Literature DB >> 18232383

Vessel extraction under non-uniform illumination: a level set approach.

K W Sum1, Paul Y S Cheung.   

Abstract

Vessel extraction is one of the critical tasks in clinical practice. This communication presents a new approach for vessel extraction using a level-set-based active contour by defining a novel local term that takes local image contrast into account. The proposed model not only preserves the performance of the existing models on blurry images, but also overcomes their inability to handle nonuniform illumination. The efficacy of the approach is demonstrated with experiments involving both synthetic images and clinical angiograms.

Mesh:

Year:  2008        PMID: 18232383     DOI: 10.1109/TBME.2007.896587

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  11 in total

1.  Application of morphological bit planes in retinal blood vessel extraction.

Authors:  M M Fraz; A Basit; S A Barman
Journal:  J Digit Imaging       Date:  2013-04       Impact factor: 4.056

2.  Localizing region-based active contours.

Authors:  Shawn Lankton; Allen Tannenbaum
Journal:  IEEE Trans Image Process       Date:  2008-11       Impact factor: 10.856

Review 3.  Delineation of blood vessels in pediatric retinal images using decision trees-based ensemble classification.

Authors:  Muhammad Moazam Fraz; Alicja R Rudnicka; Christopher G Owen; Sarah A Barman
Journal:  Int J Comput Assist Radiol Surg       Date:  2013-12-24       Impact factor: 2.924

4.  Bayesian method with spatial constraint for retinal vessel segmentation.

Authors:  Zhiyong Xiao; Mouloud Adel; Salah Bourennane
Journal:  Comput Math Methods Med       Date:  2013-07-14       Impact factor: 2.238

5.  Accurate image analysis of the retina using hessian matrix and binarisation of thresholded entropy with application of texture mapping.

Authors:  Xiaoxia Yin; Brian W-H Ng; Jing He; Yanchun Zhang; Derek Abbott
Journal:  PLoS One       Date:  2014-04-29       Impact factor: 3.240

6.  Automatic segmentation of myocardium from black-blood MR images using entropy and local neighborhood information.

Authors:  Qian Zheng; Zhentai Lu; Minghui Zhang; Lin Xu; Huan Ma; Shengli Song; Qianjin Feng; Yanqiu Feng; Wufan Chen; Taigang He
Journal:  PLoS One       Date:  2015-03-26       Impact factor: 3.240

7.  Robust Retinal Blood Vessel Segmentation Based on Reinforcement Local Descriptions.

Authors:  Meng Li; Zhenshen Ma; Chao Liu; Guang Zhang; Zhe Han
Journal:  Biomed Res Int       Date:  2017-01-18       Impact factor: 3.411

8.  A Combined Random Forests and Active Contour Model Approach for Fully Automatic Segmentation of the Left Atrium in Volumetric MRI.

Authors:  Chao Ma; Gongning Luo; Kuanquan Wang
Journal:  Biomed Res Int       Date:  2017-02-19       Impact factor: 3.411

9.  A framework for retinal vasculature segmentation based on matched filters.

Authors:  Xianjing Meng; Yilong Yin; Gongping Yang; Zhe Han; Xiaowei Yan
Journal:  Biomed Eng Online       Date:  2015-10-24       Impact factor: 2.819

10.  A robust statistics driven volume-scalable active contour for segmenting anatomical structures in volumetric medical images with complex conditions.

Authors:  Kuanquan Wang; Chao Ma
Journal:  Biomed Eng Online       Date:  2016-04-14       Impact factor: 2.819

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