Constraint factor graph cut-based active contour method for automated cellular image segmentation in RNAi screening

作     者：Chen, C. Li, H. Zhou, X. Wong, S. T. C. 

作者机构：Univ Sci & Technol China Dept EEIS Hefei 230026 Peoples R China Harvard Univ Sch Med Brigham & Womens Hosp HCNR Ctr Bioinformat Boston MA 02115 USA 

出 版 物：《JOURNAL OF MICROSCOPY》 (显微镜学杂志)

年 卷 期：2008年第230卷第2期

页      面：177-191页

核心收录：

学科分类：07[理学] 08[工学] 0804[工学-仪器科学与技术] 

基　　金：NLM NIH HHS [R01 LM008696-04  R01 LM008696] Funding Source: Medline 

主　　题：active contour automatic image segmentation constraint factor fluorescent microscopy genome-wide screening graph cut morphological algorithm RNAi 

摘      要：Image-based, high throughput genome-wide RNA interference (RNAi) experiments are increasingly carried out to facilitate the understanding of gene functions in intricate biological processes. Automated screening of such experiments generates a large number of images with great variations in image quality, which makes manual analysis unreasonably time-consuming. Therefore, effective techniques for automatic image analysis are urgently needed, in which segmentation is one of the most important steps. This paper proposes a fully automatic method for cells segmentation in genome-wide RNAi screening images. The method consists of two steps: nuclei and cytoplasm segmentation. Nuclei are extracted and labelled to initialize cytoplasm segmentation. Since the quality of RNAi image is rather poor, a novel scale-adaptive steerable filter is designed to enhance the image in order to extract long and thin protrusions on the spiky cells. Then, constraint factor GCBAC method and morphological algorithms are combined to be an integrated method to segment tight clustered cells. Compared with the results obtained by using seeded watershed and the ground truth, that is, manual labelling results by experts in RNAi screening data, our method achieves higher accuracy. Compared with active contour methods, our method consumes much less time. The positive results indicate that the proposed method can be applied in automatic image analysis of multi-channel image screening data.