![](/images/graphics-bg.png)
Cirrhosis Classification Based on Texture Classification of Random Features
Joint Authors
Liu, Hui
Zhao, Zuowei
Zheng, Yuanjie
Qiu, Tianshuang
Shao, Ying
Guo, Dongmei
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-02-24
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Accurate staging of hepatic cirrhosis is important in investigating the cause and slowing down the effects of cirrhosis.
Computer-aided diagnosis (CAD) can provide doctors with an alternative second opinion and assist them to make a specific treatment with accurate cirrhosis stage.
MRI has many advantages, including high resolution for soft tissue, no radiation, and multiparameters imaging modalities.
So in this paper, multisequences MRIs, including T1-weighted, T2-weighted, arterial, portal venous, and equilibrium phase, are applied.
However, CAD does not meet the clinical needs of cirrhosis and few researchers are concerned with it at present.
Cirrhosis is characterized by the presence of widespread fibrosis and regenerative nodules in the hepatic, leading to different texture patterns of different stages.
So, extracting texture feature is the primary task.
Compared with typical gray level cooccurrence matrix (GLCM) features, texture classification from random features provides an effective way, and we adopt it and propose CCTCRF for triple classification (normal, early, and middle and advanced stage).
CCTCRF does not need strong assumptions except the sparse character of image, contains sufficient texture information, includes concise and effective process, and makes case decision with high accuracy.
Experimental results also illustrate the satisfying performance and they are also compared with typical NN with GLCM.
American Psychological Association (APA)
Liu, Hui& Shao, Ying& Guo, Dongmei& Zheng, Yuanjie& Zhao, Zuowei& Qiu, Tianshuang. 2014. Cirrhosis Classification Based on Texture Classification of Random Features. Computational and Mathematical Methods in Medicine،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-479509
Modern Language Association (MLA)
Liu, Hui…[et al.]. Cirrhosis Classification Based on Texture Classification of Random Features. Computational and Mathematical Methods in Medicine No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-479509
American Medical Association (AMA)
Liu, Hui& Shao, Ying& Guo, Dongmei& Zheng, Yuanjie& Zhao, Zuowei& Qiu, Tianshuang. Cirrhosis Classification Based on Texture Classification of Random Features. Computational and Mathematical Methods in Medicine. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-479509
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-479509