Interest-Based Ordering for Fuzzy Morphology on White Blood Cell Image Segmentation

An Interest-based Ordering Scheme (IOS) for fuzzy morphology on White-Blood-Cell (WBC) image segmentation is proposed to improve accuracy of segmentation. The proposed method shows a high accuracy in segmenting both high- and low-density nuclei. Further, its running time is low, so it can be used for real applications. To evaluate the performance of the proposed method, 100 WBC images and 10 leukemia images are used, and the experimental results show that the proposed IOS segments a nucleus in WBC images 3.99% more accurately on average than the Lexicographical Ordering Scheme (LOS) does and 5.29% more accurately on average than the combined Fuzzy Clustering and Binary Morphology (FCBM) method does. The proposal method segments a cytoplasm 20.72% more accurately on average than the FCBM method. The WBC image segmentation is a part of WBC classification in an automatic cancer-diagnosis application that is being developed. In addition, the proposed method can be used to segment any images that focus on the important color of an object of interest.

Registration-based segmentation of nerve cells in microscopy images

Nerve cell segmentation is important for better understanding of the connections between broken nerves. In this paper, we propose a registration-based segmentation to improve the segmentation from a series of microscopic image sequence. A global iterative closest point (ICP) registration is first employed to find the corresponding cells between neighboring frames. Then a local ICP registration is adopted to refine shape matching between corresponding cells. When registering the current frame to the previous frame, a missing cell in the current frame can obtain a dummy region with respect to the previous frame. The dummy region is then confirmed as a cell by checking the correspondence of the cell among the previous, current and next frames. Experimental results show that the proposed registration based algorithm recovers the cells that are missed in conventional segmentation.