The Comparison of Clustering Algorithms K-Means and Fuzzy C-Means for Segmentation Retinal Blood Vessels

• Published in: Acta informatica medica Vol. 28; no. 1; pp. 42 - 47
• Main Authors: Wiharto, Wiharto, Suryani, Esti
• Format: Journal Article
• Language: English
• Published: Bosnia and Herzegovina Academy of Medical Sciences of Bosnia and Herzegovina 01.03.2020; Academy of Medical sciences
• Subjects: Adaptive filters; Algorithms; Blood vessels; Clustering; Equalization; Histograms; Median (statistics); Original Paper; Preprocessing; Segmentation; Statistical tests; fuzzy c-meaans; segmentation; clustering; k-mean; median; mean
• ISSN: 0353-8109; 1986-5988
• DOI: 10.5455/AIM.2020.28.42-47

The segmentation method has a number of approaches, one of which is clustering. The clustering method is widely used for segmenting retinal blood vessels, especially the k-mean algorithm and fuzzy c-means (FCM). Unfortunately, so far there have been no studies comparing the two methods for blood vessel segmentation. Many studies do not explain the reason for choosing the method. This study aims to analyze the performance of the algorithms of k-means and FCM for retinal blood vessel segmentation. This research method is divided into three stages, namely preprocessing, segmentation, and performance analysis. Preprocessing uses the green channel method, Contrast-limited adaptive histogram equalization (CLAHE) and median filter. Segmentation is divided into three processes, namely clustering, thresholding and determining the region of interest (ROI). In the thresholding process, the determination of the threshold value uses two methods, namely the mean and the median. The third stage performs performance analysis using the performance parameters of the area under the curve (AUC) and statistical tests. The statistical test results comparing FCM with k-means based on AUC values resulted in p-values <0.05 with a confidence level of 95%. Retinal vascular segmentation with the FCM method is significantly better than k-means.