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Medical Image Analysis

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https://www.readbyqxmd.com/read/28208100/involuntary-eye-motion-correction-in-retinal-optical-coherence-tomography-hardware-or-software-solution
#1
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...
February 4, 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
#2
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
#3
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/28160692/the-status-of-augmented-reality-in-laparoscopic-surgery-as-of-2016
#4
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...
January 24, 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
#5
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...
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
#6
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
#7
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
#8
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
#9
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
#10
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/28011374/robust-cranial-cavity-segmentation-in-ct-and-ct-perfusion-images-of-trauma-and-suspected-stroke-patients
#11
Ajay Patel, Bram van Ginneken, Frederick J A Meijer, Ewoud J van Dijk, Mathias Prokop, Rashindra Manniesing
A robust and accurate method is presented for the segmentation of the cranial cavity in computed tomography (CT) and CT perfusion (CTP) images. The method consists of multi-atlas registration with label fusion followed by a geodesic active contour levelset refinement of the segmentation. Pre-registration atlas selection based on differences in anterior skull anatomy reduces computation time whilst optimising performance. The method was evaluated on a large clinical dataset of 573 acute stroke and trauma patients that received a CT or CTP in our hospital in the period February 2015-December 2015...
December 13, 2016: Medical Image Analysis
https://www.readbyqxmd.com/read/28006726/perfusion-deconvolution-in-dsc-mri-with-dispersion-compliant-bases
#12
Marco Pizzolato, Timothé Boutelier, Rachid Deriche
Perfusion imaging of the brain via Dynamic Susceptibility Contrast MRI (DSC-MRI) allows tissue perfusion characterization by recovering the tissue impulse response function and scalar parameters such as the cerebral blood flow (CBF), blood volume (CBV), and mean transit time (MTT). However, the presence of bolus dispersion causes the data to reflect macrovascular properties, in addition to tissue perfusion. In this case, when performing deconvolution of the measured arterial and tissue concentration time-curves it is only possible to recover the effective, i...
December 6, 2016: Medical Image Analysis
https://www.readbyqxmd.com/read/27940226/direct-and-simultaneous-estimation-of-cardiac-four-chamber-volumes-by-multioutput-sparse-regression
#13
Xiantong Zhen, Heye Zhang, Ali Islam, Mousumi Bhaduri, Ian Chan, Shuo Li
Cardiac four-chamber volume estimation serves as a fundamental and crucial role in clinical quantitative analysis of whole heart functions. It is a challenging task due to the huge complexity of the four chambers including great appearance variations, huge shape deformation and interference between chambers. Direct estimation has recently emerged as an effective and convenient tool for cardiac ventricular volume estimation. However, existing direct estimation methods were specifically developed for one single ventricle, i...
November 30, 2016: Medical Image Analysis
https://www.readbyqxmd.com/read/27940225/migration-and-interaction-tracking-for-quantitative-analysis-of-phagocyte-pathogen-confrontation-assays
#14
Susanne Brandes, Stefanie Dietrich, Kerstin Hünniger, Oliver Kurzai, Marc Thilo Figge
Invasive fungal infections are emerging as a significant health risk for humans. The innate immune system is the first line of defense against invading micro-organisms and involves the recruitment of phagocytes, which engulf and kill pathogens, to the site of infection. To gain a quantitative understanding of the interplay between phagocytes and fungal pathogens, live-cell imaging is a modern approach to monitor the dynamic process of phagocytosis in time and space. However, this requires the processing of large amounts of video data that is tedious to be performed manually...
November 25, 2016: Medical Image Analysis
https://www.readbyqxmd.com/read/27914302/progressive-multi-atlas-label-fusion-by-dictionary-evolution
#15
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
#16
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
https://www.readbyqxmd.com/read/27871000/comparison-of-atlas-based-techniques-for-whole-body-bone-segmentation
#17
Hossein Arabi, Habib Zaidi
We evaluate the accuracy of whole-body bone extraction from whole-body MR images using a number of atlas-based segmentation methods. The motivation behind this work is to find the most promising approach for the purpose of MRI-guided derivation of PET attenuation maps in whole-body PET/MRI. To this end, a variety of atlas-based segmentation strategies commonly used in medical image segmentation and pseudo-CT generation were implemented and evaluated in terms of whole-body bone segmentation accuracy. Bone segmentation was performed on 23 whole-body CT/MR image pairs via leave-one-out cross validation procedure...
February 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/27816859/cross-contrast-multi-channel-image-registration-using-image-synthesis-for-mr-brain-images
#18
Min Chen, Aaron Carass, Amod Jog, Junghoon Lee, Snehashis Roy, Jerry L Prince
Multi-modal deformable registration is important for many medical image analysis tasks such as atlas alignment, image fusion, and distortion correction. Whereas a conventional method would register images with different modalities using modality independent features or information theoretic metrics such as mutual information, this paper presents a new framework that addresses the problem using a two-channel registration algorithm capable of using mono-modal similarity measures such as sum of squared differences or cross-correlation...
February 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/27750189/assisting-the-examination-of-large-histopathological-slides-with-adaptive-forests
#19
Loïc Peter, Diana Mateus, Pierre Chatelain, Denis Declara, Noemi Schworm, Stefan Stangl, Gabriele Multhoff, Nassir Navab
The examination of biopsy samples plays a central role in the diagnosis and staging of numerous diseases, including most cancer types. However, because of the large size of the acquired images, the localization and quantification of diseased portions of a tissue is usually time-consuming, as pathologists must scroll through the whole slide to look for objects of interest which are often only scarcely distributed. In this work, we introduce an approach to facilitate the visual inspection of large digital histopathological slides...
January 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/27662597/probabilistic-tractography-using-lasso-bootstrap
#20
Chuyang Ye, Jerry L Prince
Diffusion magnetic resonance imaging (dMRI) can be used for noninvasive imaging of white matter tracts. Using fiber tracking, which propagates fiber streamlines according to fiber orientations (FOs) computed from dMRI, white matter tracts can be reconstructed for investigation of brain diseases and the brain connectome. Because of image noise, probabilistic tractography has been proposed to characterize uncertainties in FO estimation. Bootstrap provides a nonparametric approach to the estimation of FO uncertainties and residual bootstrap has been used for developing probabilistic tractography...
January 2017: Medical Image Analysis
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