (ADS)

Jiao, Shuming; Zou, Wenbin; Li, Xia

2017-03-01

In optical encryption systems, speckle noise is one major challenge in obtaining high quality decrypted images. This problem can be addressed by employing a QR code based noise-free scheme. Previous works have been conducted for optically encrypting a few characters or a short expression employing QR codes. This paper proposes a practical scheme for optically encrypting and decrypting a gray-scale image based on QR codes for the first time. The proposed scheme is compatible with common QR code generators and readers. Numerical simulation results reveal the proposed method can encrypt and decrypt an input image correctly.

Homographic Patch Feature Transform: A Robustness Registration for Gastroscopic Surgery.

PubMed

Hu, Weiling; Zhang, Xu; Wang, Bin; Liu, Jiquan; Duan, Huilong; Dai, Ning; Si, Jianmin

2016-01-01

Image registration is a key component of computer assistance in image guided surgery, and it is a challenging topic in endoscopic environments. In this study, we present a method for image registration named Homographic Patch Feature Transform (HPFT) to match gastroscopic images. HPFT can be used for tracking lesions and augmenting reality applications during gastroscopy. Furthermore, an overall evaluation scheme is proposed to validate the precision, robustness and uniformity of the registration results, which provides a standard for rejection of false matching pairs from corresponding results. Finally, HPFT is applied for processing in vivo gastroscopic data. The experimental results show that HPFT has stable performance in gastroscopic applications.

Seamless Image Mosaicking via Synchronization

NASA Astrophysics Data System (ADS)

Santellani, E.; Maset, E.; Fusiello, A.

2018-05-01

This paper proposes an innovative method to create high-quality seamless planar mosaics. The developed pipeline ensures good robustness against many common mosaicking problems (e.g., misalignments, colour distortion, moving objects, parallax) and differs from other works in the literature because a global approach, known as synchronization, is used for image registration and colour correction. To better conceal the mosaic seamlines, images are cut along specific paths, computed using a Voronoi decomposition of the mosaic area and a shortest path algorithm. Results obtained on challenging real datasets show that the colour correction mitigates significantly the colour variations between the original images and the seams on the final mosaic are not evident.

Hybrid-modality high-resolution imaging: for diagnostic biomedical imaging and sensing for disease diagnosis

NASA Astrophysics Data System (ADS)

Murukeshan, Vadakke M.; Hoong Ta, Lim

2014-11-01

Medical diagnostics in the recent past has seen the challenging trend to come up with dual and multi-modality imaging for implementing better diagnostic procedures. The changes in tissues in the early disease stages are often subtle and can occur beneath the tissue surface. In most of these cases, conventional types of medical imaging using optics may not be able to detect these changes easily due to its penetration depth of the orders of 1 mm. Each imaging modality has its own advantages and limitations, and the use of a single modality is not suitable for every diagnostic applications. Therefore the need for multi or hybrid-modality imaging arises. Combining more than one imaging modalities overcomes the limitation of individual imaging method and integrates the respective advantages into a single setting. In this context, this paper will be focusing on the research and development of two multi-modality imaging platforms. The first platform combines ultrasound and photoacoustic imaging for diagnostic applications in the eye. The second platform consists of optical hyperspectral and photoacoustic imaging for diagnostic applications in the colon. Photoacoustic imaging is used as one of the modalities in both platforms as it can offer deeper penetration depth compared to optical imaging. The optical engineering and research challenges in developing the dual/multi-modality platforms will be discussed, followed by initial results validating the proposed scheme. The proposed schemes offer high spatial and spectral resolution imaging and sensing, and is expected to offer potential biomedical imaging solutions in the near future.

Total body photography for skin cancer screening.

PubMed

Dengel, Lynn T; Petroni, Gina R; Judge, Joshua; Chen, David; Acton, Scott T; Schroen, Anneke T; Slingluff, Craig L

2015-11-01

Total body photography may aid in melanoma screening but is not widely applied due to time and cost. We hypothesized that a near-simultaneous automated skin photo-acquisition system would be acceptable to patients and could rapidly obtain total body images that enable visualization of pigmented skin lesions. From February to May 2009, a study of 20 volunteers was performed at the University of Virginia to test a prototype 16-camera imaging booth built by the research team and to guide development of special purpose software. For each participant, images were obtained before and after marking 10 lesions (five "easy" and five "difficult"), and images were evaluated to estimate visualization rates. Imaging logistical challenges were scored by the operator, and participant opinion was assessed by questionnaire. Average time for image capture was three minutes (range 2-5). All 55 "easy" lesions were visualized (sensitivity 100%, 90% CI 95-100%), and 54/55 "difficult" lesions were visualized (sensitivity 98%, 90% CI 92-100%). Operators and patients graded the imaging process favorably, with challenges identified regarding lighting and positioning. Rapid-acquisition automated skin photography is feasible with a low-cost system, with excellent lesion visualization and participant acceptance. These data provide a basis for employing this method in clinical melanoma screening. © 2014 The International Society of Dermatology.

An Assessment of Imaging Informatics for Precision Medicine in Cancer.

PubMed

Chennubhotla, C; Clarke, L P; Fedorov, A; Foran, D; Harris, G; Helton, E; Nordstrom, R; Prior, F; Rubin, D; Saltz, J H; Shalley, E; Sharma, A

2017-08-01

Objectives: Precision medicine requires the measurement, quantification, and cataloging of medical characteristics to identify the most effective medical intervention. However, the amount of available data exceeds our current capacity to extract meaningful information. We examine the informatics needs to achieve precision medicine from the perspective of quantitative imaging and oncology. Methods: The National Cancer Institute (NCI) organized several workshops on the topic of medical imaging and precision medicine. The observations and recommendations are summarized herein. Results: Recommendations include: use of standards in data collection and clinical correlates to promote interoperability; data sharing and validation of imaging tools; clinician's feedback in all phases of research and development; use of open-source architecture to encourage reproducibility and reusability; use of challenges which simulate real-world situations to incentivize innovation; partnership with industry to facilitate commercialization; and education in academic communities regarding the challenges involved with translation of technology from the research domain to clinical utility and the benefits of doing so. Conclusions: This article provides a survey of the role and priorities for imaging informatics to help advance quantitative imaging in the era of precision medicine. While these recommendations were drawn from oncology, they are relevant and applicable to other clinical domains where imaging aids precision medicine. Georg Thieme Verlag KG Stuttgart.

a New Color Correction Method for Underwater Imaging

NASA Astrophysics Data System (ADS)

Bianco, G.; Muzzupappa, M.; Bruno, F.; Garcia, R.; Neumann, L.

2015-04-01

Recovering correct or at least realistic colors of underwater scenes is a very challenging issue for imaging techniques, since illumination conditions in a refractive and turbid medium as the sea are seriously altered. The need to correct colors of underwater images or videos is an important task required in all image-based applications like 3D imaging, navigation, documentation, etc. Many imaging enhancement methods have been proposed in literature for these purposes. The advantage of these methods is that they do not require the knowledge of the medium physical parameters while some image adjustments can be performed manually (as histogram stretching) or automatically by algorithms based on some criteria as suggested from computational color constancy methods. One of the most popular criterion is based on gray-world hypothesis, which assumes that the average of the captured image should be gray. An interesting application of this assumption is performed in the Ruderman opponent color space lαβ, used in a previous work for hue correction of images captured under colored light sources, which allows to separate the luminance component of the scene from its chromatic components. In this work, we present the first proposal for color correction of underwater images by using lαβ color space. In particular, the chromatic components are changed moving their distributions around the white point (white balancing) and histogram cutoff and stretching of the luminance component is performed to improve image contrast. The experimental results demonstrate the effectiveness of this method under gray-world assumption and supposing uniform illumination of the scene. Moreover, due to its low computational cost it is suitable for real-time implementation.

Deep Learning for Classification of Colorectal Polyps on Whole-slide Images

PubMed Central

Korbar, Bruno; Olofson, Andrea M.; Miraflor, Allen P.; Nicka, Catherine M.; Suriawinata, Matthew A.; Torresani, Lorenzo; Suriawinata, Arief A.; Hassanpour, Saeed

2017-01-01

Context: Histopathological characterization of colorectal polyps is critical for determining the risk of colorectal cancer and future rates of surveillance for patients. However, this characterization is a challenging task and suffers from significant inter- and intra-observer variability. Aims: We built an automatic image analysis method that can accurately classify different types of colorectal polyps on whole-slide images to help pathologists with this characterization and diagnosis. Setting and Design: Our method is based on deep-learning techniques, which rely on numerous levels of abstraction for data representation and have shown state-of-the-art results for various image analysis tasks. Subjects and Methods: Our method covers five common types of polyps (i.e., hyperplastic, sessile serrated, traditional serrated, tubular, and tubulovillous/villous) that are included in the US Multisociety Task Force guidelines for colorectal cancer risk assessment and surveillance. We developed multiple deep-learning approaches by leveraging a dataset of 2074 crop images, which were annotated by multiple domain expert pathologists as reference standards. Statistical Analysis: We evaluated our method on an independent test set of 239 whole-slide images and measured standard machine-learning evaluation metrics of accuracy, precision, recall, and F1 score and their 95% confidence intervals. Results: Our evaluation shows that our method with residual network architecture achieves the best performance for classification of colorectal polyps on whole-slide images (overall accuracy: 93.0%, 95% confidence interval: 89.0%–95.9%). Conclusions: Our method can reduce the cognitive burden on pathologists and improve their efficacy in histopathological characterization of colorectal polyps and in subsequent risk assessment and follow-up recommendations. PMID:28828201

A Novel Unsupervised Segmentation Quality Evaluation Method for Remote Sensing Images

PubMed Central

Tang, Yunwei; Jing, Linhai; Ding, Haifeng

2017-01-01

The segmentation of a high spatial resolution remote sensing image is a critical step in geographic object-based image analysis (GEOBIA). Evaluating the performance of segmentation without ground truth data, i.e., unsupervised evaluation, is important for the comparison of segmentation algorithms and the automatic selection of optimal parameters. This unsupervised strategy currently faces several challenges in practice, such as difficulties in designing effective indicators and limitations of the spectral values in the feature representation. This study proposes a novel unsupervised evaluation method to quantitatively measure the quality of segmentation results to overcome these problems. In this method, multiple spectral and spatial features of images are first extracted simultaneously and then integrated into a feature set to improve the quality of the feature representation of ground objects. The indicators designed for spatial stratified heterogeneity and spatial autocorrelation are included to estimate the properties of the segments in this integrated feature set. These two indicators are then combined into a global assessment metric as the final quality score. The trade-offs of the combined indicators are accounted for using a strategy based on the Mahalanobis distance, which can be exhibited geometrically. The method is tested on two segmentation algorithms and three testing images. The proposed method is compared with two existing unsupervised methods and a supervised method to confirm its capabilities. Through comparison and visual analysis, the results verified the effectiveness of the proposed method and demonstrated the reliability and improvements of this method with respect to other methods. PMID:29064416

Exploring the feasibility of iris recognition for visible spectrum iris images obtained using smartphone camera

NASA Astrophysics Data System (ADS)

Trokielewicz, Mateusz; Bartuzi, Ewelina; Michowska, Katarzyna; Andrzejewska, Antonina; Selegrat, Monika

2015-09-01

In the age of modern, hyperconnected society that increasingly relies on mobile devices and solutions, implementing a reliable and accurate biometric system employing iris recognition presents new challenges. Typical biometric systems employing iris analysis require expensive and complicated hardware. We therefore explore an alternative way using visible spectrum iris imaging. This paper aims at answering several questions related to applying iris biometrics for images obtained in the visible spectrum using smartphone camera. Can irides be successfully and effortlessly imaged using a smartphone's built-in camera? Can existing iris recognition methods perform well when presented with such images? The main advantage of using near-infrared (NIR) illumination in dedicated iris recognition cameras is good performance almost independent of the iris color and pigmentation. Are the images obtained from smartphone's camera of sufficient quality even for the dark irides? We present experiments incorporating simple image preprocessing to find the best visibility of iris texture, followed by a performance study to assess whether iris recognition methods originally aimed at NIR iris images perform well with visible light images. To our best knowledge this is the first comprehensive analysis of iris recognition performance using a database of high-quality images collected in visible light using the smartphones flashlight together with the application of commercial off-the-shelf (COTS) iris recognition methods.

3D Actin Network Centerline Extraction with Multiple Active Contours

PubMed Central

Xu, Ting; Vavylonis, Dimitrios; Huang, Xiaolei

2013-01-01

Fluorescence microscopy is frequently used to study two and three dimensional network structures formed by cytoskeletal polymer fibers such as actin filaments and actin cables. While these cytoskeletal structures are often dilute enough to allow imaging of individual filaments or bundles of them, quantitative analysis of these images is challenging. To facilitate quantitative, reproducible and objective analysis of the image data, we propose a semi-automated method to extract actin networks and retrieve their topology in 3D. Our method uses multiple Stretching Open Active Contours (SOACs) that are automatically initialized at image intensity ridges and then evolve along the centerlines of filaments in the network. SOACs can merge, stop at junctions, and reconfigure with others to allow smooth crossing at junctions of filaments. The proposed approach is generally applicable to images of curvilinear networks with low SNR. We demonstrate its potential by extracting the centerlines of synthetic meshwork images, actin networks in 2D Total Internal Reflection Fluorescence Microscopy images, and 3D actin cable meshworks of live fission yeast cells imaged by spinning disk confocal microscopy. Quantitative evaluation of the method using synthetic images shows that for images with SNR above 5.0, the average vertex error measured by the distance between our result and ground truth is 1 voxel, and the average Hausdorff distance is below 10 voxels. PMID:24316442

Classification of visible and infrared hyperspectral images based on image segmentation and edge-preserving filtering

NASA Astrophysics Data System (ADS)

Cui, Binge; Ma, Xiudan; Xie, Xiaoyun; Ren, Guangbo; Ma, Yi

2017-03-01

The classification of hyperspectral images with a few labeled samples is a major challenge which is difficult to meet unless some spatial characteristics can be exploited. In this study, we proposed a novel spectral-spatial hyperspectral image classification method that exploited spatial autocorrelation of hyperspectral images. First, image segmentation is performed on the hyperspectral image to assign each pixel to a homogeneous region. Second, the visible and infrared bands of hyperspectral image are partitioned into multiple subsets of adjacent bands, and each subset is merged into one band. Recursive edge-preserving filtering is performed on each merged band which utilizes the spectral information of neighborhood pixels. Third, the resulting spectral and spatial feature band set is classified using the SVM classifier. Finally, bilateral filtering is performed to remove "salt-and-pepper" noise in the classification result. To preserve the spatial structure of hyperspectral image, edge-preserving filtering is applied independently before and after the classification process. Experimental results on different hyperspectral images prove that the proposed spectral-spatial classification approach is robust and offers more classification accuracy than state-of-the-art methods when the number of labeled samples is small.

Stable image acquisition for mobile image processing applications

NASA Astrophysics Data System (ADS)

Henning, Kai-Fabian; Fritze, Alexander; Gillich, Eugen; Mönks, Uwe; Lohweg, Volker

2015-02-01

Today, mobile devices (smartphones, tablets, etc.) are widespread and of high importance for their users. Their performance as well as versatility increases over time. This leads to the opportunity to use such devices for more specific tasks like image processing in an industrial context. For the analysis of images requirements like image quality (blur, illumination, etc.) as well as a defined relative position of the object to be inspected are crucial. Since mobile devices are handheld and used in constantly changing environments the challenge is to fulfill these requirements. We present an approach to overcome the obstacles and stabilize the image capturing process such that image analysis becomes significantly improved on mobile devices. Therefore, image processing methods are combined with sensor fusion concepts. The approach consists of three main parts. First, pose estimation methods are used to guide a user moving the device to a defined position. Second, the sensors data and the pose information are combined for relative motion estimation. Finally, the image capturing process is automated. It is triggered depending on the alignment of the device and the object as well as the image quality that can be achieved under consideration of motion and environmental effects.

Stable and accurate methods for identification of water bodies from Landsat series imagery using meta-heuristic algorithms

NASA Astrophysics Data System (ADS)

Gamshadzaei, Mohammad Hossein; Rahimzadegan, Majid

2017-10-01

Identification of water extents in Landsat images is challenging due to surfaces with similar reflectance to water extents. The objective of this study is to provide stable and accurate methods for identifying water extents in Landsat images based on meta-heuristic algorithms. Then, seven Landsat images were selected from various environmental regions in Iran. Training of the algorithms was performed using 40 water pixels and 40 nonwater pixels in operational land imager images of Chitgar Lake (one of the study regions). Moreover, high-resolution images from Google Earth were digitized to evaluate the results. Two approaches were considered: index-based and artificial intelligence (AI) algorithms. In the first approach, nine common water spectral indices were investigated. AI algorithms were utilized to acquire coefficients of optimal band combinations to extract water extents. Among the AI algorithms, the artificial neural network algorithm and also the ant colony optimization, genetic algorithm, and particle swarm optimization (PSO) meta-heuristic algorithms were implemented. Index-based methods represented different performances in various regions. Among AI methods, PSO had the best performance with average overall accuracy and kappa coefficient of 93% and 98%, respectively. The results indicated the applicability of acquired band combinations to extract accurately and stably water extents in Landsat imagery.

Measurement of smaller colon polyp in CT colonography images using morphological image processing.

PubMed

Manjunath, K N; Siddalingaswamy, P C; Prabhu, G K

2017-11-01

Automated measurement of the size and shape of colon polyps is one of the challenges in Computed tomography colonography (CTC). The objective of this retrospective study was to improve the sensitivity and specificity of smaller polyp measurement in CTC using image processing techniques. A domain knowledge-based method has been implemented with hybrid method of colon segmentation, morphological image processing operators for detecting the colonic structures, and the decision-making system for delineating the smaller polyp-based on a priori knowledge. The method was applied on 45 CTC dataset. The key finding was that the smaller polyps were accurately measured. In addition to 6-9 mm range, polyps of even <5 mm were also detected. The results were validated qualitatively and quantitatively using both 2D MPR and 3D view. Implementation was done on a high-performance computer with parallel processing. It takes [Formula: see text] min for measuring the smaller polyp in a dataset of 500 CTC images. With this method, [Formula: see text] and [Formula: see text] were achieved. The domain-based approach with morphological image processing has given good results. The smaller polyps were measured accurately which helps in making right clinical decisions. Qualitatively and quantitatively the results were acceptable when compared to the ground truth at [Formula: see text].

Method for inserting noise in digital mammography to simulate reduction in radiation dose

NASA Astrophysics Data System (ADS)

Borges, Lucas R.; de Oliveira, Helder C. R.; Nunes, Polyana F.; Vieira, Marcelo A. C.

2015-03-01

The quality of clinical x-ray images is closely related to the radiation dose used in the imaging study. The general principle for selecting the radiation is ALARA ("as low as reasonably achievable"). The practical optimization, however, remains challenging. It is well known that reducing the radiation dose increases the quantum noise, which could compromise the image quality. In order to conduct studies about dose reduction in mammography, it would be necessary to acquire repeated clinical images, from the same patient, with different dose levels. However, such practice would be unethical due to radiation related risks. One solution is to simulate the effects of dose reduction in clinical images. This work proposes a new method, based on the Anscombe transformation, which simulates dose reduction in digital mammography by inserting quantum noise into clinical mammograms acquired with the standard radiation dose. Thus, it is possible to simulate different levels of radiation doses without exposing the patient to new levels of radiation. Results showed that the achieved quality of simulated images generated with our method is the same as when using other methods found in the literature, with the novelty of using the Anscombe transformation for converting signal-independent Gaussian noise into signal-dependent quantum noise.

Considering the Spatial Layout Information of Bag of Features (BoF) Framework for Image Classification.

PubMed

Mu, Guangyu; Liu, Ying; Wang, Limin

2015-01-01

The spatial pooling method such as spatial pyramid matching (SPM) is very crucial in the bag of features model used in image classification. SPM partitions the image into a set of regular grids and assumes that the spatial layout of all visual words obey the uniform distribution over these regular grids. However, in practice, we consider that different visual words should obey different spatial layout distributions. To improve SPM, we develop a novel spatial pooling method, namely spatial distribution pooling (SDP). The proposed SDP method uses an extension model of Gauss mixture model to estimate the spatial layout distributions of the visual vocabulary. For each visual word type, SDP can generate a set of flexible grids rather than the regular grids from the traditional SPM. Furthermore, we can compute the grid weights for visual word tokens according to their spatial coordinates. The experimental results demonstrate that SDP outperforms the traditional spatial pooling methods, and is competitive with the state-of-the-art classification accuracy on several challenging image datasets.

Phase congruency map driven brain tumour segmentation

NASA Astrophysics Data System (ADS)

Szilágyi, Tünde; Brady, Michael; Berényi, Ervin

2015-03-01

Computer Aided Diagnostic (CAD) systems are already of proven value in healthcare, especially for surgical planning, nevertheless much remains to be done. Gliomas are the most common brain tumours (70%) in adults, with a survival time of just 2-3 months if detected at WHO grades III or higher. Such tumours are extremely variable, necessitating multi-modal Magnetic Resonance Images (MRI). The use of Gadolinium-based contrast agents is only relevant at later stages of the disease where it highlights the enhancing rim of the tumour. Currently, there is no single accepted method that can be used as a reference. There are three main challenges with such images: to decide whether there is tumour present and is so localize it; to construct a mask that separates healthy and diseased tissue; and to differentiate between the tumour core and the surrounding oedema. This paper presents two contributions. First, we develop tumour seed selection based on multiscale multi-modal texture feature vectors. Second, we develop a method based on a local phase congruency based feature map to drive level-set segmentation. The segmentations achieved with our method are more accurate than previously presented methods, particularly for challenging low grade tumours.

Educational Neuroscience: Neuroethical Considerations

ERIC Educational Resources Information Center

Lalancette, Helene; Campbell, Stephen R.

2012-01-01

Research design and methods in educational neuroscience involve using neuroscientific tools such as brain image technologies to investigate cognitive functions and inform educational practices. The ethical challenges raised by research in social neuroscience have become the focus of neuroethics, a sub-discipline of bioethics. More specifically…

Fully automated chest wall line segmentation in breast MRI by using context information

NASA Astrophysics Data System (ADS)

Wu, Shandong; Weinstein, Susan P.; Conant, Emily F.; Localio, A. Russell; Schnall, Mitchell D.; Kontos, Despina

2012-03-01

Breast MRI has emerged as an effective modality for the clinical management of breast cancer. Evidence suggests that computer-aided applications can further improve the diagnostic accuracy of breast MRI. A critical and challenging first step for automated breast MRI analysis, is to separate the breast as an organ from the chest wall. Manual segmentation or user-assisted interactive tools are inefficient, tedious, and error-prone, which is prohibitively impractical for processing large amounts of data from clinical trials. To address this challenge, we developed a fully automated and robust computerized segmentation method that intensively utilizes context information of breast MR imaging and the breast tissue's morphological characteristics to accurately delineate the breast and chest wall boundary. A critical component is the joint application of anisotropic diffusion and bilateral image filtering to enhance the edge that corresponds to the chest wall line (CWL) and to reduce the effect of adjacent non-CWL tissues. A CWL voting algorithm is proposed based on CWL candidates yielded from multiple sequential MRI slices, in which a CWL representative is generated and used through a dynamic time warping (DTW) algorithm to filter out inferior candidates, leaving the optimal one. Our method is validated by a representative dataset of 20 3D unilateral breast MRI scans that span the full range of the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) fibroglandular density categorization. A promising performance (average overlay percentage of 89.33%) is observed when the automated segmentation is compared to manually segmented ground truth obtained by an experienced breast imaging radiologist. The automated method runs time-efficiently at ~3 minutes for each breast MR image set (28 slices).

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.

Automated ultrasound scanning on a dual-modality breast imaging system: coverage and motion issues and solutions.

PubMed

Sinha, Sumedha P; Goodsitt, Mitchell M; Roubidoux, Marilyn A; Booi, Rebecca C; LeCarpentier, Gerald L; Lashbrook, Christine R; Thomenius, Kai E; Chalek, Carl L; Carson, Paul L

2007-05-01

We are developing an automated ultrasound imaging-mammography system wherein a digital mammography unit has been augmented with a motorized ultrasound transducer carriage above a special compression paddle. Challenges of this system are acquiring complete coverage of the breast and minimizing motion. We assessed these problems and investigated methods to increase coverage and stabilize the compressed breast. Visual tracings of the breast-to-paddle contact area and breast periphery were made for 10 patients to estimate coverage area. Various motion artifacts were evaluated in 6 patients. Nine materials were tested for coupling the paddle to the breast. Fourteen substances were tested for coupling the transducer to the paddle in lateral-to-medial and medial-to-lateral views and filling the gap between the peripheral breast and paddle. In-house image registration software was used to register adjacent ultrasound sweeps. The average breast contact area was 56%. The average percentage of the peripheral air gap filled with ultrasound gel was 61%. Shallow patient breathing proved equivalent to breath holding, whereas speech and sudden breathing caused unacceptable artifacts. An adhesive spray that preserves image quality was found to be best for coupling the breast to the paddle and minimizing motion. A highly viscous ultrasound gel proved most effective for coupling the transducer to the paddle for lateral-to-medial and medial-to-lateral views and for edge fill-in. The challenges of automated ultrasound scanning in a multimodality breast imaging system have been addressed by developing methods to fill in peripheral gaps, minimize patient motion, and register and reconstruct multisweep ultrasound image volumes.

Image Reconstruction for Interferometric Imaging of Geosynchronous Satellites

NASA Astrophysics Data System (ADS)

DeSantis, Zachary J.

Imaging distant objects at a high resolution has always presented a challenge due to the diffraction limit. Larger apertures improve the resolution, but at some point the cost of engineering, building, and correcting phase aberrations of large apertures become prohibitive. Interferometric imaging uses the Van Cittert-Zernike theorem to form an image from measurements of spatial coherence. This effectively allows the synthesis of a large aperture from two or more smaller telescopes to improve the resolution. We apply this method to imaging geosynchronous satellites with a ground-based system. Imaging a dim object from the ground presents unique challenges. The atmosphere creates errors in the phase measurements. The measurements are taken simultaneously across a large bandwidth of light. The atmospheric piston error, therefore, manifests as a linear phase error across the spectral measurements. Because the objects are faint, many of the measurements are expected to have a poor signal-to-noise ratio (SNR). This eliminates possibility of use of commonly used techniques like closure phase, which is a standard technique in astronomical interferometric imaging for making partial phase measurements in the presence of atmospheric error. The bulk of our work has been focused on forming an image, using sub-Nyquist sampled data, in the presence of these linear phase errors without relying on closure phase techniques. We present an image reconstruction algorithm that successfully forms an image in the presence of these linear phase errors. We demonstrate our algorithm’s success in both simulation and in laboratory experiments.

Modeling super-resolution SERS using a T-matrix method to elucidate molecule-nanoparticle coupling and the origins of localization errors

NASA Astrophysics Data System (ADS)

Heaps, Charles W.; Schatz, George C.

2017-06-01

A computational method to model diffraction-limited images from super-resolution surface-enhanced Raman scattering microscopy is introduced. Despite significant experimental progress in plasmon-based super-resolution imaging, theoretical predictions of the diffraction limited images remain a challenge. The method is used to calculate localization errors and image intensities for a single spherical gold nanoparticle-molecule system. The light scattering is calculated using a modification of generalized Mie (T-matrix) theory with a point dipole source and diffraction limited images are calculated using vectorial diffraction theory. The calculation produces the multipole expansion for each emitter and the coherent superposition of all fields. Imaging the constituent fields in addition to the total field provides new insight into the strong coupling between the molecule and the nanoparticle. Regardless of whether the molecular dipole moment is oriented parallel or perpendicular to the nanoparticle surface, the anisotropic excitation distorts the center of the nanoparticle as measured by the point spread function by approximately fifty percent of the particle radius toward to the molecule. Inspection of the nanoparticle multipoles reveals that distortion arises from a weak quadrupole resonance interfering with the dipole field in the nanoparticle. When the nanoparticle-molecule fields are in-phase, the distorted nanoparticle field dominates the observed image. When out-of-phase, the nanoparticle and molecule are of comparable intensity and interference between the two emitters dominates the observed image. The method is also applied to different wavelengths and particle radii. At off-resonant wavelengths, the method predicts images closer to the molecule not because of relative intensities but because of greater distortion in the nanoparticle. The method is a promising approach to improving the understanding of plasmon-enhanced super-resolution experiments.

A level-set method for pathology segmentation in fluorescein angiograms and en face retinal images of patients with age-related macular degeneration

NASA Astrophysics Data System (ADS)

Mohammad, Fatimah; Ansari, Rashid; Shahidi, Mahnaz

2013-03-01

The visibility and continuity of the inner segment outer segment (ISOS) junction layer of the photoreceptors on spectral domain optical coherence tomography images is known to be related to visual acuity in patients with age-related macular degeneration (AMD). Automatic detection and segmentation of lesions and pathologies in retinal images is crucial for the screening, diagnosis, and follow-up of patients with retinal diseases. One of the challenges of using the classical level-set algorithms for segmentation involves the placement of the initial contour. Manually defining the contour or randomly placing it in the image may lead to segmentation of erroneous structures. It is important to be able to automatically define the contour by using information provided by image features. We explored a level-set method which is based on the classical Chan-Vese model and which utilizes image feature information for automatic contour placement for the segmentation of pathologies in fluorescein angiograms and en face retinal images of the ISOS layer. This was accomplished by exploiting a priori knowledge of the shape and intensity distribution allowing the use of projection profiles to detect the presence of pathologies that are characterized by intensity differences with surrounding areas in retinal images. We first tested our method by applying it to fluorescein angiograms. We then applied our method to en face retinal images of patients with AMD. The experimental results included demonstrate that the proposed method provided a quick and improved outcome as compared to the classical Chan-Vese method in which the initial contour is randomly placed, thus indicating the potential to provide a more accurate and detailed view of changes in pathologies due to disease progression and treatment.

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

PubMed

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

2017-01-01

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

Combination of intensity-based image registration with 3D simulation in radiation therapy.

PubMed

Li, Pan; Malsch, Urban; Bendl, Rolf

2008-09-07

Modern techniques of radiotherapy like intensity modulated radiation therapy (IMRT) make it possible to deliver high dose to tumors of different irregular shapes at the same time sparing surrounding healthy tissue. However, internal tumor motion makes precise calculation of the delivered dose distribution challenging. This makes analysis of tumor motion necessary. One way to describe target motion is using image registration. Many registration methods have already been developed previously. However, most of them belong either to geometric approaches or to intensity approaches. Methods which take account of anatomical information and results of intensity matching can greatly improve the results of image registration. Based on this idea, a combined method of image registration followed by 3D modeling and simulation was introduced in this project. Experiments were carried out for five patients 4DCT lung datasets. In the 3D simulation, models obtained from images of end-exhalation were deformed to the state of end-inhalation. Diaphragm motions were around -25 mm in the cranial-caudal (CC) direction. To verify the quality of our new method, displacements of landmarks were calculated and compared with measurements in the CT images. Improvement of accuracy after simulations has been shown compared to the results obtained only by intensity-based image registration. The average improvement was 0.97 mm. The average Euclidean error of the combined method was around 3.77 mm. Unrealistic motions such as curl-shaped deformations in the results of image registration were corrected. The combined method required less than 30 min. Our method provides information about the deformation of the target volume, which we need for dose optimization and target definition in our planning system.

FIB-SEM imaging of carbon nanotubes in mouse lung tissue.

PubMed

Købler, Carsten; Saber, Anne Thoustrup; Jacobsen, Nicklas Raun; Wallin, Håkan; Vogel, Ulla; Qvortrup, Klaus; Mølhave, Kristian

2014-06-01

Ultrastructural characterisation is important for understanding carbon nanotube (CNT) toxicity and how the CNTs interact with cells and tissues. The standard method for this involves using transmission electron microscopy (TEM). However, in particular, the sample preparation, using a microtome to cut thin sample sections for TEM, can be challenging for investigation of regions with agglomerations of large and stiff CNTs because the CNTs cut with difficulty. As a consequence, the sectioning diamond knife may be damaged and the uncut CNTs are left protruding from the embedded block surface excluding them from TEM analysis. To provide an alternative to ultramicrotomy and subsequent TEM imaging, we studied focused ion beam scanning electron microscopy (FIB-SEM) of CNTs in the lungs of mice, and we evaluated the applicability of the method compared to TEM. FIB-SEM can provide serial section volume imaging not easily obtained with TEM, but it is time-consuming to locate CNTs in the tissue. We demonstrate that protruding CNTs after ultramicrotomy can be used to locate the region of interest, and we present FIB-SEM images of CNTs in lung tissue. FIB-SEM imaging was applied to lung tissue from mice which had been intratracheally instilled with two different multiwalled CNTs; one being short and thin, and the other longer and thicker. FIB-SEM was found to be most suitable for detection of the large CNTs (Ø ca. 70 nm), and to be well suited for studying CNT agglomerates in biological samples which is challenging using standard TEM techniques.

3D/2D image registration using weighted histogram of gradient directions

NASA Astrophysics Data System (ADS)

Ghafurian, Soheil; Hacihaliloglu, Ilker; Metaxas, Dimitris N.; Tan, Virak; Li, Kang

2015-03-01

Three dimensional (3D) to two dimensional (2D) image registration is crucial in many medical applications such as image-guided evaluation of musculoskeletal disorders. One of the key problems is to estimate the 3D CT- reconstructed bone model positions (translation and rotation) which maximize the similarity between the digitally reconstructed radiographs (DRRs) and the 2D fluoroscopic images using a registration method. This problem is computational-intensive due to a large search space and the complicated DRR generation process. Also, finding a similarity measure which converges to the global optimum instead of local optima adds to the challenge. To circumvent these issues, most existing registration methods need a manual initialization, which requires user interaction and is prone to human error. In this paper, we introduce a novel feature-based registration method using the weighted histogram of gradient directions of images. This method simplifies the computation by searching the parameter space (rotation and translation) sequentially rather than simultaneously. In our numeric simulation experiments, the proposed registration algorithm was able to achieve sub-millimeter and sub-degree accuracies. Moreover, our method is robust to the initial guess. It can tolerate up to +/-90°rotation offset from the global optimal solution, which minimizes the need for human interaction to initialize the algorithm.

A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images

PubMed Central

Hou, Bin; Wang, Yunhong; Liu, Qingjie

2016-01-01

Characterizations of up to date information of the Earth’s surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD) methods have been developed to solve them by utilizing remote sensing (RS) images. The advent of high resolution (HR) remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs) allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC) segmentation. Then, saliency and morphological building index (MBI) extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF). Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation. PMID:27618903

Hair segmentation using adaptive threshold from edge and branch length measures.

PubMed

Lee, Ian; Du, Xian; Anthony, Brian

2017-10-01

Non-invasive imaging techniques allow the monitoring of skin structure and diagnosis of skin diseases in clinical applications. However, hair in skin images hampers the imaging and classification of the skin structure of interest. Although many hair segmentation methods have been proposed for digital hair removal, a major challenge in hair segmentation remains in detecting hairs that are thin, overlapping, of similar contrast or color to underlying skin, or overlaid on highly-textured skin structure. To solve the problem, we present an automatic hair segmentation method that uses edge density (ED) and mean branch length (MBL) to measure hair. First, hair is detected by the integration of top-hat transform and modified second-order Gaussian filter. Second, we employ a robust adaptive threshold of ED and MBL to generate a hair mask. Third, the hair mask is refined by k-NN classification of hair and skin pixels. The proposed algorithm was tested using two datasets of healthy skin images and lesion images respectively. These datasets were taken from different imaging platforms in various illumination levels and varying skin colors. We compared the hair detection and segmentation results from our algorithm and six other hair segmentation methods of state of the art. Our method exhibits high value of sensitivity: 75% and specificity: 95%, which indicates significantly higher accuracy and better balance between true positive and false positive detection than the other methods. Published by Elsevier Ltd.

A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images.

PubMed

Hou, Bin; Wang, Yunhong; Liu, Qingjie

2016-08-27

Characterizations of up to date information of the Earth's surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD) methods have been developed to solve them by utilizing remote sensing (RS) images. The advent of high resolution (HR) remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs) allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC) segmentation. Then, saliency and morphological building index (MBI) extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF). Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation.

Joint reconstruction via coupled Bregman iterations with applications to PET-MR imaging

NASA Astrophysics Data System (ADS)

Rasch, Julian; Brinkmann, Eva-Maria; Burger, Martin

2018-01-01

Joint reconstruction has recently attracted a lot of attention, especially in the field of medical multi-modality imaging such as PET-MRI. Most of the developed methods rely on the comparison of image gradients, or more precisely their location, direction and magnitude, to make use of structural similarities between the images. A challenge and still an open issue for most of the methods is to handle images in entirely different scales, i.e. different magnitudes of gradients that cannot be dealt with by a global scaling of the data. We propose the use of generalized Bregman distances and infimal convolutions thereof with regard to the well-known total variation functional. The use of a total variation subgradient respectively the involved vector field rather than an image gradient naturally excludes the magnitudes of gradients, which in particular solves the scaling behavior. Additionally, the presented method features a weighting that allows to control the amount of interaction between channels. We give insights into the general behavior of the method, before we further tailor it to a particular application, namely PET-MRI joint reconstruction. To do so, we compute joint reconstruction results from blurry Poisson data for PET and undersampled Fourier data from MRI and show that we can gain a mutual benefit for both modalities. In particular, the results are superior to the respective separate reconstructions and other joint reconstruction methods.

A survey of MRI-based medical image analysis for brain tumor studies

NASA Astrophysics Data System (ADS)

Bauer, Stefan; Wiest, Roland; Nolte, Lutz-P.; Reyes, Mauricio

2013-07-01

MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.