Texture based on geostatistic for glaucoma diagnosis from fundus eye image

Texture based on geostatistic for glaucoma diagnosis from fundus eye image Glaucoma is an ocular disorder that can permanently damage patient vision. Initially, it reduces the visual field, and may cause blindness. Effective methods for early detection is crucial for avoiding significant damages of the patient vision. The use of CAD (Computer-Aided Detection) and CADx (Computer-Aided Diagnosis) systems has contributed to increase the chances of detection and precise diagnoses, assisting experts’ decision making on treatment regarding glaucoma. This paper proposes a method that analyzes the texture of the optical disk image region to diagnose glaucoma. Such analysis is done using the Local Binary Pattern (LBP) to represent the optic disk region, and geostatistical functions to describe texture patterns. The obtained texture features are used for classification based on Support Vector Machine. The proposed method presented as best results a sensitivity of 95%, accuracy of 91% and specificity of 88% in the diagnosis of glaucoma. The method has proved to be promising in assisting glaucoma diagnosis. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Texture based on geostatistic for glaucoma diagnosis from fundus eye image

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-017-4608-y
Publisher site
See Article on Publisher Site

Abstract

Glaucoma is an ocular disorder that can permanently damage patient vision. Initially, it reduces the visual field, and may cause blindness. Effective methods for early detection is crucial for avoiding significant damages of the patient vision. The use of CAD (Computer-Aided Detection) and CADx (Computer-Aided Diagnosis) systems has contributed to increase the chances of detection and precise diagnoses, assisting experts’ decision making on treatment regarding glaucoma. This paper proposes a method that analyzes the texture of the optical disk image region to diagnose glaucoma. Such analysis is done using the Local Binary Pattern (LBP) to represent the optic disk region, and geostatistical functions to describe texture patterns. The obtained texture features are used for classification based on Support Vector Machine. The proposed method presented as best results a sensitivity of 95%, accuracy of 91% and specificity of 88% in the diagnosis of glaucoma. The method has proved to be promising in assisting glaucoma diagnosis.

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Mar 27, 2017

References

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