Context-dependent segmentation of retinal blood vessels using Hidden Markov models

Amir Pourmorteza, Seyed Hamid Reza Tofighi, Alireza Roodaki, Ashkan Yazdani, Hamid Soltanian-Zadeh

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

2 Citations (Scopus)

Abstract

Hidden Markov Models (HMMs) have proven valuable in segmentation of brain MR images. Here, a combination of HMMs-based segmentation and morphological and spatial image processing techniques is proposed for the segmentation of retinal blood vessels in optic fundus images. First the image is smoothed and the result is subtracted from the green channel image to reduce the background variations. After a simple gray-level stretching, aimed to enhance the contrast of the image, the feature vectors are extracted. The feature vector of a pixel is formed from the gray-level intensity of that pixel and those of its neighbors in a predefined neighborhood. The ability of the HHMs to build knowledge about the transitions of the elements of the feature vectors is exploited here for the classification of the vectors. The performance of the algorithm is tested on the DRIVE database and is comparable with those of the previous works.

Original languageEnglish
Title of host publicationAdvances in Computer Science and Engineering - 13th International CSI Computer Conference, CSICC 2008, Revised Selected Papers
PublisherSpringer
Pages348-355
Number of pages8
ISBN (Print)3540899847, 9783540899846
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event13th International Computer Society of Iran Computer Conference on Advances in Computer Science and Engineering, CSICC 2008 - Kish Island, Iran
Duration: 9 Mar 200811 Mar 2008

Publication series

NameCommunications in Computer and Information Science
PublisherSpringer
Volume6
ISSN (Print)1865-0929

Conference

Conference13th International Computer Society of Iran Computer Conference on Advances in Computer Science and Engineering, CSICC 2008
CountryIran
CityKish Island
Period9/03/0811/03/08

Keywords

  • context-dependent image segmentation
  • Hidden Markov models (HMM)
  • retinal images

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