Cited By
View all- Kumar SMoni RRajeesh J(2011)Liver tumour classification using co-occurrence matrices on the contourlet domainMachine Graphics & Vision International Journal10.5555/2751268.275127320:2(197-214)Online publication date: 1-Feb-2011
Classification of Hepatic Tissues from CT Images Based on Texture Features and Multiclass Support Vector Machines
Authors: 
Luyao Wang, Zhi Zhang, Jingjing Liu, Bo Jiang, Xiyao Duan, Qingguo Xie, Daoyu Hu, Zhen LiAuthors Info & Claims
Pages 374 - 381
Abstract
A computer-aided diagnosis (CAD) of X-ray Computed Tomography (CT) liver images with contrast agent injection is presented. Regions of interests (ROIs) on CT liver images are defined by experienced radiologists. For each ROI, texture features based on first order statistics (FOS), spatial gray level dependence matrix (SGLDM), gray level run length matrix (GLRLM) and gray level difference matrix (GLDM) are extracted. Support vector machine (SVM) is originally for binary classification. In order to classify hepatic tissues from CT images into primary hepatic carcinoma, hemangioma and normal liver, we utilize two methods to construct multiclass SVMs: one-against-all (OAA), one-against-one (OAO) and compare their performance. The result shows that a total accuracy rate of 97.78% is obtained with the multiclass SVM using the OAO method. Our study has some practical significance for clinical diagnosis.
References
[1]
Taylor, H.M., Ros, P.R.: Hepatic Imaging: An Overview. Radiologic Clinics of North America 36, 237-245 (1998)
[2]
Mougiakakou, S.G., Valavanis, I.K., Nikita, A., Nikita, K.S.: Differential Diagnosis of CT Focal Liver Lesions Using Texture Features, Feature Selection and Ensemble Driven Classifiers. Artificial Intelligence in Medicine 41, 25-37 (2007)
[3]
Chen, E.L., Chung, P.C., Chen, C.L., Tsai, H.M., Chang, C.I.: An Automatic Diagnostic System for CT Liver Image Classification. IEEE Transactions on Biomedical Engineering 45, 783-794 (1998)
[4]
Gletsos, M., Mougiakakou, S.G., Matsopoulos, G.K., Nikita, K.S., Nikita, A.S., Kelekis, D.: 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 Biomedicine 7, 153-162 (2003)
[5]
Lambrou, T., Linney, A.D., Todd-Pokropek, A.: Wavelet Transform Analysis and Classification of the Liver from Computed Tomography Datasets. In: IEEE 5th International Special Topic Conference on Information Technology in Biomedicine (2006)
[6]
Mala, K., Sadasivam, V., Alagappan, S.: Neural Network Based Texture Analysis of Liver Tumor from Computed Tomography Images. International Journal of Biomedical Sciences 2, 33-40 (2006)
[7]
Huang, Y.L., Chen, J.H., Shen, W.C.: Diagnosis of Hepatic Tumors with Texture Analysis in Non-enhanced Computed Tomography Images. Academic Radiology 13, 713-720 (2006)
[8]
Mir, A.H., Hanmandlu, M., Tandon, S.N.: Texture Analysis of CT Images. In: IEEE International Conference of the Engineering in Medicine and Biology Society, vol. 14, pp. 781- 786 (1995)
[9]
Valavanis, I.K., Mougiakakou, S.G., Nikita, A., Nikita, K.S.: Evaluation of Texture Features in Hepatic Tissue Characterization from Non-enhanced CT Images. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3741-3744 (2007)
[10]
Rifkin, R., Mukherjee, S., Tamayo, P., Ramaswamy, S., Yeang, C.H., Angelo, M., Reich, M., Poggio, T., Lander, E.S., Golub, T.R., Mesirov, J.P.: An Analytical Method for Multiclass Molecular Cancer Classification. SIAM Reviews 45, 706-723 (2003)
[11]
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
[12]
http://www.isis.ecs.soton.ac.uk/resources/svminfo
[13]
Liu, Y., Zheng, Y.F.: One-Against-All Multi-Class SVM Classification Using Reliability Measures. In: 2005 IEEE International Joint Conference on Neural Networks, pp. 849- 854. IEEE Press, New York (2005)
[14]
Hsu, C.W., Lin, C.J.: A Comparison of Methods for Multiclass Support Vector Machines. IEEE Transactions on Neural Networks 13, 415-425 (2002)
[15]
Friedman, J.H.: Another Approach to Polychotomous Classification. Technical report, Stanford Department of Statistics, http://www-stat.stanford.edu/reports/friedman/poly.ps.Z
- Classification of Hepatic Tissues from CT Images Based on Texture Features and Multiclass Support Vector Machines
Recommendations
A vector-valued support vector machine model for multiclass problem
In this paper, a new model named Multiclass Support Vector Machines with Vector-Valued Decision (M-SVMs-VVD) or VVD is proposed. The basic idea is to separate 2^a classes by a SVM hyperplanes in the feature space induced by certain kernels, where a is a ...
Lung Nodule Classification in CT Thorax Images Using Support Vector Machines
MICAI '13: Proceedings of the 2013 12th Mexican International Conference on Artificial IntelligenceIn this paper a computational alternative to classify lung nodules using computed tomography (CT) thorax images is presented. The novelty of the method is the elimination of the segmentation stage. The contribution consist of several steps. After image ...
Multiclass Posterior Probability Support Vector Machines
Tao et. al. have recently proposed the posterior probability support vector machine (PPSVM) which uses soft labels derived from estimated posterior probabilities to be more robust to noise and outliers. Tao et. al.'s model uses a window-based density ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 26 May 2009
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 12 Jan 2025
Other Metrics
Citations
Cited By
View all- Kumar SMoni RRajeesh J(2011)Liver tumour classification using co-occurrence matrices on the contourlet domainMachine Graphics & Vision International Journal10.5555/2751268.275127320:2(197-214)Online publication date: 1-Feb-2011