An Optimized Approach for Prostate Image Segmentation Using K-Means Clustering Algorithm with Elbow Method

• Published in: Computational intelligence and neuroscience Vol. 2021; no. 1; p. 4553832
• Main Authors: Sammouda, Rachid, El-Zaart, Ali
• Format: Journal Article
• Language: English
• Published: New York Hindawi 2021; John Wiley & Sons, Inc
• Subjects: Algorithms; Antigens; Cancer; Care and treatment; Cluster analysis; Clustering; Datasets; Diagnosis; Disease; Elbow; Image acquisition; Image processing; Image segmentation; Infrared analysis; Infrared imagery; Localization; Medical imaging; Methods; Neural networks; Pathology; Photochemotherapy; Photodynamic therapy; Pixels; Prostate cancer; Research methodology; Statistical analysis; Tumors; Vector quantization; Saudi Arabia
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2021/4553832

Prostate cancer disease is one of the common types that cause men’s prostate damage all over the world. Prostate-specific membrane antigen (PSMA) expressed by type-II is an extremely attractive style for imaging-based diagnosis of prostate cancer. Clinically, photodynamic therapy (PDT) is used as noninvasive therapy in treatment of several cancers and some other diseases. This paper aims to segment or cluster and analyze pixels of histological and near-infrared (NIR) prostate cancer images acquired by PSMA-targeting PDT low weight molecular agents. Such agents can provide image guidance to resection of the prostate tumors and permit for the subsequent PDT in order to remove remaining or noneradicable cancer cells. The color prostate image segmentation is accomplished using an optimized image segmentation approach. The optimized approach combines the k-means clustering algorithm with elbow method that can give better clustering of pixels through automatically determining the best number of clusters. Clusters’ statistics and ratio results of pixels in the segmented images show the applicability of the proposed approach for giving the optimum number of clusters for prostate cancer analysis and diagnosis.