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IFCM Based Segmentation Method for Liver Ultrasound Images

  • Systems-Level Quality Improvement
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Abstract

In this paper we have proposed an iterative Fuzzy C-Mean (IFCM) method which divides the pixels present in the image into a set of clusters. This set of clusters is then used to segment a focal liver lesion from a liver ultrasound image. Advantage of IFCM methods is that n-clusters FCM method may lead to non-uniform distribution of centroids, whereas in IFCM method centroids will always be uniformly distributed. Proposed method is compared with the edge based Active contour Chan-Vese (CV) method, and MAP-MRF method by implementing the methods on MATLAB. Proposed method is also compared with region based active contour region-scalable fitting energy (RSFE) method whose MATLAB code is available in author’s website. Since no comparison is available on a common database, the performance of three methods and the proposed method have been compared on liver ultrasound (US) images available with us. Proposed method gives the best accuracy of 99.8 % as compared to accuracy of 99.46 %, 95.81 % and 90.08 % given by CV, MAP-MRF and RSFE methods respectively. Computation time taken by the proposed segmentation method for segmentation is 14.25 s as compared to 44.71, 41.27 and 49.02 s taken by CV, MAP-MRF and RSFE methods respectively.

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  1. Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India

    Nishant Jain & Vinod Kumar

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  1. Nishant Jain
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  2. Vinod Kumar
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Correspondence to Nishant Jain.

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Jain, N., Kumar, V. IFCM Based Segmentation Method for Liver Ultrasound Images. J Med Syst 40, 249 (2016). https://doi.org/10.1007/s10916-016-0623-1

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