Automatic detection and segmentation of optic disc and fovea in retinal images. Issue 11 (6th September 2018)

• Record Type: Journal Article
• Authors: Chalakkal, Renoh Johnson; Abdulla, Waleed Habib; Thulaseedharan, Sinumol Sukumaran
• Is Part Of: IET image processing. Volume 12:Issue 11(2018)
• Page Start: 2100
• Page End: 2110
• Publication Date: 2018-09-06
• Subjects: image segmentation -- object detection -- eye -- medical image processing -- biomedical optical imaging -- unsupervised learning -- image enhancement -- feature extraction -- graph theory -- image matching; automatic optic disc detection -- automatic optic disc segmentation -- automatic fovea detection -- automatic fovea segmentation -- digital retinal fundus images -- feature extraction -- automatic feature detection -- unsupervised algorithm -- histogram based template matching -- circular Hough transform -- horizontal strips -- image processing steps -- morphological operations -- connected component analysis -- macular region -- public databases -- University of Auckland Diabetic Retinopathy database -- OD boundary segmentation -- retinal image databases; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5666
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Feature extraction from retinal images is gaining popularity worldwide as many pathologies are proved having connections with these features. Automatic detection of these features makes it easier for the specialist ophthalmologists to analyse them without spending exhaustive time to segment them manually. The proposed method automatically detects the optic disc (OD) using histogram‐based template matching combined with the maximum sum of vessel information in the retinal image. The OD region is segmented by using the circular Hough transform. For detecting fovea, the retinal image is uniformly divided into three horizontal strips and the strip including the detected OD is selected. Contrast of the horizontal strip containing the OD region is then enhanced using a series of image processing steps. The macula region is first detected in the OD strip using various morphological operations and connected component analysis. The fovea is located inside this detected macular region. The proposed method achieves an OD detection accuracy over 95% upon testing on seven public databases and on our locally developed database, University of Auckland Diabetic Retinopathy database (UoA‐DR). The average OD boundary segmentation overlap score, sensitivity and fovea detection accuracy achieved are 0.86, 0.968 and 97.26% respectively.