Literature DB >> 19419790

Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm.

Muhammed Gökhan Cinsdikici1, Doğan Aydin.   

Abstract

Blood vessels in ophthalmoscope images play an important role in diagnosis of some serious pathologies on retinal images. Hence, accurate extraction of vessels is becoming a main topic of this research area. Matched filter (MF) implementation for blood vessel detection is one of the methods giving more accurate results. Using this filter alone might not recover all the vessels (especially the capillaries). In this paper, a novel approach (MF/ant algorithm) is proposed to overcome the deficiency of the MF. The proposed method is a hybrid model of matched filter and ant colony algorithm. In this work, the accuracy and parameters of the hybrid algorithm are also discussed. The proposed method shows its success using the well known reference ophthalmoscope images of DRIVE database.

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Year:  2009        PMID: 19419790     DOI: 10.1016/j.cmpb.2009.04.005

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  12 in total

1.  Blood vessel extraction of diabetic retinopathy using optimized enhanced images and matched filter.

Authors:  Asit Subudhi; Subhra Pattnaik; Sukanta Sabut
Journal:  J Med Imaging (Bellingham)       Date:  2016-11-30

2.  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

3.  An Unsupervised Approach for Extraction of Blood Vessels from Fundus Images.

Authors:  Jyotiprava Dash; Nilamani Bhoi
Journal:  J Digit Imaging       Date:  2018-12       Impact factor: 4.056

4.  Automatic extraction of blood vessels in the retinal vascular tree using multiscale medialness.

Authors:  Mariem Ben Abdallah; Jihene Malek; Ahmad Taher Azar; Philippe Montesinos; Hafedh Belmabrouk; Julio Esclarín Monreal; Karl Krissian
Journal:  Int J Biomed Imaging       Date:  2015-04-22

Review 5.  Retinal blood vessels extraction using probabilistic modelling.

Authors:  Djibril Kaba; Chuang Wang; Yongmin Li; Ana Salazar-Gonzalez; Xiaohui Liu; Ahmed Serag
Journal:  Health Inf Sci Syst       Date:  2014-01-27

6.  A Novel Multiscale Gaussian-Matched Filter Using Neural Networks for the Segmentation of X-Ray Coronary Angiograms.

Authors:  Ivan Cruz-Aceves; Fernando Cervantes-Sanchez; Maria Susana Avila-Garcia
Journal:  J Healthc Eng       Date:  2018-04-18       Impact factor: 2.682

7.  Optimizing the trainable B-COSFIRE filter for retinal blood vessel segmentation.

Authors:  Sufian A Badawi; Muhammad Moazam Fraz
Journal:  PeerJ       Date:  2018-11-13       Impact factor: 2.984

8.  A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation.

Authors:  Ahsan Khawaja; Tariq M Khan; Mohammad A U Khan; Syed Junaid Nawaz
Journal:  Sensors (Basel)       Date:  2019-11-13       Impact factor: 3.576

9.  An Automated Approach for Localizing Retinal Blood Vessels in Confocal Scanning Laser Ophthalmoscopy Fundus Images.

Authors:  Robert Kromer; Rahman Shafin; Sebastian Boelefahr; Maren Klemm
Journal:  J Med Biol Eng       Date:  2016-08-25       Impact factor: 1.553

10.  Principled network extraction from images.

Authors:  Diego Baptista; Caterina De Bacco
Journal:  R Soc Open Sci       Date:  2021-07-28       Impact factor: 2.963

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