An Automated Computer-aided Diagnosis System for Abdominal CT Liver Images | Zendy

Gehad Ismail Sayed | Zendy; Aboul Ella Hassanien | Zendy; Gerald Schaefer | Zendy

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An Automated Computer-aided Diagnosis System for Abdominal CT Liver Images

Author(s) -

Gehad Ismail Sayed,

Aboul Ella Hassanien,

Gerald Schaefer

Publication year - 2016

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2016.07.012

Subject(s) - computer science , cad , computer aided diagnosis , cluster analysis , artificial intelligence , segmentation , pattern recognition (psychology) , feature extraction , image segmentation , computer aided , support vector machine , fuzzy logic , fuzzy clustering , engineering , programming language , engineering drawing

In this paper, we present a computer-aided diagnosis (CAD) system for abdominal Computed Tomography liver images that comprises four main phases: liver segmentation, lesion candidate segmentation, feature extraction from each candidate lesion, and liver disease classification. A hybrid approach based on fuzzy clustering and grey wolf optimisation is employed for automatic liver segmentation. Fast fuzzy c-means clustering is used for lesion candidates extraction, and a variety of features are extracted from each candidate. Finally, these features are used in a classification stage using a support vector machine. Experimental results confirm the efficacy of the proposed CAD system, which is shown to yield an overall accuracy of almost 96% in terms of healthy liver extraction and 97% for liver disease classification

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