Open Access

ARTICLE

Automatic Optic Disc Detection in Retinal Images Using FKMT‒MOPDF

Kittipol Wisaeng^*

Technology and Business Information System Unit, Mahasarakham Business School, Mahasarakham University, Mahasarakham, 44150, Thailand

* Corresponding Author: Kittipol Wisaeng. Email:

Computer Systems Science and Engineering 2023, 45(3), 2569-2586. https://doi.org/10.32604/csse.2023.033226

Received 11 June 2022; Accepted 12 July 2022; Issue published 21 December 2022

Abstract

In recent days, detecting Optic Disc (OD) in retinal images has been challenging and very important to the early diagnosis of eye diseases. The process of detecting the OD is challenging due to the diversity of color, intensity, brightness and shape of the OD. Moreover, the color similarities of the neighboring organs of the OD create difficulties during OD detection. In the proposed Fuzzy K‒Means Threshold (FKMT) and Morphological Operation with Pixel Density Feature (MOPDF), the input retinal images are coarsely segmented by fuzzy K‒means clustering and thresholding, in which the OD is classified from its neighboring organs with intensity similarities. Then, the segmented images are given as the input to morphological operation with pixel density feature calculations, which reduce the false detection in the small pixel of the OD. Finally, the OD area is detected by applying the Sobel edge detection method, which accurately detects the OD from the retinal images. After detection optimization, the proposed method achieved Sensitivity (SEN), Specificity (SPEC) and Accuracy (ACC), with 96.74%, 96.78% and 96.92% in DiaretDB0 (Standard Diabetic Retinopathy Database Calibration level 0), 97.12%, 97.10% and 97.75% in DiaretDB1 (Standard Diabetic Retinopathy Database Calibration level 1) and 97.19%, 97.47% and 97.43% in STARE (Structured Analysis of the Retina) dataset respectively. The experimental results demonstrated the proposed method’s efficiency for segmenting and detecting OD areas.

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Keywords

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