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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Bibliographic Details
Published in:Journal of medical systems Vol. 43; no. 11; p. 322
Main Author: Cai, Jianhong
Format: Journal Article
Language:English
Published: New York Springer US 01.11.2019
Springer Nature B.V
Subjects:
Algorithms
Clustering
Computed tomography
Decision support systems
Diagnosis
Diagnosis, Computer-Assisted
Distributed Analytics and Deep Learning in Health Care
Euclidean geometry
Euclidean space
Fuzzy Logic
Health Informatics
Health Sciences
Histograms
Humans
Image & Signal Processing
Image analysis
Image filters
Image processing
Image Processing, Computer-Assisted - methods
Image segmentation
Iterative methods
Kernel functions
Lagrange multiplier
Liver
Liver cancer
Liver Neoplasms - diagnostic imaging
Liver Neoplasms - pathology
Manufacturing cells
Medical diagnosis
Medical imaging
Medicine
Medicine & Public Health
Pixels
Robustness (mathematics)
Statistics for Life Sciences
Tomography, X-Ray Computed - methods
Tumors
Lagrange multiplier
Liver carcinoma
Fuzzy clustering
Non-uniform background
Linear weighted filtering
European space
Scale-kernel space
ISSN:0148-5598, 1573-689X, 1573-689X
Online Access:Get full text
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Summary: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 is mapped 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.
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ISSN:0148-5598
1573-689X
1573-689X
DOI:10.1007/s10916-019-1459-2