A novel segmentation algorithm for nucleus in white blood cells based on low-rank representation

作者：Cao, Feilong^*; Cai, Miaomiao; Chu, Jianjun; Zhao, Jianwei; Zhou, Zhenghua

来源：Neural Computing & Applications, 2017, 28(S1): S503-S511.

DOI：10.1007/s00521-016-2391-8

• Low-rank representation
• Image segmentation
• Manifold learning
• White blood cell (WBC)

White blood cells (WBCs) segmentation is a challenging problem in the study of automated morphological systems, due to both the complex nature of the cells and the uncertainty that is present in video microscopy. This paper investigates how to boost the effects of region-based nucleus segmentation in WBCs by means of optimal thresholding and low-rank representation. The main idea is firstly using optimal thresholding to obtain the possible uniform WBC regions in the input image. After that, a manifold-based low-rank representation technique is employed to infer a unified affinity matrix that implicitly encodes the segmentation of the pixels of possible WBC regions. This is achieved by separating the low-rank affinities from the feature matrix into a pair of sparse and low-rank matrices. The experiments show that the proposed method is possible to produce better segmentation results compared with existing approaches.

• 出版日期2017-12
• 单位中国计量大学