Optimal GLCM combined FCM segmentation algorithm for detection of kidney cysts and tumor

• Published in: Multimedia tools and applications Vol. 78; no. 13; pp. 18419 - 18441
• Main Authors: Raju, Paladugu, Rao, Veera Malleswara, Rao, Bhima Prabhakara
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2019; Springer Nature B.V
• Subjects: Algorithms; Centroids; Computer Communication Networks; Computer Science; Cysts; Data Structures and Information Theory; Feature extraction; Image segmentation; Kidneys; Multimedia Information Systems; Optimization; Special Purpose and Application-Based Systems; Tumors; Whale Optimization algorithm (WOA); Ultrasound (US) kidney tumor and cyst segmentation; Gray Level Co-occurrence Matrix (GLCM); Feature based Fuzzy C-means (FbFCM)
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-018-7145-4

In this document, we employed an efficient Optimal GLCM attribute related FCM segmentation algorithm which is used to categorize the kidney cysts and tumor from the ultrasound kidney images. The FCM is exploiting some appropriate attributes of GLCM texture feature extractor and optimally attach the cluster centroids of FCM by the help of Whale optimization algorithm. The proposed approach is executed in the working platform of Matlab. The findings demonstrate that the proposed model have better performance in recognizing the detection of kidney cysts and tumor in patients by examining US kidney images. Also, we have shown the comparison of our proposed method FB-FCM-WOA with the existing methodologies like FB-FCM, FB-K-means, IB-FCM and IB-K-means. Hence, we would suggest that our proposed method is much better for detecting kidney cysts and tumor.