International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 16, No. 6, 8 Dec. 2024
Cover page and Table of Contents: PDF (size: 629KB)
WOA Enabled Fuzzy-C-Means Segmentation for Accurate Detection of Polycystic Kidney Disease
PDF (629KB), PP.87-95
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Author(s)
S. Helora Padmini
1,*
C. Sujatha
1
Index Terms
Kidney Segmentation, Feature Based Fuzzy-C-Means, Feature Extraction
Abstract
Polycystic Kidney disease (PKD) is often caused due to inherited condition and it forms many cysts around the kidney, and it is damaged when it grow. Accurate segmentation of PKD is very crucial for a persistent MRI diagnostics. Because many people have no symptoms, they can lead to complications until the surgery is done to remove the cyst. Methods: For accurate detection PKD, the heap of MRI images have been considered, In this work, A novel method includes feature based Fuzzy C means (FFCM) with whale optimization algorithm (WOA) for accurate segmentation of kidney cyst. WOA is used to optimally attach the cluster centroids of FCM. In the conventional methods like mountain models and fuzzy C-shells models are used to identify the regions of interest (ROI). Result: The outcomes of FFCM and WOA based process are compared with the results from existing methods using IB-FCM and Fuzzy K-means and FCM model. Conclusion: However, an exact boundary of the region is obtained and computed an experimental dispersal of the image by Feature extraction based Fuzzy C-Means Clustering segmentation. A detection process is based on the FFCM and WOA segmentation is accomplished to discriminate the normal cyst and the kidney disease. The experimental evaluation is accomplished through the use of Ischemic kidney Disease (IKD) database.
Cite This Paper
S. Helora Padmini, C. Sujatha, "WOA Enabled Fuzzy-C-Means Segmentation for Accurate Detection of Polycystic Kidney Disease", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.16, No.6, pp. 87-95, 2024. DOI:10.5815/ijigsp.2024.06.07
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