A two-layer structure prediction framework for microscopy cell detection. (April 2015)

• Record Type: Journal Article
• Authors: Xu, Yan; Wu, Weiying; Chang, Eric I-Chao; Chen, Danny; Mu, Jian; Lee, Peter P.; Blenman, Kim R.M.; Tu, Zhuowen
• Is Part Of: Computerized medical imaging and graphics. Volume 41(2015)
• Page Start: 29
• Page End: 36
• Publication Date: 2015-04
• Subjects: Microscopy cell detection -- Layered models -- Structural prediction -- Computer vision -- Machine learning; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2014.07.001
• Languages: English
• ISSNs: 0895-6111

Abstract

Abstract : Highlights: A new two-layer structural prediction framework for detecting microscopic image cells, which focuses on the representation issue to propose a model to effectively capture the rich contextual information. We tackle the problem of microscopic image cell detection from structure learning perspective. The second layer takes the output of the first layer as knowledge abstraction and propagation. Abstract: The task of microscopy cell detection is of great biological and clinical importance. However, existing algorithms for microscopy cell detection usually ignore the large variations of cells and only focus on the shape feature/descriptor design. Here we propose a new two-layer model for cell centre detection by a two-layer structure prediction framework, which is respectively built on classification for the cell centres implicitly using rich appearances and contextual information and explicit structural information for the cells. Experimental results demonstrate the efficiency and effectiveness of the proposed method over competing state-of-the-art methods, providing a viable alternative for microscopy cell detection.