Automatic segmentation of optic disk in retinal images. (January 2017)

• Record Type: Journal Article
• Authors: Bharkad, Sangita
• Is Part Of: Biomedical signal processing and control. Volume 31(2017)
• Page Start: 483
• Page End: 498
• Publication Date: 2017-01
• Subjects: Retinal images -- Diabetic retinopathy -- Optic disk -- Equiripple low pass FIR filter; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2016.09.009
• Languages: English
• ISSNs: 1746-8094

Abstract

Graphical abstract: Highlights: The equiripple low pass finite impulse response (FIR) filter is designed to eliminate the blood vessels. Fast and efficient threshold calculation procedure is elaborated to locate the OD in retinal image. A disk structuring element of radius half the size of the optic disk is found to be more effective for segmentation of the optic disk region. Seven metrics are used to assess the performance of proposed algorithm. Better performance is achieved on four standard databases over the methods reviewed in literature. Abstract: In this paper a new algorithm is presented for automatic segmentation of the optic disk (OD) in human retinal images. In proposed method, an equiripple low pass finite impulse response (FIR) filter is designed to suppress the dominance of blood vessels and to enhance the OD region in retinal image. Desired frequency response with optimized order of the filter is achieved by choosing proper values of filter design parameters. This method computes the appropriate value of threshold to locate the OD in retinal image. Segmentation of the OD region is carried out using grayscale morphological dilation and median filtering operation. The proposed algorithm has been tested on four standard databases (DRIVE, DIRATEDB0, DIRATEDB1 and DRIONS database). The proposed algorithm detects the OD successfully with accuracies 100%, 96.92%, 98.98%, 100% on DRIVE, DIRATEDB0, DIRATEDB1 and DRIONS databases respectively. The OD segmentation sensitivity and specificity are in the range of 74.60–87.07%, 99.39–99.61% on these four databases. The proposed algorithm has low computation complexity as it takes low computation time to segment the OD over existing methods. Also it is robust to differences in image illumination and retinal anomalies. Hence the proposed method can be used for automatic screening system of retinal related diseases like glaucoma and diabetic retinopathy.