Image Processing Based Automatic Diagnosis of Glaucoma using Wavelet Features of Segmented Optic Disc from Fundus Image

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F15%3APU116096" target="_blank" >RIV/00216305:26220/15:PU116096 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.sciencedirect.com/science/article/pii/S0169260715002709" target="_blank" >http://www.sciencedirect.com/science/article/pii/S0169260715002709</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cmpb.2015.10.010" target="_blank" >10.1016/j.cmpb.2015.10.010</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Image Processing Based Automatic Diagnosis of Glaucoma using Wavelet Features of Segmented Optic Disc from Fundus Image

Popis výsledku v původním jazyce

Glaucoma is a disease of the retina which is one of the most common causes of permanent blindness worldwide. This paper presents an automatic image processing based method for glaucoma diagnosis from the digital fundus image. In this paper wavelet feature extraction has been followed by optimized genetic feature selection combined with several learning algorithms and various parameter settings. Unlike the existing research works where the features are considered from the complete fundus or a sub image of the fundus, this work is based on feature extraction from the segmented and blood vessel removed optic disc to improve the accuracy of identification. The experimental results presented in this paper indicate that the wavelet features of the segmented optic disc image are clinically more significant in comparison to features of the whole or sub fundus image in the detection of glaucoma from fundus image. Accuracy of glaucoma identification achieved in this work is 94.7 % and a comparison with existing methods of glaucoma detection from fundus image indicates that the proposed approach has improved accuracy of classification

Název v anglickém jazyce

Image Processing Based Automatic Diagnosis of Glaucoma using Wavelet Features of Segmented Optic Disc from Fundus Image

Popis výsledku anglicky

Glaucoma is a disease of the retina which is one of the most common causes of permanent blindness worldwide. This paper presents an automatic image processing based method for glaucoma diagnosis from the digital fundus image. In this paper wavelet feature extraction has been followed by optimized genetic feature selection combined with several learning algorithms and various parameter settings. Unlike the existing research works where the features are considered from the complete fundus or a sub image of the fundus, this work is based on feature extraction from the segmented and blood vessel removed optic disc to improve the accuracy of identification. The experimental results presented in this paper indicate that the wavelet features of the segmented optic disc image are clinically more significant in comparison to features of the whole or sub fundus image in the detection of glaucoma from fundus image. Accuracy of glaucoma identification achieved in this work is 94.7 % and a comparison with existing methods of glaucoma detection from fundus image indicates that the proposed approach has improved accuracy of classification

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE

ISSN

0169-2607

e-ISSN

1872-7565

Svazek periodika

122

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

12

Strana od-do

1-12

Kód UT WoS článku

000368956300010

EID výsledku v databázi Scopus

2-s2.0-84954517359