Publication Type : Journal Article
Publisher : IET Image Processing
Source : (2021), IET Image Processing, Vol 14, Issue 16, 2021, (IF: 2.373)
Url : https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/iet-ipr.2019.1717
Campus : Bengaluru
School : Department of Computer Science and Engineering
Department : Computer Science
Year : 2021
Abstract : 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 Rényi's two-dimensional entropy with a cultural algorithm (2DR 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.
Cite this Research Publication : Babu, T., Singh, T., Gupta, Deepa. (2021), “Colon cancer prediction using 2DReCA segmentation and hybrid features on histopathology images”, (2021), IET Image Processing, Vol 14, Issue 16, 2021, (IF: 2.373)