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 ...
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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.
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