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Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society

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https://www.readbyqxmd.com/read/30390573/evaluation-of-accuracy-of-automatic-out-of-plane-respiratory-gating-for-dceus-based-quantification-using-principal-component-analysis
#1
Diya Wang, Zhe Su, Qiang Su, Xinyu Zhang, Zhen Qu, Na Wang, Yujin Zong, Yaning Yang, Mingxi Wan
The accuracy of abdominal multi-parametric quantification based on dynamic contrast-enhanced ultrasound (DCEUS) is limited by out-of-plane severe distortion induced by respiratory motion. This study developed a fully automatic respiratory gating scheme by using principal component analysis to remove distortions and disturbances in free-breathing DCEUS-based quantification. Taking the known in-vitro perfusions as ground truths, we further evaluated the respiratory gating accuracy from multiple perspectives in a controllable rotary distortion flow model with out-of-plane severe distortion induced by respiratory motion...
October 22, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/30366215/multiswarm-heterogeneous-binary-pso-using-win-win-approach-for-improved-feature-selection-in-liver-and-kidney-disease-diagnosis
#2
S Gunasundari, S Janakiraman, S Meenambal
Feature selection is a significant preprocessing method in the classification part of an expert system. We propose a new Multiswarm Heterogeneous Binary Particle Swarm Optimization algorithm (MHBPSO) using a Win-Win approach to improve the performance of Binary Particle Swarm Optimization algorithm (BPSO) for feature selection. MHBPSO is a cooperation algorithm, which includes BPSO and its three variants such as Boolean PSO (BoPSO), Self Adjusted Hierarchical Boolean PSO (SAHBoPSO), and Catfish Self Adjusted Hierarchical Boolean PSO (CSAHBoPSO)...
October 17, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/30326367/survey-of-automated-multiple-sclerosis-lesion-segmentation-techniques-on-magnetic-resonance-imaging
#3
REVIEW
Antonios Danelakis, Theoharis Theoharis, Dimitrios A Verganelakis
Multiple sclerosis (MS) is a chronic disease. It affects the central nervous system and its clinical manifestation can variate. Magnetic Resonance Imaging (MRI) is often used to detect, characterize and quantify MS lesions in the brain, due to the detailed structural information that it can provide. Manual detection and measurement of MS lesions in MRI data is time-consuming, subjective and prone to errors. Therefore, multiple automated methodologies for MRI-based MS lesion segmentation have been proposed. Here, a review of the state-of-the-art of automatic methods available in the literature is presented...
October 5, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/30340094/alzheimer-s-disease-diagnosis-based-on-multiple-cluster-dense-convolutional-networks
#4
Fan Li, Manhua Liu
Alzheimer's disease (AD) is an irreversible neurodegenerative disorder with progressive impairment of memory and cognitive functions. Structural magnetic resonance images (MRI) play important role to evaluate the brain anatomical changes for AD Diagnosis. Machine learning technologies have been widely studied on MRI computation and analysis for quantitative evaluation and computer-aided-diagnosis of AD. Most existing methods extract the hand-craft features after image processing such as registration and segmentation, and then train a classifier to distinguish AD subjects from other groups...
October 2, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/30340095/automatic-localization-of-normal-active-organs-in-3d-pet-scans
#5
Saeedeh Afshari, Aïcha BenTaieb, Ghassan Hamarneh
PET imaging captures the metabolic activity of tissues and is commonly visually interpreted by clinicians for detecting cancer, assessing tumor progression, and evaluating response to treatment. To automate accomplishing these tasks, it is important to distinguish between normal active organs and activity due to abnormal tumor growth. In this paper, we propose a deep learning method to localize and detect normal active organs visible in a 3D PET scan field-of-view. Our method adapts the deep network architecture of YOLO to detect multiple organs in 2D slices and aggregates the results to produce semantically labeled 3D bounding boxes...
September 29, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/30296625/multi-dimensional-proprio-proximus-machine-learning-for-assessment-of-myocardial-infarction
#6
Feng Yang, Xulei Yang, Soo Kng Teo, Gary Lee, Liang Zhong, Ru San Tan, Yi Su
This work presents a novel analysis methodology that utilises high-resolution, multi-dimensional information to better classify regions of the left ventricle after myocardial infarction. Specifically, the focus is to determine degree of infarction in regions of the left ventricle based on information extracted from cardiac magnetic resonance imaging. Enhanced classification accuracy is achieved using three mechanisms: Firstly, a plurality of indices/features is used in the pattern classification process, rather than a single index/feature (hence the term "multi-dimensional)...
September 26, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/30286332/magnetic-resonance-angiography-contrast-enhancement-and-combined-3d-visualization-of-cerebral-vasculature-and-white-matter-pathways
#7
Hans-H Ehricke, Till-K Hauser, Thomas Nägele, Thomas Schult, Uwe Klose
Recently, in diffusion magnetic resonance imaging, the reconstruction and three-dimensional rendering of white matter pathways have been introduced to clinical routine protocols. In a number of clinical situations, for example the preoperative analysis of vascular pathologies, the assessment of spatial relations between vascular structures and nearby fiber pathways is of vital interest for treatment planning. In this paper, we present an approach to the integrated vessel and fiber visualization, based on a novel vascular contrast enhancement operator for Magnetic Resonance Angiography (MRA) datasets...
September 25, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/30273831/3d-imaging-system-for-respiratory-monitoring-in-pediatric-intensive-care-environment
#8
Haythem Rehouma, Rita Noumeir, Wassim Bouachir, Philippe Jouvet, Sandrine Essouri
Assessment of respiratory activity in pediatric intensive care unit allows a comprehensive view of the patient's condition. This allows the identification of high-risk cases for prompt and appropriate medical treatment. Numerous research works on respiration monitoring have been conducted in recent years. However, most of them are unsuitable for clinical environment or require physical contact with the patient, which limits their efficiency. In this paper, we present a novel system for measuring the breathing pattern based on a computer vision method and contactless design...
September 25, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/30296626/an-iterative-multi-atlas-patch-based-approach-for-cortex-segmentation-from-neonatal-mri
#9
Carlos Tor-Díez, Nicolas Passat, Isabelle Bloch, Sylvain Faisan, Nathalie Bednarek, François Rousseau
Brain structure analysis in the newborn is a major health issue. This is especially the case for preterm neonates, in order to obtain predictive information related to the child development. In particular, the cortex is a structure of interest, that can be observed in magnetic resonance imaging (MRI). However, neonatal MRI data present specific properties that make them challenging to process. In this context, multi-atlas approaches constitute an efficient strategy, taking advantage of images processed beforehand...
September 22, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/30292910/sd-cnn-a-shallow-deep-cnn-for-improved-breast-cancer-diagnosis
#10
Fei Gao, Teresa Wu, Jing Li, Bin Zheng, Lingxiang Ruan, Desheng Shang, Bhavika Patel
Breast cancer is the second leading cause of cancer death among women worldwide. Nevertheless, it is also one of the most treatable malignances if detected early. Screening for breast cancer with full field digital mammography (FFDM) has been widely used. However, it demonstrates limited performance for women with dense breasts. An emerging technology in the field is contrast-enhanced digital mammography (CEDM), which includes a low energy (LE) image similar to FFDM, and a recombined image leveraging tumor neoangiogenesis similar to breast magnetic resonance imaging (MRI)...
September 22, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/30286333/deep-learning-nuclei-detection-a-simple-approach-can-deliver-state-of-the-art-results
#11
Henning Höfener, André Homeyer, Nick Weiss, Jesper Molin, Claes F Lundström, Horst K Hahn
BACKGROUND: Deep convolutional neural networks have become a widespread tool for the detection of nuclei in histopathology images. Many implementations share a basic approach that includes generation of an intermediate map indicating the presence of a nucleus center, which we refer to as PMap. Nevertheless, these implementations often still differ in several parameters, resulting in different detection qualities. METHODS: We identified several essential parameters and configured the basic PMap approach using combinations of them...
September 17, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/30253305/semi-automatic-lymphoma-detection-and-segmentation-using-fully-conditional-random-fields
#12
Yuntao Yu, Pierre Decazes, Jérôme Lapuyade-Lahorgue, Isabelle Gardin, Pierre Vera, Su Ruan
The detection and delineation of the lymphoma volume are a critical step for its treatment and its outcome prediction. Positron Emission Tomography (PET) is widely used for lymphoma detection. Two common types of approaches can be distinguished for lymphoma detection and segmentation in PET. The first one is ROI dependent which needs a ROI defined by physicians. The second one is based on machine learning methods which need a large learning database. However, such a large standard database is quite rare in medical field...
September 17, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/30359946/hybrid-method-combining-superpixel-random-walk-and-active-contour-model-for-fast-and-accurate-liver-segmentation
#13
Ye Yuan, Yen-Wei Chen, Chunhua Dong, Hai Yu, Zhiliang Zhu
Organ segmentation is an important pre-processing step in surgery planning and computer-aided diagnosis. In this paper, we propose a fast and accurate liver segmentation framework. Our proposed method combines a knowledge-based slice-by-slice Random Walk (RW) segmentation algorithm (proposed in our previous work) with a superpixel algorithm called the Contrast-enhanced Compact Watershed (CCWS) method to reduce computing time and memory costs. Compared to the commonly used Simple Linear Iterative Clustering (SLIC), we demonstrate that our CCWS is more appropriate for liver segmentation...
September 15, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/30273832/adaptive-fusion-of-texture-based-grading-for-alzheimer-s-disease-classification
#14
Kilian Hett, Vinh-Thong Ta, José V Manjón, Pierrick Coupé
Alzheimer's disease is a neurodegenerative process leading to irreversible mental dysfunctions. To date, diagnosis is established after incurable brain structure alterations. The development of new biomarkers is crucial to perform an early detection of this disease. With the recent improvement of magnetic resonance imaging, numerous methods were proposed to improve computer-aided detection. Among these methods, patch-based grading framework demonstrated state-of-the-art performance. Usually, methods based on this framework use intensity or grey matter maps...
September 7, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/30268005/practical-guidelines-for-handling-head-and-neck-computed-tomography-artifacts-for-quantitative-image-analysis
#15
Rachel B Ger, Daniel F Craft, Dennis S Mackin, Shouhao Zhou, Rick R Layman, A Kyle Jones, Hesham Elhalawani, Clifton D Fuller, Rebecca M Howell, Heng Li, R Jason Stafford, Laurence E Court
Radiomics studies have demonstrated the potential use of quantitative image features to improve prognostic stratification of patients with head and neck cancer. Imaging protocol parameters that can affect radiomics feature values have been investigated, but the effects of artifacts caused by intrinsic patient factors have not. Two such artifacts that are common in patients with head and neck cancer are streak artifacts caused by dental fillings and beam-hardening artifacts caused by bone. The purpose of this study was to test the impact of these artifacts and if needed, methods for compensating for these artifacts in head and neck radiomics studies...
November 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/30243216/an-em-based-semi-supervised-deep-learning-approach-for-semantic-segmentation-of-histopathological-images-from-radical-prostatectomies
#16
Jiayun Li, William Speier, King Chung Ho, Karthik V Sarma, Arkadiusz Gertych, Beatrice S Knudsen, Corey W Arnold
Automated Gleason grading is an important preliminary step for quantitative histopathological feature extraction. Different from the traditional task of classifying small pre-selected homogeneous regions, semantic segmentation provides pixel-wise Gleason predictions across an entire slide. Deep learning-based segmentation models can automatically learn visual semantics from data, which alleviates the need for feature engineering. However, performance of deep learning models is limited by the scarcity of large-scale fully annotated datasets, which can be both expensive and time-consuming to create...
November 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/30237146/optimal-multi-object-segmentation-with-novel-gradient-vector-flow-based-shape-priors
#17
Junjie Bai, Abhay Shah, Xiaodong Wu
Shape priors have been widely utilized in medical image segmentation to improve segmentation accuracy and robustness. A major way to encode such a prior shape model is to use a mesh representation, which is prone to causing self-intersection or mesh folding. Those problems require complex and expensive algorithms to mitigate. In this paper, we propose a novel shape prior directly embedded in the voxel grid space, based on gradient vector flows of a pre-segmentation. The flexible and powerful prior shape representation is ready to be extended to simultaneously segmenting multiple interacting objects with minimum separation distance constraint...
November 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/30237145/efficient-multi-kernel-multi-instance-learning-using-weakly-supervised-and-imbalanced-data-for-diabetic-retinopathy-diagnosis
#18
Peng Cao, Fulong Ren, Chao Wan, Jinzhu Yang, Osmar Zaiane
OBJECTIVE: Diabetic retinopathy (DR) is one of the most serious complications of diabetes. Early detection and treatment of DR are key public health interventions that can significantly reduce the risk of vision loss. How to effectively screen and diagnose the retinal fundus image in order to identify retinopathy in time is a major challenge. In the traditional DR screening system, the accuracy of micro-aneurysm (MA) and hemorrhagic (H) lesion detection determines the final screening performance...
November 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/30219737/case-control-comparison-brain-lesion-segmentation-for-early-infarct-detection
#19
Fung Fung Ting, Kok Swee Sim, Chee Peng Lim
Computed Tomography (CT) images are widely used for the identification of abnormal brain tissues following infarct and hemorrhage of a stroke. The treatment of this medical condition mainly depends on doctors' experience. While manual lesion delineation by medical doctors is currently considered as the standard approach, it is time-consuming and dependent on each doctor's expertise and experience. In this study, a case-control comparison brain lesion segmentation (CCBLS) method is proposed to segment the region pertaining to brain injury by comparing the voxel intensity of CT images between control subjects and stroke patients...
November 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/30212736/a-wavelet-gradient-sparsity-based-algorithm-for-reconstruction-of-reduced-view-tomography-datasets-obtained-with-a-monochromatic-synchrotron-based-x-ray-source
#20
S Ali Melli, Khan A Wahid, Paul Babyn, David M L Cooper, Ahmed M Hasan
High-resolution synchrotron computed tomography (CT) is very helpful in the diagnosis and monitor of chronic diseases including osteoporosis. Osteoporosis is characterized by low bone mass and cortical bone porosity best imaged with CT. Synchrotron CT requires a large number of angular projections to reconstruct images with high resolution for detailed and accurate diagnosis. However, this poses great risks and challenges for serial in-vivo human and animal imaging due to a large amount of X-ray radiation dose required that can damage living specimens...
November 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
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