Cell segmentation in phase contrast microscopy images via semi-supervised classification over optics-related features Hang Su a,b,⇑ , Zhaozheng Yin c , Seungil Huh b , Takeo Kanade b a Department of Electronic Engineering, Shanghai Jiaotong University, China b The Robotics Institute, Carnegie Mellon University, United States c Department of Computer Science, Missouri University of Science and Technology, United States article info Article history: Available online 29 April 2013 Keywords: Phase contrast microscopy image Sparse representation Phase retardation feature Semi-supervised classification Cell segmentation abstract 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 pro- posed approach produces quality segmentation of individual cells and outperforms previous approaches. Ó 2013 Elsevier B.V. All rights reserved. 1. Introduction Phase contrast microscopy (Murphy, 2001) is a widely used optical microscopy technique, especially for the examination of transparent and colorless specimens. It produces images by con- verting the phase difference between waves traversing the biolog- ical material and those passing through the surrounding medium to a visible difference in image intensity. Therefore, it allows cell observation without exogenous fixing or staining, and thus enables a long-term monitoring of proliferation processes of live cells by recording time-lapse microscopy images, such as cell migration, cell cycle, and cell differentiation. Although analysis of such images can be conducted manually, large volumes of image data captured from long-time high-throughput biological experiments make manual analysis extremely time-consuming, labor-intensive and prone to human error. Therefore, it is imperative to develop a com- puter-aided method to identify the individual cells and measure relevant cell characteristics automatically and accurately. Among the tasks of automatic microscopy cell image analysis, cell segmen- tation is recognized as one of the most fundamental components, because lots of subsequent analysis is performed based on it, e.g., cell tracking and cell event detection, etc. 1.1. Related work Different challenges arise for cell segmentation in phase con- trast microscopy images. Firstly, phase contrast microscopy images are often of low contrast between cells and background. Therefore, the single grayscale thresholding (e.g., Otsu, 1979) may fail to sep- arate dark cells out of the background due to the low contrast (see Fig. 1.2). A multi-level thresholding method, which segments images into bright cells, dark cells and background, improves the results, but cells are still not well segmented due to the intensity similarities between some cells and background (see Fig. 1.3). Deformable models, a popular category of approaches including active contours (Yezzi et al., 1999) and level sets (Vese et al., 2002), are also challenged by low contrast of phase contrast microscopy images. Active contour (Grimm et al., 2003; Li et al., 2009) detects positions of cells’ edges in phase contrast microscopy images, but it presents poor results if the boundaries are fuzzy. Le- vel-set based approaches (Xiong et al., 2006; Padfield et al., 2009; Ambuhl et al., 2012), which compute energy of an object using intensity variance inside and outside the contours, are sensitive to initializations (see Fig. 1.4). 1361-8415/$ - see front matter Ó 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.media.2013.04.004 ⇑ Corresponding author at: The Robotics Institute, Carnegie Mellon University, United States. Tel.: +1 412 996 5949. E-mail address: suhangss@gmail.com (H. Su). Medical Image Analysis 17 (2013) 746–765 Contents lists available at SciVerse ScienceDirect Medical Image Analysis journal homepage: www.elsevier.com/locate/media