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Liver fibrosis staging using CT image texture analysis and soft computing.

Authors :
Kayaaltı, Ömer
Aksebzeci, Bekir Hakan
Karahan, İbrahim Ökkeş
Deniz, Kemal
Öztürk, Mehmet
Yılmaz, Bülent
Kara, Sadık
Asyalı, Musa Hakan
Source :
Applied Soft Computing; Dec2014, Vol. 25, p399-413, 15p
Publication Year :
2014

Abstract

Liver biopsy is considered to be the gold standard for analyzing chronic hepatitis and fibrosis; however, it is an invasive and expensive approach, which is also difficult to standardize. Medical imaging techniques such as ultrasonography, computed tomography (CT), and magnetic resonance imaging are non-invasive and helpful methods to interpret liver texture, and may be good alternatives to needle biopsy. Recently, instead of visual inspection of these images, computer-aided image analysis based approaches have become more popular. In this study, a non-invasive, low-cost and relatively accurate method was developed to determine liver fibrosis stage by analyzing some texture features of liver CT images. In this approach, some suitable regions of interests were selected on CT images and a comprehensive set of texture features were obtained from these regions using different methods, such as Gray Level Co-occurrence matrix (GLCM), Laws’ method, Discrete Wavelet Transform (DWT), and Gabor filters. Afterwards, sequential floating forward selection and exhaustive search methods were used in various combinations for the selection of most discriminating features. Finally, those selected texture features were classified using two methods, namely, Support Vector Machines (SVM) and k -nearest neighbors ( k -NN). The mean classification accuracy in pairwise group comparisons was approximately 95% for both classification methods using only 5 features. Also, performance of our approach in classifying liver fibrosis stage of subjects in the test set into 7 possible stages was investigated. In this case, both SVM and k -NN methods have returned relatively low classification accuracies. Our pairwise group classification results showed that DWT, Gabor, GLCM, and Laws’ texture features were more successful than the others; as such features extracted from these methods were used in the feature fusion process. Fusing features from these better performing families further improved the classification performance. The results show that our approach can be used as a decision support system in especially pairwise fibrosis stage comparisons. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15684946
Volume :
25
Database :
Supplemental Index
Journal :
Applied Soft Computing
Publication Type :
Academic Journal
Accession number :
99124054
Full Text :
https://doi.org/10.1016/j.asoc.2014.08.065