Pang Wenbo, Jiang Huiyan, Li Siqi
Software College, Northeastern University, Shenyang 110819, China.
Biomed Res Int. 2017;2017:9718386. doi: 10.1155/2017/9718386. Epub 2017 Jul 17.
Accurate classification of hepatocellular carcinoma (HCC) image is of great importance in pathology diagnosis and treatment. This paper proposes a concave-convex variation (CCV) method to optimize three classifiers (random forest, support vector machine, and extreme learning machine) for the more accurate HCC image classification results. First, in preprocessing stage, hematoxylin-eosin (H&E) pathological images are enhanced using bilateral filter and each HCC image patch is obtained under the guidance of pathologists. Then, after extracting the complete features of each patch, a new sparse contribution (SC) feature selection model is established to select the beneficial features for each classifier. Finally, a concave-convex variation method is developed to improve the performance of classifiers. Experiments using 1260 HCC image patches demonstrate that our proposed CCV classifiers have improved greatly compared to each original classifier and CCV-random forest (CCV-RF) performs the best for HCC image recognition.
肝细胞癌（HCC）图像的准确分类在病理诊断和治疗中具有重要意义。本文提出一种凹凸变化（CCV）方法，以优化三种分类器（随机森林、支持向量机和极限学习机），从而获得更准确的HCC图像分类结果。首先，在预处理阶段，使用双边滤波器对苏木精-伊红（H&E）病理图像进行增强，并在病理学家的指导下获取每个HCC图像块。然后，在提取每个图像块的完整特征后，建立一个新的稀疏贡献（SC）特征选择模型，为每个分类器选择有益特征。最后，开发一种凹凸变化方法来提高分类器的性能。使用1260个HCC图像块进行的实验表明，我们提出的CCV分类器与每个原始分类器相比有了很大改进，并且CCV-随机森林（CCV-RF）在HCC图像识别方面表现最佳。
