Cell segmentation in phase contrast microscopy images via semi-supervised classification over optics-related features
Cell segmentation in phase contrast microscopy images via semi-supervised classification over optics-related features
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DOI：
10.1016/j.media.2013.04.004
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发表时间：
2013-10-01
影响因子：
10.9
通讯作者：
Kanade, Takeo
中科院分区：
文献类型：
--
作者：
Su, Hang;Yin, Zhaozheng;Kanade, Takeo
相差显微镜是观察长期多细胞过程最常见和最方便的成像方式之一，它通过穿过透明标本和具有不同延迟相位的背景介质的光的干涉来生成图像。尽管经过多年的研究，计算机辅助相衬显微镜对细胞行为的分析仍受到相衬光学器件引起的图像质量和伪影的挑战。为了解决尚未解决的挑战，作者提出了（1）一种产生相位延迟特征的相衬显微镜图像恢复方法，这是相衬显微镜的固有特征，以及（2）一种基于半监督学习的细胞分割算法，这是各种细胞行为分析的基本任务。具体来说，首先使用衍射图案字典对相衬显微镜图像的图像形成过程进行计算建模；因此，相衬显微镜图像的每个像素都由基底的线性组合表示，我们称之为相位延迟特征。然后通过聚集具有相似相位延迟特征的相邻像素，将图像划分为相位同质原子。因此，细胞分割是通过半监督分类技术对同质原子进行的。实验表明，所提出的方法可以对单个细胞进行高质量分割，并且优于以前的方法。 (C) 2013 Elsevier B.V. 保留所有权利。
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. (C) 2013 Elsevier B.V. All rights reserved.