Medical Image Segmentation (MIS) has been applied to many applications such as delineation of tissue structures, cell counting, lesion and tumor tracking, etc. . Normally, the MIS approach can be classified into three types. First, segmentation using classical image processing techniques such as thresholding, morphological operations and watershed transformation. Second, train a classification model based on hand-crafted features such as statistical features, gray level co-occurrence matrix, local binary model, etc. The third approach is segmentation using high-level features obtained by a DCNN. Wu et al. used classical image processing algorithms including thresholding and seeded region growing for segmentation of human intestinal glands. However, this method took into account prior knowledge of the morphological structures of the gland and was evaluated qualitatively (Wu et al., 2005).Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get an original essay In another approach by Peng et al., k-means clustering and morphological operations were used to segment the glandular structures of the prostate. Based on these structures, a linear classifier to distinguish normal and malignant glands was constructed (Peng et al., 2010). Feature extraction and selection have been widely used in application fields such as biomedicine, image analysis, biometric authentication, etc. In the contribution by Farzam et al. and Doyle et al., texture, shape and graph features were extracted and a linear classifier was constructed to distinguish different pathological tissue sections of prostate cancer patients (Farzam et al., 2007 ) (Doyle et al., 2007). In the work presented by Naik et al., a Bayesian classifier was used to classify between lumen, stroma and nuclei. The true areas of light were identified by applying size and structure constraints. A contour line was initialized using the true light area and evolved to the inner boundary of the cores. Morphological features were calculated from the boundaries followed by a manifold learning scheme to classify cancer grades based on the reduced features (Naik et al., 2008). Using previous methods, regularly shaped glandular structures were segmented efficiently. However, due to various sample preparation factors, gland structures exhibit variations and segmenting irregularly shaped gland structures poses a challenge. To alleviate this problem, Gunduz-Demir et al. proposed an object graph-based approach that relies on decomposing the image into objects. Their approach used a three-step region growing algorithm, followed by boundary detection and false region elimination (Gunduz-Demir et al., 2009). In another work by Sirinukunwattana et al., a random polygon model for segmenting the glandular structure of human colon tissue was formulated. The glandular structures were modeled as polygons whose vertices were located on the epithelial cores. Firstly, the glandular probability map was generated using super-pixel texture features, followed by identifying the kernel vertices and constructing random polygons from the starting areas. Falsely polygons.