A Novel Hepatocellular Carcinoma Image Classification Method Based on Voting Ranking Random Forests.
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 Co-occurrence 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.
Software College, Northeastern University, Shenyang 110819, China.
Bingbing
B
Xia
Software College, Northeastern University, Shenyang 110819, China.
Huiyan
H
Jiang
Software College, Northeastern University, Shenyang 110819, China.
Huiling
H
Liu
The Department of Hepatobiliary Surgery, The First Affiliated Hospital of China Medical University, Shenyang 110001, China.
Dehui
D
Yi
D006528:Carcinoma, Hepatocellular / Q000145:classification / Q000175:diagnosis* / Q000473:pathology; D019295:Computational Biology / Q000379:methods; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008113:Liver Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000473:pathology; D012985:Software Design; D060388:Support Vector Machine