Colon cancer prediction using 2DReCA segmentation and hybrid features on histopathology images

作     者：Babu, Tina Singh, Tripty Gupta, Deepa 

作者机构：Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Comp Sci & Engn Bengaluru Karnataka India 

出 版 物：《IET IMAGE PROCESSING》 (IET影像处理)

年 卷 期：2020年第14卷第16期

页      面：4144-4157页

核心收录：

学科分类：0808[工学-电气工程] 1002[医学-临床医学] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：visual databases image texture cancer entropy image segmentation image classification feature extraction biological tissues medical image processing colon cancer prediction hybrid features histopathology images histopathological images cancer-free colon tissue elliptical shape structure alters malignant tissue colon biopsy image segmentation multiple databases image segmentation method multilevel thresholding Renyi's two-dimensional entropy elliptical epithelial cells segmented background successful segmentation shape descriptors texture features grey-level co-occurrence matrix block-wise elliptical local binary pattern pre-processed grey-scale colon images hybrid feature vector data sets multiple performance measures 

摘      要：Since histopathological images exist in various forms, performing segmentation on these images is tedious. While in cancer-free colon tissue, epithelial cells generally have an elliptical shape;their structure alters in a malignant tissue. This study proposes a technique consisting of colon biopsy image segmentation and a hybrid set of features for classification, and is evaluated on multiple databases with various levels of magnifications. This study presents a novel image segmentation method with multi-level thresholding based on Renyi s two-dimensional entropy with a cultural algorithm (2DR(e)CA). Based on the entropy, elliptical epithelial cells, being the region of interest, are identified from the segmented background. After successful segmentation, shape descriptors are extracted with morphological operations. Two sets of texture features (grey-level co-occurrence matrix and block-wise elliptical local binary pattern) are calculated based on pre-processed grey-scale colon images. The proposed hybrid feature vector set, then concatenates the extracted features for training and testing with a random forest classifier. The proposed segmentation and classification model is evaluated by considering four data sets consisting of various colon images at different magnifications. In addition, it is evaluated by multiple performance measures and compared with existing techniques.