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Segmentation and Diagnosis of Liver Carcinoma Based on Adaptive Scale-Kernel Fuzzy Clustering Model for CT Images.

Medical image analysis plays an important role in computer-aided liver-carcinoma diagnosis. Aiming at the existing image fuzzy clustering segmentation being not suitable to segment CT image with non-uniform background, a fast robust kernel space fuzzy clustering segmentation algorithm is proposed. F...

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Publicado en:Journal of Medical Systems Vol. 43; no. 11; pp. 1 - 12
Autor principal: Jianhong Cai
Formato: Journal Article
Publicado: Springer Nature Nov2019
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Nov2019
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10916-019-1459-2
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        atl: Segmentation and Diagnosis of Liver Carcinoma Based on Adaptive Scale-Kernel Fuzzy Clustering Model for CT Images.
      aug:
        au: Jianhong Cai
        affil: School of Computer and Information Engineering, Hubei University, Wuhan 430064, Hubei, China.
      sug:
      ab: Medical image analysis plays an important role in computer-aided liver-carcinoma diagnosis. Aiming at the existing image fuzzy clustering segmentation being not suitable to segment CT image with non-uniform background, a fast robust kernel space fuzzy clustering segmentation algorithm is proposed. Firstly, the sample in euclidean space ismapped into the high dimensional feature space through the kernel function. Then the linear weighted filtering image is obtained by combining the current pixel with its neighborhood pixels through the space information in CT image. Finally, the two-dimensional histogram between the clustered pixel and its neighborhood mean is introduced into the robust kernel space image fuzzy clustering, and the iterative expression of the fast robust fuzzy clustering in kernel space is obtained by using Lagrange multiplier method. The experimental results on four databases show that our proposed method can segment liver tumors from abdominal CT volumes effectively and automatically, and the comprehensive segmentation performance of the proposed method is superior to that of several existing methods.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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