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A Novel Hepatocellular Carcinoma Image Classification Method Based on Voting Ranking Random Forests
Joint Authors
Yi, Dehui
Xia, Bingbing
Liu, Huiling
Jiang, Huiyan
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-05-17
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
This paper proposed a novel voting ranking random forests (VRRF) method for solving hepatocellular carcinoma (HCC) image classification problem.
Firstly, in preprocessing stage, this paper used bilateral filtering for hematoxylin-eosin (HE) pathological images.
Next, this paper segmented the bilateral filtering processed image and got three different kinds of images, which include single binary cell image, single minimum exterior rectangle cell image, and single cell image with a size of n ⁎ n .
After that, this paper defined atypia features which include auxiliary circularity, amendment circularity, and cell symmetry.
Besides, this paper extracted some shape features, fractal dimension features, and several gray features like Local Binary Patterns (LBP) feature, Gray Level Cooccurrence Matrix (GLCM) feature, and Tamura features.
Finally, this paper proposed a HCC image classification model based on random forests and further optimized the model by voting ranking method.
The experiment results showed that the proposed features combined with VRRF method have a good performance in HCC image classification problem.
American Psychological Association (APA)
Xia, Bingbing& Jiang, Huiyan& Liu, Huiling& Yi, Dehui. 2016. A Novel Hepatocellular Carcinoma Image Classification Method Based on Voting Ranking Random Forests. Computational and Mathematical Methods in Medicine،Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1100086
Modern Language Association (MLA)
Xia, Bingbing…[et al.]. A Novel Hepatocellular Carcinoma Image Classification Method Based on Voting Ranking Random Forests. Computational and Mathematical Methods in Medicine No. 2016 (2016), pp.1-8.
https://search.emarefa.net/detail/BIM-1100086
American Medical Association (AMA)
Xia, Bingbing& Jiang, Huiyan& Liu, Huiling& Yi, Dehui. A Novel Hepatocellular Carcinoma Image Classification Method Based on Voting Ranking Random Forests. Computational and Mathematical Methods in Medicine. 2016. Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1100086
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1100086