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Reseach Article

Diagnosis of Liver Tumor from CT Images Using Fast Discrete Curvelet Transform

Published on None 2010 by Dr R.S.Moni, S.S.Kumar
Computer Aided Soft Computing Techniques for Imaging and Biomedical Applications
Foundation of Computer Science USA
CASCT - Number 1
None 2010
Authors: Dr R.S.Moni, S.S.Kumar
e30902ef-3e7b-48b3-9857-702d2b9184ac

Dr R.S.Moni, S.S.Kumar . Diagnosis of Liver Tumor from CT Images Using Fast Discrete Curvelet Transform. Computer Aided Soft Computing Techniques for Imaging and Biomedical Applications. CASCT, 1 (None 2010), 1-6.

@article{
author = { Dr R.S.Moni, S.S.Kumar },
title = { Diagnosis of Liver Tumor from CT Images Using Fast Discrete Curvelet Transform },
journal = { Computer Aided Soft Computing Techniques for Imaging and Biomedical Applications },
issue_date = { None 2010 },
volume = { CASCT },
number = { 1 },
month = { None },
year = { 2010 },
issn = 0975-8887,
pages = { 1-6 },
numpages = 6,
url = { /specialissues/casct/number1/999-34/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 Computer Aided Soft Computing Techniques for Imaging and Biomedical Applications
%A Dr R.S.Moni
%A S.S.Kumar
%T Diagnosis of Liver Tumor from CT Images Using Fast Discrete Curvelet Transform
%J Computer Aided Soft Computing Techniques for Imaging and Biomedical Applications
%@ 0975-8887
%V CASCT
%N 1
%P 1-6
%D 2010
%I International Journal of Computer Applications
Abstract

In this paper, a novel feature extraction scheme is proposed, based on multiresolution fast discrete curvelet transform for computer-aided diagnosis of liver diseases. The liver is segmented from CT images using adaptive threshold detection and morphological processing. The suspected tumour region is extracted from the segmented liver using FCM clustering. The textural information obtained from the extracted tumour using Fast Discrete Curvelet Transform (FDCT) is used to train and classify the liver tumour into hemangioma and hepatoma employing artificial neural network classifier. A comparison with a similar algorithm based on Wavelet texture descriptors shows that using FDCT based texture features significantly improves the classification rate of liver tumours from CT scans.

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Index Terms

Computer Science
Information Sciences

Keywords

Liver Tumour FCM technique Texture analysis Fast Discrete Curvelet Transform