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

  • Paladugu RajuEmail author
  • Veera Malleswara Rao
  • Bhima Prabhakara Rao


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.


Feature based Fuzzy C-means (FbFCM) Whale Optimization algorithm (WOA) Gray Level Co-occurrence Matrix (GLCM) Ultrasound (US) kidney tumor and cyst segmentation 



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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Paladugu Raju
    • 1
    Email author
  • Veera Malleswara Rao
    • 2
  • Bhima Prabhakara Rao
    • 3
  1. 1.Department of ECEJNTUK KakinadaKakinadaIndia
  2. 2.Department of ECE, GITGITAM Deemed to be UniversityVisakhapatnamIndia
  3. 3.Programme Director, NanotechnologyJNTUK KakinadaKakinadaIndia

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