Automated segmentation technique with self-driven post-processing for histopathological breast cancer images

作     者：Chetna Kaushal Anshu Singla 

作者机构：Chitkara University Institute of Engineering and TechnologyChitkara UniversityPunjabIndia 

出 版 物：《CAAI Transactions on Intelligence Technology》 (智能技术学报（英文）)

年 卷 期：2020年第5卷第4期

页      面：294-300页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：operations image segmentation 

摘      要：Automated segmentation of histopathological images is a challenging task to detect cancerous cells in breast *** reviews state high accuracy to segment image,but depends on user input,say window area size,time steps,level set,magnification factor and so *** extract the region of interest effectively,the subject expert performs post-processing operations several times on the segmentation results with different input values for different parameters say,area opening,fill holes and selects most appropriate enhanced image required for further *** authors proposed an automated segmentation technique followed by self-driven post-processing operations to detect cancerous cells *** post-processing method itself determines the value of different parameters for different operations based on segmented results *** proposed technique has the following features:(i)technique is context sensitive;(ii)no prior setting of time step,weighted area coefficient parameters is required;(iii)magnification independent;(iv)post-processing operations are self-driven which enhance segmentation results *** experimental results are compared with four state-of-the-art techniques:fuzzy C-means,spatial fuzzy C-means,spatial neutrosophic distance regularised level set and convolutional neural network-based *** results obtained on two publicly available data sets show that the proposed technique outperforms effectively.