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Medical Image Analysis

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https://www.readbyqxmd.com/read/28314191/robust-detection-and-segmentation-of-cell-nuclei-in-biomedical-images-based-on-a-computational-topology-framework
#1
Rodrigo Rojas-Moraleda, Wei Xiong, Niels Halama, Katja Breitkopf-Heinlein, Steven Dooley, Luis Salinas, Dieter W Heermann, Nektarios A Valous
The segmentation of cell nuclei is an important step towards the automated analysis of histological images. The presence of a large number of nuclei in whole-slide images necessitates methods that are computationally tractable in addition to being effective. In this work, a method is developed for the robust segmentation of cell nuclei in histological images based on the principles of persistent homology. More specifically, an abstract simplicial homology approach for image segmentation is established. Essentially, the approach deals with the persistence of disconnected sets in the image, thus identifying salient regions that express patterns of persistence...
March 6, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28282641/dual-discriminative-local-coding-for-tissue-aging-analysis
#2
Yang Song, Qing Li, Fan Zhang, Heng Huang, Dagan Feng, Yue Wang, Mei Chen, Weidong Cai
In aging research, morphological age of tissue helps to characterize the effects of aging on different individuals. While currently manual evaluations are used to estimate morphological ages under microscopy, such operation is difficult and subjective due to the complex visual characteristics of tissue images. In this paper, we propose an automated method to quantify morphological ages of tissues from microscopy images. We design a new sparse representation method, namely dual discriminative local coding (DDLC), that classifies the tissue images into different chronological ages...
February 27, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28282640/longitudinal-segmentation-of-age-related-white-matter-hyperintensities
#3
Carole H Sudre, M Jorge Cardoso, Sebastien Ourselin
Although white matter hyperintensities evolve in the course of ageing, few solutions exist to consider the lesion segmentation problem longitudinally. Based on an existing automatic lesion segmentation algorithm, a longitudinal extension is proposed. For evaluation purposes, a longitudinal lesion simulator is created allowing for the comparison between the longitudinal and the cross-sectional version in various situations of lesion load progression. Finally, applied to clinical data, the proposed framework demonstrates an increased robustness compared to available cross-sectional methods and findings are aligned with previously reported clinical patterns...
February 24, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28279915/low-rank-and-sparse-decomposition-based-shape-model-and-probabilistic-atlas-for-automatic-pathological-organ-segmentation
#4
Changfa Shi, Yuanzhi Cheng, Jinke Wang, Yadong Wang, Kensaku Mori, Shinichi Tamura
One major limiting factor that prevents the accurate delineation of human organs has been the presence of severe pathology and pathology affecting organ borders. Overcoming these limitations is exactly what we are concerned in this study. We propose an automatic method for accurate and robust pathological organ segmentation from CT images. The method is grounded in the active shape model (ASM) framework. It leverages techniques from low-rank and sparse decomposition (LRSD) theory to robustly recover a subspace from grossly corrupted data...
February 22, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28242473/task-fmri-data-analysis-based-on-supervised-stochastic-coordinate-coding
#5
Jinglei Lv, Binbin Lin, Qingyang Li, Wei Zhang, Yu Zhao, Xi Jiang, Lei Guo, Junwei Han, Xintao Hu, Christine Guo, Jieping Ye, Tianming Liu
Task functional magnetic resonance imaging (fMRI) has been widely employed for brain activation detection and brain network analysis. Modeling rich information from spatially-organized collection of fMRI time series is challenging because of the intrinsic complexity. Hypothesis-driven methods, such as the general linear model (GLM), which regress exterior stimulus from voxel-wise functional brain activity, are limited due to overlooking the complexity of brain activities and the diversity of concurrent brain networks...
February 20, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28273512/multi-resolution-multi-object-statistical-shape-models-based-on-the-locality-assumption
#6
Matthias Wilms, Heinz Handels, Jan Ehrhardt
Statistical shape models learned from a population of previously observed training shapes are nowadays widely used in medical image analysis to aid segmentation or classification. However, providing an appropriate and representative training population of preferably manual segmentations is typically either very labor-intensive or even impossible. Therefore, statistical shape models in practice frequently suffer from the high-dimension-low-sample-size (HDLSS) problem resulting in models with insufficient expressiveness...
February 17, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28282642/shape-aware-surface-reconstruction-from-sparse-3d-point-clouds
#7
Florian Bernard, Luis Salamanca, Johan Thunberg, Alexander Tack, Dennis Jentsch, Hans Lamecker, Stefan Zachow, Frank Hertel, Jorge Goncalves, Peter Gemmar
The reconstruction of an object's shape or surface from a set of 3D points plays an important role in medical image analysis, e.g. in anatomy reconstruction from tomographic measurements or in the process of aligning intra-operative navigation and preoperative planning data. In such scenarios, one usually has to deal with sparse data, which significantly aggravates the problem of reconstruction. However, medical applications often provide contextual information about the 3D point data that allow to incorporate prior knowledge about the shape that is to be reconstructed...
February 14, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28219833/statistical-appearance-models-based-on-probabilistic-correspondences
#8
Julia Krüger, Jan Ehrhardt, Heinz Handels
Model-based image analysis is indispensable in medical image processing. One key aspect of building statistical shape and appearance models is the determination of one-to-one correspondences in the training data set. At the same time, the identification of these correspondences is the most challenging part of such methods. In our earlier work, we developed an alternative method using correspondence probabilities instead of exact one-to-one correspondences for a statistical shape model (Hufnagel et al., 2008)...
February 7, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28171807/a-deep-learning-approach-for-the-analysis-of-masses-in-mammograms-with-minimal-user-intervention
#9
Neeraj Dhungel, Gustavo Carneiro, Andrew P Bradley
We present an integrated methodology for detecting, segmenting and classifying breast masses from mammograms with minimal user intervention. This is a long standing problem due to low signal-to-noise ratio in the visualisation of breast masses, combined with their large variability in terms of shape, size, appearance and location. We break the problem down into three stages: mass detection, mass segmentation, and mass classification. For the detection, we propose a cascade of deep learning methods to select hypotheses that are refined based on Bayesian optimisation...
January 28, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28167394/deep-ensemble-learning-of-sparse-regression-models-for-brain-disease-diagnosis
#10
Heung-Il Suk, Seong-Whan Lee, Dinggang Shen
Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications...
January 24, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28161567/multi-resolution-cell-orientation-congruence-descriptors-for-epithelium-segmentation-in-endometrial-histology-images
#11
Guannan Li, Shan E Ahmed Raza, Nasir M Rajpoot
It has been recently shown that recurrent miscarriage can be caused by abnormally high ratio of number of uterine natural killer (UNK) cells to the number of stromal cells in human female uterus lining. Due to high workload, the counting of UNK and stromal cells needs to be automated using computer algorithms. However, stromal cells are very similar in appearance to epithelial cells which must be excluded in the counting process. To exclude the epithelial cells from the counting process it is necessary to identify epithelial regions...
January 22, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28157660/adaptive-local-window-for-level-set-segmentation-of-ct-and-mri-liver-lesions
#12
Assaf Hoogi, Christopher F Beaulieu, Guilherme M Cunha, Elhamy Heba, Claude B Sirlin, Sandy Napel, Daniel L Rubin
We propose a novel method, the adaptive local window, for improving level set segmentation technique. The window is estimated separately for each contour point, over iterations of the segmentation process, and for each individual object. Our method considers the object scale, the spatial texture, and the changes of the energy functional over iterations. Global and local statistics are considered by calculating several gray level co-occurrence matrices. We demonstrate the capabilities of the method in the domain of medical imaging for segmenting 233 images with liver lesions...
January 13, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28104551/a-framework-for-analysis-of-linear-ultrasound-videos-to-detect-fetal-presentation-and-heartbeat
#13
M A Maraci, C P Bridge, R Napolitano, A Papageorghiou, J A Noble
Confirmation of pregnancy viability (presence of fetal cardiac activity) and diagnosis of fetal presentation (head or buttock in the maternal pelvis) are the first essential components of ultrasound assessment in obstetrics. The former is useful in assessing the presence of an on-going pregnancy and the latter is essential for labour management. We propose an automated framework for detection of fetal presentation and heartbeat from a predefined free-hand ultrasound sweep of the maternal abdomen. Our method exploits the presence of key anatomical sonographic image patterns in carefully designed scanning protocols to develop, for the first time, an automated framework allowing novice sonographers to detect fetal breech presentation and heartbeat from an ultrasound sweep...
January 10, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28104550/robust-estimation-of-carotid-artery-wall-motion-using-the-elasticity-based-state-space-approach
#14
Zhifan Gao, Huahua Xiong, Xin Liu, Heye Zhang, Dhanjoo Ghista, Wanqing Wu, Shuo Li
The dynamics of the carotid artery wall has been recognized as a valuable indicator to evaluate the status of atherosclerotic disease in the preclinical stage. However, it is still a challenge to accurately measure this dynamics from ultrasound images. This paper aims at developing an elasticity-based state-space approach for accurately measuring the two-dimensional motion of the carotid artery wall from the ultrasound imaging sequences. In our approach, we have employed a linear elasticity model of the carotid artery wall, and converted it into the state space equation...
January 10, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28131075/accurate-and-interpretable-classification-of-microspectroscopy-pixels-using-artificial-neural-networks
#15
Petru Manescu, Young Jong Lee, Charles Camp, Marcus Cicerone, Mary Brady, Peter Bajcsy
This paper addresses the problem of classifying materials from microspectroscopy at a pixel level. The challenges lie in identifying discriminatory spectral features and obtaining accurate and interpretable models relating spectra and class labels. We approach the problem by designing a supervised classifier from a tandem of Artificial Neural Network (ANN) models that identify relevant features in raw spectra and achieve high classification accuracy. The tandem of ANN models is meshed with classification rule extraction methods to lower the model complexity and to achieve interpretability of the resulting model...
January 6, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28208100/involuntary-eye-motion-correction-in-retinal-optical-coherence-tomography-hardware-or-software-solution
#16
Ahmadreza Baghaie, Zeyun Yu, Roshan M D'Souza
In this paper, we review state-of-the-art techniques to correct eye motion artifacts in Optical Coherence Tomography (OCT) imaging. The methods for eye motion artifact reduction can be categorized into two major classes: (1) hardware-based techniques and (2) software-based techniques. In the first class, additional hardware is mounted onto the OCT scanner to gather information about the eye motion patterns during OCT data acquisition. This information is later processed and applied to the OCT data for creating an anatomically correct representation of the retina, either in an offline or online manner...
April 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28160692/the-status-of-augmented-reality-in-laparoscopic-surgery-as-of-2016
#17
REVIEW
Sylvain Bernhardt, Stéphane A Nicolau, Luc Soler, Christophe Doignon
This article establishes a comprehensive review of all the different methods proposed by the literature concerning augmented reality in intra-abdominal minimally invasive surgery (also known as laparoscopic surgery). A solid background of surgical augmented reality is first provided in order to support the survey. Then, the various methods of laparoscopic augmented reality as well as their key tasks are categorized in order to better grasp the current landscape of the field. Finally, the various issues gathered from these reviewed approaches are organized in order to outline the remaining challenges of augmented reality in laparoscopic surgery...
April 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28160691/subject-specific-abnormal-region-detection-in-traumatic-brain-injury-using-sparse-model-selection-on-high-dimensional-diffusion-data
#18
Matineh Shaker, Deniz Erdogmus, Jennifer Dy, Sylvain Bouix
We present a method to estimate a multivariate Gaussian distribution of diffusion tensor features in a set of brain regions based on a small sample of healthy individuals, and use this distribution to identify imaging abnormalities in subjects with mild traumatic brain injury. The multivariate model receives apriori knowledge in the form of a neighborhood graph imposed on the precision matrix, which models brain region interactions, and an additional L1 sparsity constraint. The model is then estimated using the graphical LASSO algorithm and the Mahalanobis distance of healthy and TBI subjects to the distribution mean is used to evaluate the discriminatory power of the model...
April 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/27914302/progressive-multi-atlas-label-fusion-by-dictionary-evolution
#19
Yantao Song, Guorong Wu, Khosro Bahrami, Quansen Sun, Dinggang Shen
Accurate segmentation of anatomical structures in medical images is important in recent imaging based studies. In the past years, multi-atlas patch-based label fusion methods have achieved a great success in medical image segmentation. In these methods, the appearance of each input image patch is first represented by an atlas patch dictionary (in the image domain), and then the latent label of the input image patch is predicted by applying the estimated representation coefficients to the corresponding anatomical labels of the atlas patches in the atlas label dictionary (in the label domain)...
February 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/27898305/view-aligned-hypergraph-learning-for-alzheimer-s-disease-diagnosis-with-incomplete-multi-modality-data
#20
Mingxia Liu, Jun Zhang, Pew-Thian Yap, Dinggang Shen
Effectively utilizing incomplete multi-modality data for the diagnosis of Alzheimer's disease (AD) and its prodrome (i.e., mild cognitive impairment, MCI) remains an active area of research. Several multi-view learning methods have been recently developed for AD/MCI diagnosis by using incomplete multi-modality data, with each view corresponding to a specific modality or a combination of several modalities. However, existing methods usually ignore the underlying coherence among views, which may lead to sub-optimal learning performance...
February 2017: Medical Image Analysis
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