Title: Diagnosis of liver tumour from CT images using contourlet transform

Authors: S.S. Kumar; R.S. Moni

Addresses: Department of Electronics and Instrumentation Engineering, Noorul Islam College of Engineering, Kumaracoil 629180, India. ' Department of Electronics and Instrumentation Engineering, Noorul Islam College of Engineering, Kumaracoil 629180, India

Abstract: In this paper, a novel feature extraction scheme based on multiresolution contourlet transform for the automatic diagnosis of benign and malignant liver tumours from Computed Tomography (CT) images is presented. The liver is segmented automatically from CT images using region growing and the suspected tumour region is extracted from the segmented liver using Fuzzy C Means (FCM) clustering. The textural information obtained from the segmented tumour by contourlet transform is used to detect and classify the liver tumours using feed-forward neural network classifier. The tumours diagnosed by this method are haemangioma (benign) and hepatocellular carcinoma. A comparison with a similar algorithm based on wavelet texture descriptors shows that using contourlet-based texture features significantly improves the classification rate of liver tumour from CT scans.

Keywords: CAD; contourlet transform; liver tumours; liver cancer; segmentation; feature extraction; automatic diagnosis; computed tomography; medical imaging; fuzzy C means clustering; neural networks; haemangioma; hepatocellular carcinoma.

DOI: 10.1504/IJBET.2011.043300

International Journal of Biomedical Engineering and Technology, 2011 Vol.7 No.3, pp.276 - 290

Published online: 21 Jan 2015 *

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