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BioMed Research International
Volume 2013 (2013), Article ID 656391, 8 pages
Research Article

Statistical Fractal Models Based on GND-PCA and Its Application on Classification of Liver Diseases

1Software College, Northeastern University, Shenyang 110819, China
2Radioactive Branch, PLA General Hospital of Shenyang Military Region, Shenyang 110016, China
3Department of Information Science and Engineering, Ritsumeikan University, Shiga 5258577, Japan

Received 1 July 2013; Accepted 31 August 2013

Academic Editor: Tai-hoon Kim

Copyright © 2013 Huiyan Jiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A new method is proposed to establish the statistical fractal model for liver diseases classification. Firstly, the fractal theory is used to construct the high-order tensor, and then Generalized -dimensional Principal Component Analysis (GND-PCA) is used to establish the statistical fractal model and select the feature from the region of liver; at the same time different features have different weights, and finally, Support Vector Machine Optimized Ant Colony (ACO-SVM) algorithm is used to establish the classifier for the recognition of liver disease. In order to verify the effectiveness of the proposed method, PCA eigenface method and normal SVM method are chosen as the contrast methods. The experimental results show that the proposed method can reconstruct liver volume better and improve the classification accuracy of liver diseases.