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

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.

[1]  Jacob D. Furst,et al.  Wavelet-based texture classification of tissues in computed tomography , 2005, 18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05).

[2]  Maryellen L. Giger,et al.  A Fuzzy C-Means (FCM)-Based Approach for Computerized Segmentation of Breast Lesions in Dynamic Contrast-Enhanced MR Images1 , 2006 .

[3]  Miodrag Popović,et al.  Characterization of visually similar diffuse diseases from B-scan liver images using nonseparable wavelet transform , 1998, IEEE Transactions on Medical Imaging.

[4]  Jacek M. Zurada,et al.  Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images , 1996, IEEE Trans. Medical Imaging.

[5]  M. Giger,et al.  A fuzzy c-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images. , 2006, Academic radiology.

[6]  J. Kennedy.  Imaging the liver. , 1995, Nursing standard (Royal College of Nursing (Great Britain) : 1987).

[7]  Dushyant V. Sahani,et al.  Imaging the liver. , 2004, The oncologist.

[8]  K. Blekas,et al.  Fuzzy neural network-based texture analysis of ultrasonic images , 2000, IEEE Engineering in Medicine and Biology Magazine.

[9]  S Swarnamani,et al.  Application of artificial neural networks for the classification of liver lesions by image texture parameters. , 1996, Ultrasound in medicine & biology.

[10]  G. Ravindran,et al.  Optimal Feature Selection and Automatic Classification of Abnormal Masses in Ultrasound Liver Images , 2007, 2007 International Conference on Signal Processing, Communications and Networking.

[11]  Laurent Demanet,et al.  Fast Discrete Curvelet Transforms , 2006, Multiscale Model. Simul..

[12]  E-Liang Chen,et al.  An automatic diagnostic system for CT liver image classification , 1998, IEEE Transactions on Biomedical Engineering.

[13]  David L. Donoho,et al.  Digital curvelet transform: strategy, implementation, and experiments , 2000, SPIE Defense + Commercial Sensing.

[14]  Konstantina S. Nikita,et al.  A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier , 2003, IEEE Transactions on Information Technology in Biomedicine.

[15]  Chien-Cheng Lee,et al.  Gabor Wavelets and SVM Classifier for Liver Diseases Classiflcation from CT Images , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[16]  Xinhua Zhuang,et al.  Image Analysis Using Mathematical Morphology , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Lexing Ying,et al.  3D discrete curvelet transform , 2005, SPIE Optics + Photonics.