DocumentCode :
3585938
Title :
Drusen exudate lesion discrimination in colour fundus images
Author :
Waseem, Saima ; Akram, M. Usman ; Ahmed, Bilal Ashfaq
Author_Institution :
Dept. of Comput. Eng., Coll. of E&ME, Rawalpindi, Pakistan
fYear :
2014
Firstpage :
176
Lastpage :
180
Abstract :
Automatic screening and diagnosis of ocular disease through fundus images are in place and considered worldwide. One of the leading sight loosing disease known as age related macular degeneration (AMD) has many proposed automatic screening systems. These systems detect yellow bright lesion and through the number of lesion and their size the disease is graded as advance and earlier stage. It becomes difficult for these systems to differentiate drusens from exudates another bright lesion associated with Diabetic retinopathy. These two lesions look similar on retinal surface. Differentiating these two lesions can improve the performance of any automatic system. In this paper we proposed a novel approach to discriminate these lesions. The approach consists of two stage procedure. The first stage after pre-processing detects all bright pixels from the image. The suspicious pixels are removed from the detected region. On the second stage bright regions are classified as drusen and exudates through Support Vector Machine (SVM). Proposed method was evaluated on publically available dataset STARE. The system achieve 92% accuracy.
Keywords :
diseases; image classification; image colour analysis; medical image processing; object detection; retinal recognition; support vector machines; AMD; STARE dataset; SVM; age related macular degeneration; bright pixel detection; bright region classification; colour fundus images; diabetic retinopathy; drusen exudate lesion discrimination; ocular disease diagnosis; ocular disease screening; retinal surface; sight loosing disease; support vector machine; yellow bright lesion detection; Biomedical imaging; Diseases; Feature extraction; Hemorrhaging; Image color analysis; Lesions; Retina; AMD; Drusen; Exudate; SVM;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Hybrid Intelligent Systems (HIS), 2014 14th International Conference on
Print_ISBN :
978-1-4799-7632-4
Type :
conf
DOI :
10.1109/HIS.2014.7086193
Filename :
7086193
Link To Document :
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