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Rising of deep learning methodologies draws huge attention to their application in image processing and classification. Catching up the trends, this study briefly presents state-of-the-art of deep learning applications in medical imaging interfered with achievements of blood vessel segmentation methods in neurosensory retinal fundus images. Successful segmentation based on deep learning offers advantage...
Reliable, fast and efficient optic disc localization and blood-vessel detection are the primary tasks in computer analyses of retinal image. Most of the existing algorithms suffer due to inconsistent image contrast, varying individual condition, noises and computational complexity. This paper presents an algorithm to automatically detect landmark features of retinal image, such as optic disc and blood...
In the recent years, reconstructing 3D liver and its vessels from abdominal CT volume images becomes an inevitable and necessary research field. In this paper, a method of 3D reconstruction of liver with its vessels has been implemented, which involves volume preprocessing, de-noising, segmentation, contouring, and combination of different modalities. An advanced liver segmentation algorithms have...
This paper introduces a novel procedure to segment retinal vessels using new technique namely Morphological Angular Scale-Space (MASS). Line structuring element is rotated about the seed point to determine the curvature of the vessels thereby ensuring that the components remains connected along vessels segmented. Scale-Space is created by varying the length of the structuring element which gradually...
In the context of vessel tree structures segmentation with implicit deformable models, we propose to exploit convolution surfaces to introduce a novel variational formulation, robust to bifurcations, tangential vessels and aneurysms. Vessels are represented by an implicit function resulting from the convolution of the centerlines of the vessels, modeled as a second implicit function, with localized...
Micro-tomography produces high resolution images of biological structures such as vascular networks. In this paper, we present a new approach for segmenting vascular network into pathological and normal regions from considering their micro-vessel 3D structure only. We define and use a conditional random field for segmenting the output of a watershed algorithm. The tumoral and normal classes are thus...
A reliable and accurate method to measure the width of retinal blood vessel in fundus photography is proposed in this paper. Our approach is based on a graph-theoretic algorithm. The two boundaries of the same blood vessel are segmented simultaneously by converting the two-boundary segmentation problem into a two-slice, three-dimension surface segmentation problem, which is further converted into...
Segmentation of 3D cerebral vasculature is important for clinical diagnosis. However, many relevant thin vessels are not visible in 1.5T and 3T MRA. With the recent introduction of 7T MRA, images of higher resolution can be acquired, which contain much more thin vessels. We propose a fully automatic hybrid approach for segmenting vessels from 7T MRA images of the human cerebrovascular system. First,...
Segmentation of cerebral vascular networks from 3D angiographic data remains a challenge. Automation generally induces a high computational cost and possible errors, while interactive methods are hard to use due to the dimension and complexity of images. This article presents a compromise between both approaches, by using the concept of example-based segmentation. Segmentation examples of vascular...
This paper presents a novel unsupervised vascular segmentation algorithm which is applied to retinal fundus images, however could be generalised to any two-dimensional vascular image. The algorithm presents a new fully automatic framework for vessel segmentation and comprises the following features: novel application of the NPWindows method for intensity distribution estimation on localised `image...
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...
This paper describes a methodology for integrating confocal microscopy images acquired from two different illumination sources over time obtained from experiments of cell migration and vessel formation. The three-dimensional fluorescent images of stained cell nuclei and two dimensional bright field images of the extracellular matrix, supplement one another as the outline of the conduit formed by the...
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...
The segmentation and classification of the major intra-hepatic blood vessels are critical for the robust identification of the segmental anatomy of the liver. We propose a novel 4D graph-based method to segment and label the hepatic and portal veins. The algorithm uses multi-phase CT images to model the differential enhancement of the liver structures and Hessian-based vesselness likelihood to avoid...
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...
The aim of this study is to automatically detect the boundary of vessel walls in optical coherence tomography (OCT) sequences. We developed a new method to eliminate guide-wire shadow artifacts and accurately estimate the vessel wall. The estimation of the position of the guide-wire is the key concept for the elimination of guide-wire shadow artifacts. After identification of the artifacts we propose...
Blood vessel segmentation, that is, extraction of the center lines and corresponding local cylinder radii are important for the study of vascular diseases, and in the brain also important for the modeling and understanding of relationships between hemodynamics and electrical neural activity. Several image processing methods have been proposed for vessel extraction in many domains including those that...
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...
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