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Hamza, Alnazier Osman
- Developing a Real Time Algorithm for Diagnosing Glaucoma
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Authors
Affiliations
1 Department of Biomedical Engineering, Sudan University of Science and Technology, SD
2 Department of Radiology, University of Medical Sciences and Technology, SD
1 Department of Biomedical Engineering, Sudan University of Science and Technology, SD
2 Department of Radiology, University of Medical Sciences and Technology, SD
Source
ICTACT Journal on Image and Video Processing, Vol 9, No 2 (2018), Pagination: 1894-1900Abstract
A Glaucoma is a group of eye diseases causing optic nerve damage and if not detected at an early stage it may cause permanent blindness. Glaucoma progression precedes some structural damage to the retina are the symptoms of Glaucoma. Manually, it is diagnosed by examination of size, structure, shape, and color of optic disc and optic cup and retinal nerve fiber layer (RNFL), which suffer from the subjectivity of human due to experience, fatigue factor etc., and with the widespread of higher quality medical imaging techniques, there are increasing demands for computer-aided diagnosis (CAD) systems for glaucoma detection, because the human mistakes, other retinal diseases like Age-related Macular Degeneration (AMD) affecting in early glaucoma detection, and the existing medical devices like Optical Coherence Tomography (OCT) and Heidelberg Retinal Tomography (HRT) are expensive. This paper proposes a novel algorithm by extract 13 shape features from disc and cup, extract 25 texture features from RNFL(retinal nerve fiber layer) using gray level co-occurrence method and Tamara algorithm and 3 color features for each of disc and cup and RNFL. Next, best features selected using two methods, first method is the student t-test and the second method applied was the Sequential Feature Selection (SFS) to introduce the best 6 features. The evaluation of proposed algorithm is performed using a RIM_ONE and DRISHTI-GS databases, the average accuracy 97%, maximize area under curve (AUC) 0.99, specificity 96.6% and sensitivity 98.4% using support vector machine classifier (SVM). Future works suggested to design a complete, automated system not just diagnose glaucoma but calculate the progress of the disease too.Keywords
Glaucoma, Fundus Image, Classification, GLCM Texture Feature, SFS.
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