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Liver disease is one of the most common diseases around the world, seriously affecting the health of humans. Computed tomography image based Computer Aided Diagnosis (CAD) could be crucially important in supporting liver cancer diagnosis. An effective approach to realize a CAD system for this purpose is described in this work. The CAD system employs automatic tumour segmentation, texture feature extraction and characterization into malignant and benign tumours. A Region of Inter- est (ROI) cropped from the automatically segmented tumour by confidence connected region growing and alternative fuzzy c means clustering is decomposed using multiresolution and multidirectional con- tourlet transform to obtain contourlet coefficients. Co-occurrence matrices of the contourlet coefficients are determined, and six parameters of texture characteristics, which include Angular Second Moment, Contrast, Correlation, Inverse Difference Moment, Entropy and Variance, are extracted from them. The extracted feature sets are classified into benign and malignant by a Generalized Regression Neural Net- work (GRNN) classifier. The performance of this scheme is evaluated by various performance measures and by the use a of the Receiver Operating Characteristic (ROC) curve. The results are compared with those obtained by a similar system using Wavelet Coefficients co-occurrence Matrix (WCCM) and Gray Level co-occurrence Matrix (GLCM) texture features. The results indicate that the proposed scheme based on the CCCM texture is effective for classifying malignant and begin liver tumours in abdominal CT imaging.
Czasopismo
Rocznik
Tom
Strony
197--214
Opis fizyczny
Bibliogr. 26 poz., il., wykr.
Twórcy
autor
autor
autor
- Dept. of EIE, Noorul Islam College of Engineering, Cumaracoil, India 629180, kumar_s_s@hotmail.com
Bibliografia
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- [4] Andrew P. Bradley : The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30, 1145-1159, 1997.
- [5] E-Liang Chen, Pau-CHoo Chung, Ching-Liang Chen, Hong-Ming Tsai, Chein I. Chang : An Automatic Diagnostic system for CT Liver Image Classification. IEEE Transactions Biomedical Engineering, 45, 783-794, 1998.
- [6] Miin-Shen Yang, Yu-Jen Hu, Karen Chia-Ren Lin, Charles Chia-Lee Lin : Segmentation Techniques for Tissue Differentiation in MRI of Ophthalmology using Fuzzy Clustering Algorithms. Magnetic Resonance Imaging, 20, 173-179, 2002.
- [7] Wu, K. L., Yang, M. S. : Alternative c-means clustering algorithms. Pattern Recognition, 35, 2267-2278, 2002.
- [8] Miltiades Gletsos, Stavroula G. Mougiakakou, George K. Matsopoulos, Konstantina S. Nikita, Alexandra S. Nikita, Dimitrios Kelekis : A Computer-Aided Diagnostic System to Characterize CT Focal Liver Lesions. Design and Optimization of a Neural Network Classifier. IEEE Transactions on Information Technology in Bio Medicine, 7, 153-162, 2003.
- [9] Yu-Len Huang, Jeon-Hor Chen, Wu-Chung Shen : Computer-Aided Diagnosis of Liver Tumours in Non-enhanced CT Images. Journal of Medical Physics, 9, 141-150, 2004.
- [10] Do, M. N., Vetterli, M. : The contourlet transform: An efficient directional multiresolution image representation. IEEE Trans. Image Process., 14, 2091-2106, 2005.
- [11] Fang-Hsuan Cheng, Yu-Liang Che n: Real time multiple objects tracking and identification based on discrete wavelet transform. Pattern Recognition , 39, 1126-1139, 2006.
- [12] Fawcett, T. : An introduction. to ROC analysis. Pattern Recognition Letters, 27, 861-874, 2006.
- [13] Yu-Len Huang, Jeon-Hor Chen, Wu-Chung Shen : Diagnosis of hepatic tumours with texture analysis in non enhanced computed tomography images. Academic Radiology, 13, 713-720, 2006.
- [14] Mala, K., Sadasivam, V. : Wavelet based texture analysis of Liver tumour from computed tomography images for characterization using linear vector quantization neural network. Proceedings of International Conference on Advanced Computing and Communications-ADCOM'2006, 267-270, 2007.
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- [17] Hiremath, P. S, Shivashankar, S. : Wavelet based co-occurrence histogram features for texture classificationwith an application to script identification in a document image. Pattern Recognition Letters, 29, 1182-1189, 2008.
- [18] Subbiah Bharathi, V., Ganesan, L. : Orthogonal moments based texture analysis of CT liver images. Pattern Recognition Letters, 29, 1868-1872, 2008.
- [19] Truong T. Nguyen, Yilong Liu, Herve Chauris, Soontorn Oraintara : Implementational aspects of the contourlet filter bank and application in image coding. EURASIP Journal on Advances in Signal Processing, doi: 10.1155/2008/373487, 2008.
- [20] Dongmei Guo, Tianshuang Qiu, Jie Bian, Wei Kang, Li Zhang : A computer-aided diagnostic system to discriminate SPIO-enhanced magnetic resonance hepatocellular carcinoma by a neural network classifier. Computerized Medical Imaging and Graphics, 33, 588-592, 2009.
- [21] Luyao Wang, Zhi Zhang, Jingjing Liu, Bo Jiang, Xiyao Duan, Qingguo Xie, Daoyu Hu, Zhen Li : Classification of Hepatic Tissues from CT Images Based on Texture Features and Multiclass Support Vector Machines. Proceedings of the 6th International Symposium on Neural Networks. Advances in Neural Networks. 2, 374-381, 2009.
- [22] Julien Bonnel, April Khademi, Sridhar Krishnan, Cornel Ioana : Small bowel image classification using cross-co-occurrence matrices on wavelet domain. Biomedical Signal Processing and Control, 4, 7-15, 2009.
- [23] Mahmood Amiri, Hamed Davande, Alireza Sadeghian, Sylvain Chartier : Feedback associative memory based on a new hybrid model of generalized regression and self-feedback neural networks. Neural Networks, 23, 892-904, 2010.
- [24] Ming-Huwi Horng : Performance evaluation of multiple classification of the ultrasonic supraspinatus images by using ML, RBFNN and SVM classifiers. Expert Systems with Applications, 37, 4146-4155, 2010.
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Bibliografia
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