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The problem of reconstructing an MR image from limited (and sparsely sampled) k-space data in the presence of a reference image occurs in various applications, including interventional imaging and dynamic contrast-enhanced imaging. This paper addresses the problem using a dictionary composed of three types of basis functions: reference-weighted harmonic functions, wavelets, and pixel/voxel indicator...
The ocular fundus image can provide information on pathological changes caused by local ocular diseases and early signs of certain systemic diseases, such as diabetes and hypertension. Automated analysis and interpretation of fundus images has become a necessary and important diagnostic procedure in ophthalmology. The extraction of blood vessels from retinal images is an important and challenging...
Three-dimensional X-Ray Micromotomography (3D μCT) has become an important tool to investigate bone morphology. Several investigators have searched a standard method for determining the optimal threshold value (optimal TH) to segment microtomographic images and quantify the bone morphology. The Conventional methods are based on subjective methods, and it is possible to obtain under or overestimated...
Fundus Fluorescein Angiography (FA) is a powerful tool for imaging and evaluating Diabetic Macular Edema (DME), where the fluorescein dye leaks and accumulates in the diseased areas. Currently, the assessment of FA images is qualitative and suffers from large inter-observer variability. A necessary step towards quantitative assessment of DME is automatic segmentation of fluorescein leakage. In this...
Segmentation of tree-like structure within medical imaging modalities, such as x-ray, MRI, ultrasound, etc., is an important step for analyzing branching patterns involved in many anatomic structures. However, images acquired using these different acquisition techniques frequently have features of poor contrast, blurring and noise, and therefore the segmentation result of traditional image segmentation...
In this study, we aim at reconstructing single photon emission computed tomography (SPECT) images using a Bayesian framework to incorporate anatomical information from magnetic resonance (MR) as a priori knowledge about the activity distribution. This is achieved using an anatomically-driven Bowsher prior (BP). Standard BP has the potential to obtain similar results as other state-of-the-art prior...
We present a hierarchical Markov Random Field (HMRF) for multi-label image segmentation. With such a hierarchical model, we can incorporate global knowledge into our segmentation algorithm. Solving the MRF is formulated as a MAX-SUM problem for which there exist efficient solvers based on linear programming. We show that our method allows for automatic segmentation of mast cells and their cell organelles...
This contribution presents a method for automatic detection of excitatory, asymmetric synapses and segmentation of synaptic junctional complexes in stacks of serial electron microscopy images with nearly isotropic resolution. The method uses a Random Forest classifier in the space of generic image features, computed directly in the 3D neighborhoods of each pixel, and an additional step of interactive...
We present an intensity neighborhood-based system for segmenting arbitrary biomedical image datasets using supervised learning. Because neighborhood methods are often associated with high-dimensional feature vectors, we explore a Principal Component Analysis (PCA) based method to reduce the dimensionality (and provide computational savings) of each neighborhood. Our results show that the system can...
Automatic microscopy for screening of sputum smears for tuberculosis would reduce the reliance on technicians in heavily burdened laboratories in poorly-resourced countries. Autofocusing is a key component of automated microscopy. We investigate the use of wavelet-based image fusion for automatic focusing of sputum smear slides as a component of automated fluorescence microscopy to identify Mycobacterium...
Choroidal Neovascularization (CNV) is a severe retinal disease characterized by abnormal growth of blood vessels in the choroidal layer. Current diagnosis of CNV depends mainly on qualitative assessment of a temporal sequence of fundus fluorescein angiography images. Automated segmentation and identification of the CNV lesion types (either occult or classic) is required to reduce the inter-and intra-...
The proper segmentation of the vascular system of the retina currently attracts wide interest. As a precious outcome, a successful segmentation may lead to the improvement of automatic screening systems. Namely, the detection of the vessels helps the localization of other anatomical parts and lesions besides the vascular disorders. In this paper, we recommend a novel approach for the segmentation...
The current study presents an automatic algorithm for detection of myocardial infarction and ischemia using cardiac CT image data. The classification is based on probabilistic tissue modeling, where a pixel is classified according to its maximum a-posteriori probability (MAP) as belonging to a normal or abnormal tissue segment. The pixels are represented in a two-dimensional space, where the first...
Cardiac magnetic resonance imaging has proved its effectiveness to determine the patient-specific myocardial motion/functional information via the cine imaging and to detect myocardial infarction in the delayed enhanced MRI (DE-MRI). Standard cardiac MR protocols usually acquire these two sets of images across multiple acquisitions with varying imaging slice geometry, pixel spacing and different breath-holdings,...
This paper presents a geodesic voting method to segment tree structures, such as cardiac or cerebral blood vessels. Many authors have used minimal cost paths, or similarly geodesics relative to a weight potential P, to find a vessel between two end points. Our goal focuses on the use of a set of such geodesic paths for finding a tubular tree structure, using minimal interaction. This work adapts the...
Investigating multi-feature information-theoretic image registration, we introduce consistent and asymptotically unbiased kth-nearest neighbor (kNN) estimators of mutual information (MI), normalized MI and exclusive information applicable to high-dimensional random variables, and derive under closed-form their gradient flows over finite- and infinite-dimensional transform spaces. Using these results,...
The registration of breast DCE-MR images can help correct possible motions during image acquisition, and is also important for diagnosis of breast cancer, i.e., discrimination between benign and malignant tumors. However, deformable registration of DCE-MR images is challenging due to drastic image contrast change over time (especially between pre- and post-contrast images). To improve the registration,...
Arterial spin labeling (ASL) allows non-invasive imaging and quantification of brain perfusion by magnetically labeling blood in the brain-feeding arteries. ASL has been used to study cerebrovascular diseases, brain tumors and neurodegenerative disorders as well as for functional imaging. The use of a perfusion template could be of great interest to study inter-subject regional variation of perfusion...
In this paper we compare different approaches to combine color and statistical texture descriptors. Previous studies on this topic were conducted on natural images only. We focus on the particular case of histological datasets where color plays an important role due to the staining process of the biological samples. We also introduce two new variants of the well-known Local Binary Patterns (LBP) operator...
Analyzing high-resolution images of astrocytes is important in understanding the diseases, such as glaucoma and retinal detachment, to which astrocytes are known to become reactive. This is challenging because the cells are small, homogeneous, and closely packed. We propose an interactive visualization system designed for such images. Our system employs a probabilistic segmentation algorithm to help...
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