Glaucoma Detection Using Clustering and Segmentation of the Optic Disc Region from Retinal Fundus Images

被引：0

作者：

Guru Prasad M.S. ^{[1
]}

Naveen Kumar H.N. ^{[2
]}

Raju K. ^{[3
]}

Santhosh Kumar D.K. ^{[4
]}

Chandrappa S. ^{[5
]}

机构：

[1] Department of CSE, Graphic Era (Deemed to be University), Dehradun

[2] Department of ECE, Vidyavardhaka College of Engineering, Mysore

[3] Department of CSE, NMAM Institute of Technology, Nitte

[4] Department of CSE, Canara Engineering College, Mangalore

[5] Department of ISE, GSSS Institute of Engineering and Technology for Women, Mysore

来源：

SN Computer Science | / 4卷 / 2期

关键词：

Fundus images; Gamma transformation; Glaucoma; Optic disc; Segmentation; Thresholding;

D O I：

10.1007/s42979-022-01592-1

中图分类号：

学科分类号：

摘要：

Segmentation is a process of dividing image into multiple parts. Each part is called a segment. The main objective of the segmenting image is to convert the representation of an image into another format that is useful for the analysis of image features and properties. Retinal image segmentation is an essential stage for retinal disease analysis and identification. Retinal image segmentation helps the ophthalmologists in the detection of glaucoma eye disease. Glaucoma eye disease is one of the important causes of permanent vision loss. Early detection of glaucoma is most important to prevent further progression of vision loss. The vertical cup-to-disc ratio is the important clinical parameter used for glaucoma disease detection. Therefore, accurate segmentation of optic disc from retinal images is of great significance. This work presents three categories of segmentation algorithms for the segmentation of the optic disc region from retinal fundus images. Thresholding-based methods, clustering-based technique, and region-based technique are used for optic disc segmentation. The proposed methods were evaluated using DRIONS-DB dataset containing 110 images and HRF dataset containing 45 images. The performance metric boundary localization error is calculated by comparing each proposed method with the ground truth values. Results from the proposed methods show that methods are less complex and efficiently work on all images. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

引用

共 22 条

[1]

Panwar P., Gopal G., Image segmentation using K-means clustering and thresholding, (2016)

[2]

Kumbhar P.G., Holambe S.N., A review of image thresholding techniques, Int J Adv Res Comput Sci Softw Eng, 5, 6, pp. 160-163, (2015)

[3]

Nameirakpam D., Singh K., Chanu Y., Image segmentation using K-means clustering algorithm and subtractive clustering algorithm, Proc Comput Sci, 54, pp. 764-771, (2015)

[4]

Chitade A.Z., Katiyar S.K., Colour based image segmentation using k-means clustering, International Journal of Engineering Science and Technology, 2, 10, pp. 5319-5325, (2010)

[5]

Jose A., Ravi S., Sambath M., Brain tumor segmentation using K -means clustering and Fuzzy C -means algorithm and its area calculation, Int J Innov Res Comput Commu Eng, 2, 2, pp. 3496-3501, (2014)

[6]

Zhang X., Xiao P., Feng X., Wang J., Wang Z., Hybrid region merging method for segmentation of high-resolution remote sensing images, ISPRS J Photogramm Remote Sens, 98, pp. 19-28, (2014)

[7]

Gao Y., Wang J., Zhang L., Robust ROI localization based on image segmentation and outlier detection in finger vein recognition, Multimed Tools Appl, (2020)

[8]

Sevastopolsky A., Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network, Pattern Recognit Image Anal, 27, pp. 618-624, (2017)

[9]

Jayanthi P., Shivaa R., Sownthear D., Soundar T., Optic disc and cup segmentation methods for glaucoma detection using retinal fundus images, Int J Adv Sci Technol., 29, 9s, pp. 15-18, (2020)

[10]

Almazroa A., Sun W., Alodhayb S., Raahemifar K., Lakshminarayanan V., Optic disc segmentation for glaucoma screening system using fundus images, Clin Ophthalmol, 11, pp. 2017-2029, (2017)

← 1 2 3 →