The image segmentation algorithm of colorimetric sensor array based on fuzzy C-means clustering

• Published in: Journal of intelligent & fuzzy systems Vol. 38; no. 4; pp. 3605 - 3613
• Main Authors: Xu, Huan Chun, Hou, Rui, Liu, Lan, Cai, Jiao Yong, Chen, Ji Gang, Liu, Jia Yue
• Format: Journal Article
• Language: English
• Published: Amsterdam IOS Press BV 30.04.2020
• Subjects: Adaptive control; Algorithms; Clustering; Colorimetry; Exponential functions; Gray scale; Histograms; Image classification; Image segmentation; Markov analysis; Mathematical analysis; Pixels; Sensor arrays
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-179583

The conventional image segmentation algorithm of the colorimetric sensor array is inefficient and vulnerable to the interferences of the environment. Therefore, in order to improve the conventional algorithm, an image segmentation algorithm based on fuzzy C-means clustering (FCM) algorithm is proposed in this study. Through the information of the gray-scale distribution histogram, the proposed algorithm divides the different wave-peak regions, where the pixels are relatively concentrated, into different clusters to determine the number of clusters. In addition, the gray values of these clusters are calculated to determine the initial cluster center. Next, the calculation results are used as the input of the FCM algorithm to complete the clustering segmentation of FCM. The research results show that the algorithm proposed in this study avoids the human participations of the traditional FCM algorithm. Also, based on the original algorithm, the proposed algorithm can reduce the calculation iterations, thereby improving the computational efficiency and obtaining the number of clusters with reference significance. As the results indicate, the proposed algorithm can better describe the fuzzy information in the image, thereby avoiding the problem of classifying the pixels into one category. Besides, the exponential function is used to control the influence weight of the neighboring pixels, and the adaptive weighting of the pixel grayscale is realized to improve the calculation accuracy of pixel grayscale and realize the image segmentation.