1
Communication Engineer, University of Sistan and Baluchestan, Zahedan, Iran.
2
Department of Communication, University of Sistan and Baluchestan, Zahedan, Iran
Abstract
One of the issues in medical science, which has attracted the attention of many researchers, isliver segmentation from computer tomography images. Because the first step in the process of diagnosis of liver illnesses and its tumors is, having an appropriate image of the segmented liver in these images. The purpose of this paper is to provide an automated algorithm for liver segmentation in the CT images. Previous research has shown that the use of texture feature results in more favorable results in liver segmentation. The proposed algorithm of this paper is based on texture analysis to liver segmentation using the Kirsch edge detector, Mean shift, and k-means clustering. Results of the implementation of the proposed algorithm on 400 images of Milad hospital in Tehran containing liver and its lateral organs, showed the average of Dice criterion of 96%. Also, in the performance of the proposed algorithm on the sliver07 database, the average of Dice criterion is equal to 96.86%. Therefore, the proposed algorithm can be used as the first step in the process of diagnosis of liverillnesses and its tumors.
Afrooz, S., & Mohanna, F. (2022). Automatic Liver Segmentation in CT images based on Kirsch edge detector, Mean Shift, and K-means Clustering. Journal of Machine Vision and Image Processing, 9(2), 67-79.
MLA
Sobhan Afrooz; Farahnaz Mohanna. "Automatic Liver Segmentation in CT images based on Kirsch edge detector, Mean Shift, and K-means Clustering". Journal of Machine Vision and Image Processing, 9, 2, 2022, 67-79.
HARVARD
Afrooz, S., Mohanna, F. (2022). 'Automatic Liver Segmentation in CT images based on Kirsch edge detector, Mean Shift, and K-means Clustering', Journal of Machine Vision and Image Processing, 9(2), pp. 67-79.
VANCOUVER
Afrooz, S., Mohanna, F. Automatic Liver Segmentation in CT images based on Kirsch edge detector, Mean Shift, and K-means Clustering. Journal of Machine Vision and Image Processing, 2022; 9(2): 67-79.
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