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108
Robust level set method for medical image segmentation
"... Level set methods provide powerftil numerical techniques for analyzing and solving interface evolution problems based on partial differential equations. Level sets display interesting elastic behaviors and can handle topological changes. Although level set methods have many advantages, they still of ..."
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often face difficult challenges such as poor image contrast, noise, and missing or diffuse boundaries. The robust level set method of this paper is based on the anisotropic diffusion method. The fast marching method provides a fast implementation for level set methods, the anisotropic diffusion
Segmentation of Brain Tissue from Magnetic Resonance Images
, 1995
"... Segmentation of medical imagery is a challenging problem due to the complexity of the images, as well as to the absence of models of the anatomy that fully capture the possible deformations in each structure. Brain tissue is a particularly complex structure, and its segmentation is an important step ..."
Abstract
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Cited by 84 (3 self)
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Segmentation of medical imagery is a challenging problem due to the complexity of the images, as well as to the absence of models of the anatomy that fully capture the possible deformations in each structure. Brain tissue is a particularly complex structure, and its segmentation is an important
DeepMedic for Brain Tumor Segmentation
"... Abstract. Accurate automatic algorithms for the segmentation of brain tumours have the potential of improving disease diagnosis, treatment planning, as well as enabling large-scale studies of the pathology. In this work we employ DeepMedic [1], a 3D CNN architecture previously presented for lesion ..."
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Abstract. Accurate automatic algorithms for the segmentation of brain tumours have the potential of improving disease diagnosis, treatment planning, as well as enabling large-scale studies of the pathology. In this work we employ DeepMedic [1], a 3D CNN architecture previously presented for lesion
Robust segmentation of white matter tracts in the aging brain
"... Introduction: Understanding how normal aging affects brain function and structure is one of the most important challenges facing medical science. By allowing segmentation of tracts of interest from diffusion MRI (dMRI) data, tractography provides a potentially promising tool for assessing white matt ..."
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Introduction: Understanding how normal aging affects brain function and structure is one of the most important challenges facing medical science. By allowing segmentation of tracts of interest from diffusion MRI (dMRI) data, tractography provides a potentially promising tool for assessing white
Research Article Robust Vessel Segmentation in Fundus Images
"... Copyright © 2013 A. Budai et al.This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. One of themost commonmodalities to examine the human ..."
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eye is the eye-fundus photograph.The evaluation of fundus photographs is carried out by medical experts during time-consuming visual inspection. Our aim is to accelerate this process using computer aided diagnosis. As a first step, it is necessary to segment structures in the images for tissue
Model-Based Interpretation of 3D Medical Images
- In British Machine Vision Conference
, 1993
"... The automatic segmentation and labelling of anatomical structures in 3D medical imagesis a challenging task of practical importance. We describe amodel- based approach which allows robust and accurate interpretation using explicit anatomical knowledge. Our method is based on the extension to 3D o ..."
Abstract
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Cited by 45 (7 self)
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The automatic segmentation and labelling of anatomical structures in 3D medical imagesis a challenging task of practical importance. We describe amodel- based approach which allows robust and accurate interpretation using explicit anatomical knowledge. Our method is based on the extension to 3D
Over-Segmentation of 3D Medical Ima
"... markus.holzer, rene.d Abstract In this paper, we propose a novel approach to compute 3D supervoxels for radiological image datasets. It allows to cope with the high levels of noise and low contrast encountered in clinical data such as Computed Tomography (CT), Optical Coherence Tomography (OCT) and ..."
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to state of the art su-perpixel methods on the real-world images of the 2D Berkley Segmentation Dataset1 (BSD) converted to gray-scale, as well as challenging 3D CT and MR volumes of the Visceral2 dataset. It yields a highly regular, robust, homogeneous and edge-preserving over-segmentation of the image
Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation
"... Due to advances in the acquisition and analysis of medical imaging, it is currently possible to quantify the tumor phenotype. The emerging field of Radiomics addresses this issue by converting medical images into minable data by extracting a large number of quantitative imaging features. One of the ..."
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of the main challenges of Radiomics is tumor segmentation. Where manual delineation is time consuming and prone to inter-observer variability, it has been shown that semi-automated approaches are fast and reduce inter-observer variability. In this study, a semiautomatic region growing volumetric segmentation
YANG, YUAN, LI, YAN: MEDICAL IMAGE SEGMENTATION 1 Medical Image Segmentation Using Descriptive Image Features
"... Segmentation of medical images is an important component for diagnosis and treatment of diseases using medical imaging technologies. However, automated accurate medical image segmentation is still a challenge due to the difficulties in finding a robust feature descriptor to describe the object bound ..."
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Segmentation of medical images is an important component for diagnosis and treatment of diseases using medical imaging technologies. However, automated accurate medical image segmentation is still a challenge due to the difficulties in finding a robust feature descriptor to describe the object
Towards Robust and Efficient Automated Curvilinear Structure Detection in Medical Images
"... Segmenting dendritic trees and corneal nerve fibres is chal-lenging due to their uneven and irregular shape. Our first contribution is a novel ridge detector, SCIRD, which is ro-tation, scale and curvature invariant [2]. Then, we present a novel feature boosting method combining hand-crafted ap-pear ..."
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Segmenting dendritic trees and corneal nerve fibres is chal-lenging due to their uneven and irregular shape. Our first contribution is a novel ridge detector, SCIRD, which is ro-tation, scale and curvature invariant [2]. Then, we present a novel feature boosting method combining hand-crafted ap
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108
