Multifeature‐based medical image segmentation
Yashwant Kurmi, Vijayshri Chaurasia · IET Image Processing · 2018
Histopathology image segmentation is an important area in the field of computer aided diagnosis using image processing. This study presents a local feature‐based novel technique for the segmentation of histopathology images. It mainly focuses on a system that segments overlapped nuclei (OLN) without affecting the general non‐OLN segmentation performance. The proposed method suggests a three‐stage system. The initial segmentation is done by using local features for the demarcation of nuclei regions. In the second stage, salient‐based active contour is applied for complete nucleus‐region identification. In the final step, the OLN are identified and segmented using a Gaussian distribution and entropy maximisation. The performance of the proposed segmentation method is evaluated on the basis of precision, recall, accuracy, and ‐score. The proposed method is simulated on animal diagnostics laboratory histopathology image dataset and reported 90.3% average accuracy with average F 1 ‐score 0.937. Simulation results confirm the superiority of the proposed method as compared with the existing state‐of‐art methods.