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Journal of Medical Imaging

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https://www.readbyqxmd.com/read/28523284/enhanced-axial-and-lateral-resolution-using-stabilized-pulses
#1
Shujie Chen, Kevin J Parker
Ultrasound B-scan imaging systems operate under some well-known resolution limits. To improve resolution, the concept of stable pulses, having bounded inverse filters, was previously utilized for the lateral deconvolution. This framework has been extended to the axial direction, enabling a two-dimensional deconvolution. The modeling of the two-way response in the axial direction is discussed, and the deconvolution is performed in the in-phase quadrature data domain. Stable inverse filters are generated and applied for the deconvolution of the image data from Field II simulation, a tissue-mimicking phantom, and in vivo imaging of a carotid artery, where resolution enhancement is observed...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28523283/variable-temporal-sampling-and-tube-current-modulation-for-myocardial-blood-flow-estimation-from-dose-reduced-dynamic-computed-tomography
#2
Dimple Modgil, Michael D Bindschadler, Adam M Alessio, Patrick J La Rivière
Quantification of myocardial blood flow (MBF) can aid in the diagnosis and treatment of coronary artery disease. However, there are no widely accepted clinical methods for estimating MBF. Dynamic cardiac perfusion computed tomography (CT) holds the promise of providing a quick and easy method to measure MBF quantitatively. However, the need for repeated scans can potentially result in a high patient radiation dose, limiting the clinical acceptance of this approach. In our previous work, we explored techniques to reduce the patient dose by either uniformly reducing the tube current or by uniformly reducing the number of temporal frames in the dynamic CT sequence...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28523282/comparative-study-of-computational-visual-attention-models-on-two-dimensional-medical-images
#3
Gezheng Wen, Brenda Rodriguez-Niño, Furkan Y Pecen, David J Vining, Naveen Garg, Mia K Markey
Computational modeling of visual attention is an active area of research. These models have been successfully employed in applications such as robotics. However, most computational models of visual attention are developed in the context of natural scenes, and their role with medical images is not well investigated. As radiologists interpret a large number of clinical images in a limited time, an efficient strategy to deploy their visual attention is necessary. Visual saliency maps, highlighting image regions that differ dramatically from their surroundings, are expected to be predictive of where radiologists fixate their gaze...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28491908/lack-of-agreement-between-radiologists-implications-for-image-based-model-observers
#4
Juhun Lee, Robert M Nishikawa, Ingrid Reiser, Margarita L Zuley, John M Boone
We tested the agreement of radiologists' rankings of different reconstructions of breast computed tomography images based on their diagnostic (classification) performance and on their subjective image quality assessments. We used 102 pathology proven cases (62 malignant, 40 benign), and an iterative image reconstruction (IIR) algorithm to obtain 24 reconstructions per case with different image appearances. Using image feature analysis, we selected 3 IIRs and 1 clinical reconstruction and 50 lesions. The reconstructions produced a range of image quality from smooth/low-noise to sharp/high-noise, which had a range in classifier performance corresponding to AUCs of 0...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28491907/comprehensive-assessment-of-patient-image-quality-and-radiation-dose-in-latest-generation-cardiac-x-ray-equipment-for-percutaneous-coronary-interventions
#5
Amber J Gislason-Lee, Claire Keeble, Daniel Egleston, Josephine Bexon, Stephen M Kengyelics, Andrew G Davies
This study aimed to determine whether a reduction in radiation dose was found for percutaneous coronary interventional (PCI) patients using a cardiac interventional x-ray system with state-of-the-art image enhancement and x-ray optimization, compared to the current generation x-ray system, and to determine the corresponding impact on clinical image quality. Patient procedure dose area product (DAP) and fluoroscopy duration of 131 PCI patient cases from each x-ray system were compared using a Wilcoxon test on median values...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28466029/global-detection-approach-for-clustered-microcalcifications-in-mammograms-using-a-deep-learning-network
#6
Juan Wang, Robert M Nishikawa, Yongyi Yang
In computerized detection of clustered microcalcifications (MCs) from mammograms, the traditional approach is to apply a pattern detector to locate the presence of individual MCs, which are subsequently grouped into clusters. Such an approach is often susceptible to the occurrence of false positives (FPs) caused by local image patterns that resemble MCs. We investigate the feasibility of a direct detection approach to determining whether an image region contains clustered MCs or not. Toward this goal, we develop a deep convolutional neural network (CNN) as the classifier model to which the input consists of a large image window ([Formula: see text] in size)...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28466028/improved-centerline-tree-detection-of-diseased-peripheral-arteries-with-a-cascading-algorithm-for-vascular-segmentation
#7
Kristína Lidayová, Hans Frimmel, Ewert Bengtsson, Örjan Smedby
Vascular segmentation plays an important role in the assessment of peripheral arterial disease. The segmentation is very challenging especially for arteries with severe stenosis or complete occlusion. We present a cascading algorithm for vascular centerline tree detection specializing in detecting centerlines in diseased peripheral arteries. It takes a three-dimensional computed tomography angiography (CTA) volume and returns a vascular centerline tree, which can be used for accelerating and facilitating the vascular segmentation...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28466027/joint-estimation-of-activity-image-and-attenuation-sinogram-using-time-of-flight-positron-emission-tomography-data-consistency-condition-filtering
#8
Quanzheng Li, Hao Li, Kyungsang Kim, Georges El Fakhri
Attenuation correction is essential for quantitative reliability of positron emission tomography (PET) imaging. In time-of-flight (TOF) PET, attenuation sinogram can be determined up to a global constant from noiseless emission data due to the TOF PET data consistency condition. This provides the theoretical basis for jointly estimating both activity image and attenuation sinogram/image directly from TOF PET emission data. Multiple joint estimation methods, such as maximum likelihood activity and attenuation (MLAA) and maximum likelihood attenuation correction factor (MLACF), have already been shown that can produce improved reconstruction results in TOF cases...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28439524/brainsegnet-a-convolutional-neural-network-architecture-for-automated-segmentation-of-human-brain-structures
#9
Raghav Mehta, Aabhas Majumdar, Jayanthi Sivaswamy
Automated segmentation of cortical and noncortical human brain structures has been hitherto approached using nonrigid registration followed by label fusion. We propose an alternative approach for this using a convolutional neural network (CNN) which classifies a voxel into one of many structures. Four different kinds of two-dimensional and three-dimensional intensity patches are extracted for each voxel, providing local and global (context) information to the CNN. The proposed approach is evaluated on five different publicly available datasets which differ in the number of labels per volume...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28439523/tsallis-entropy-and-sparse-reconstructive-dictionary-learning-for-exudate-detection-in-diabetic-retinopathy
#10
Vineeta Das, Niladri B Puhan
Computer-assisted automated exudate detection is crucial for large-scale screening of diabetic retinopathy (DR). The motivation of this work is robust and accurate detection of low contrast and isolated hard exudates using fundus imaging. Gabor filtering is first performed to enhance exudate visibility followed by Tsallis entropy thresholding. The obtained candidate exudate pixel map is useful for further removal of falsely detected candidates using sparse-based dictionary learning and classification. Two reconstructive dictionaries are learnt using the intensity, gradient, local energy, and transform domain features extracted from exudate and background patches of the training fundus images...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28439522/automatic-image-quality-assessment-and-measurement-of-fetal-head-in-two-dimensional-ultrasound-image
#11
Lei Zhang, Nicholas J Dudley, Tryphon Lambrou, Nigel Allinson, Xujiong Ye
Owing to the inconsistent image quality existing in routine obstetric ultrasound (US) scans that leads to a large intraobserver and interobserver variability, the aim of this study is to develop a quality-assured, fully automated US fetal head measurement system. A texton-based fetal head segmentation is used as a prerequisite step to obtain the head region. Textons are calculated using a filter bank designed specific for US fetal head structure. Both shape- and anatomic-based features calculated from the segmented head region are then fed into a random forest classifier to determine the quality of the image (e...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28439521/dynamic-electronic-collimation-method-for-3-d-catheter-tracking-on-a-scanning-beam-digital-x-ray-system
#12
David A P Dunkerley, Jordan M Slagowski, Tobias Funk, Michael A Speidel
Scanning-beam digital x-ray (SBDX) is an inverse geometry x-ray fluoroscopy system capable of tomosynthesis-based 3-D catheter tracking. This work proposes a method of dose-reduced 3-D catheter tracking using dynamic electronic collimation (DEC) of the SBDX scanning x-ray tube. This is achieved through the selective deactivation of focal spot positions not needed for the catheter tracking task. The technique was retrospectively evaluated with SBDX detector data recorded during a phantom study. DEC imaging of a catheter tip at isocenter required 340 active focal spots per frame versus 4473 spots in full field-of-view (FOV) mode...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28413809/preclinical-imaging-of-iridocorneal-angle-and-fundus-using-a-modified-integrated-flexible-handheld-probe
#13
Xun Jie Jeesmond Hong, Vengalathunadakal K Shinoj, Vadakke Matham Murukeshan, Mani Baskaran, Tin Aung
A flexible handheld imaging probe consisting of a [Formula: see text] charge-coupled device camera, light-emitting diode light sources, and near-infrared laser source is designed and developed. The imaging probe is designed with specifications to capture the iridocorneal angle images and posterior segment images. Light propagation from the anterior chamber of the eye to the exterior is considered analytically using Snell's law. Imaging of the iridocorneal angle region and fundus is performed on ex vivo porcine samples and subsequently on small laboratory animals, such as the New Zealand white rabbit and nonhuman primate, in vivo...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28413808/vessel-tree-tracking-in-angiographic-sequences
#14
Dong Zhang, Shanhui Sun, Ziyan Wu, Bor-Jeng Chen, Terrence Chen
We present a method to track vessels in angiography [contrast filled vessels in two-dimensional (2-D) x-ray fluoroscopy]. Finding correspondence of a vessel tree from consecutive angiogram frames provides significant value in computer-aided clinical applications such as fast vessel tree segmentation, three-dimensional (3-D) vessel topology reconstruction from corresponding centerlines, cardiac motion understanding, etc. However, establishing an accurate vessel tree correspondence (vessel tree tracking) is a nontrivial problem due to nonlinear periodic cardiac and breathing motion in 2-D views, foreshortening, false bifurcations due to 3-D to 2-D projection, occlusion from other anatomies, etc...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28382316/connecting-markov-random-fields-and-active-contour-models-application-to-gland-segmentation-and-classification
#15
Jun Xu, James P Monaco, Rachel Sparks, Anant Madabhushi
We introduce a Markov random field (MRF)-driven region-based active contour model (MaRACel) for histological image segmentation. This Bayesian segmentation method combines a region-based active contour (RAC) with an MRF. State-of-the-art RAC models assume that every spatial location in the image is statistically independent, thereby ignoring valuable contextual information among spatial locations. To address this shortcoming, we incorporate an MRF prior into energy term of the RAC. This requires a formulation of the Markov prior consistent with the continuous variational framework characteristic of active contours; consequently, we introduce a continuous analog to the discrete Potts model...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28382315/supervised-classification-of-etoposide-treated-in-vitro-adherent-cells-based-on-noninvasive-imaging-morphology
#16
Anna Leida Mölder, Johan Persson, Zahra El-Schich, Silvester Czanner, Anette Gjörloff-Wingren
Single-cell studies using noninvasive imaging is a challenging, yet appealing way to study cellular characteristics over extended periods of time, for instance to follow cell interactions and the behavior of different cell types within the same sample. In some cases, e.g., transplantation culturing, real-time cellular monitoring, stem cell studies, in vivo studies, and embryo growth studies, it is also crucial to keep the sample intact and invasive imaging using fluorophores or dyes is not an option. Computerized methods are needed to improve throughput of image-based analysis and for use with noninvasive microscopy such methods are poorly developed...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28382314/training-a-cell-level-classifier-for-detecting-basal-cell-carcinoma-by-combining-human-visual-attention-maps-with-low-level-handcrafted-features
#17
Germán Corredor, Jon Whitney, Viviana Arias, Anant Madabhushi, Eduardo Romero
Computational histomorphometric approaches typically use low-level image features for building machine learning classifiers. However, these approaches usually ignore high-level expert knowledge. A computational model (M_im) combines low-, mid-, and high-level image information to predict the likelihood of cancer in whole slide images. Handcrafted low- and mid-level features are computed from area, color, and spatial nuclei distributions. High-level information is implicitly captured from the recorded navigations of pathologists while exploring whole slide images during diagnostic tasks...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28331891/differentiation-of-arterioles-from-venules-in-mouse-histology-images-using-machine-learning
#18
J Sachi Elkerton, Yiwen Xu, J Geoffrey Pickering, Aaron D Ward
Analysis and morphological comparison of the arteriolar and venular components of a microvascular network are essential to our understanding of multiple diseases affecting every organ system. We have developed and evaluated the first fully automatic software system for differentiation of arterioles from venules on high-resolution digital histology images of the mouse hind limb immunostained with smooth muscle [Formula: see text]-actin. Classifiers trained on statistical and morphological features by supervised machine learning provided useful classification accuracy for differentiation of arterioles from venules, achieving an area under the receiver operating characteristic curve of 0...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28331890/predicting-and-replacing-the-pathological-gleason-grade-with-automated-gland-ring-morphometric-features-from-immunofluorescent-prostate-cancer-images
#19
Faisal M Khan, Richard Scott, Michael Donovan, Gerardo Fernandez
The Gleason grade is the most common architectural and morphological assessment of prostate cancer severity and prognosis. There have been numerous algorithms developed to approximate and duplicate the Gleason scoring system, mostly developed in standard H&E brightfield microscopy. Immunofluorescence (IF) image analysis of tissue pathology has recently been proven to be robust in developing prognostic assessments of disease, particularly in prostate cancer. We leverage a method of segmenting gland rings in IF images for predicting the pathological Gleason, both the clinical and the image specific grades, which may not necessarily be the same...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28331889/unsupervised-labeling-of-glomerular-boundaries-using-gabor-filters-and-statistical-testing-in-renal-histology
#20
Brandon Ginley, John E Tomaszewski, Rabi Yacoub, Feng Chen, Pinaki Sarder
The glomerulus is the blood filtering unit of the kidney. Each human kidney contains [Formula: see text] glomeruli. Several renal conditions originate from structural damage to glomerular microcompartments, such as proteinuria, the excessive loss of blood proteins into urine. The gold standard for evaluating structural damage in renal pathology is histopathological and immunofluorescence examination of needle biopsies under a light microscope. This method is limited by qualitative or semiquantitative manual scoring approaches to the evaluation of glomerular structural features...
April 2017: Journal of Medical Imaging
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