Cell segmentation in phase contrast microscopy images via semi-supervised classification over optics-related features

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• 作者：Hang Su ; Zhaozheng Yin ; Seungil Huh ; Takeo Kanade
• 关键词：Phase contrast microscopy image ; Sparse representation ; Phase retardation feature ; Semi-supervised classification ; Cell segmentation
• 刊名：Medical Image Analysis
• 出版年：2013
• 出版时间：October, 2013
• 年：2013
• 卷：17
• 期：7
• 页码：746-765
• 全文大小：11995 K

文摘

Phase-contrast microscopy is one of the most common and convenient imaging modalities to observe long-term multi-cellular processes, which generates images by the interference of lights passing through transparent specimens and background medium with different retarded phases. Despite many years of study, computer-aided phase contrast microscopy analysis on cell behavior is challenged by image qualities and artifacts caused by phase contrast optics. Addressing the unsolved challenges, the authors propose (1) a phase contrast microscopy image restoration method that produces phase retardation features, which are intrinsic features of phase contrast microscopy, and (2) a semi-supervised learning based algorithm for cell segmentation, which is a fundamental task for various cell behavior analysis. Specifically, the image formation process of phase contrast microscopy images is first computationally modeled with a dictionary of diffraction patterns; as a result, each pixel of a phase contrast microscopy image is represented by a linear combination of the bases, which we call phase retardation features. Images are then partitioned into phase-homogeneous atoms by clustering neighboring pixels with similar phase retardation features. Consequently, cell segmentation is performed via a semi-supervised classification technique over the phase-homogeneous atoms. Experiments demonstrate that the proposed approach produces quality segmentation of individual cells and outperforms previous approaches.