Microsc. Microanal. 23, 932–937, 2017 doi:10.1017/S1431927617012375 © MICROSCOPY SOCIETY OF AMERICA 2017 Segmenting Microscopy Images of Multi-Well Plates Based on Image Contrast Weiyang Chen, 1, * Bo Liao, 2 Weiwei Li, 1 Xiangjun Dong, 1, * Matthew Flavel, 3 Markandeya Jois, 3 Guojun Li, 4,5 and Bo Xian 6 1 School of Information, Qilu University of Technology, Jinan 250353, China 2 College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China 3 School of Life Sciences, La Trobe University, Bundoora, VIC 3083, Australia 4 Beijing Center for Disease Prevention and Control/Beijing Center of Preventive Medicine Research, Beijing 100013, China 5 School of Public Health, Capital Medical University, Beijing 100086, China 6 Key Laboratory of Computational Biology, CAS Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China Abstract: Image segmentation is a key process in analyzing biological images. However, it is difficult to detect the differences between foreground and background when the image is unevenly illuminated. The unambiguous segmenting of multi-well plate microscopy images with various uneven illuminations is a challenging problem. Currently, no publicly available method adequately solves these various problems in bright-field multi-well plate images. Here, we propose a new method based on contrast values which removes the need for illumination correction. The presented method is effective enough to distinguish foreground and therefore a model organism (Caenorhabditis elegans) from an unevenly illuminated microscope image. In addition, the method also can solve a variety of problems caused by different uneven illumination scenarios. By applying this methodology across a wide range of multi-well plate microscopy images, we show that our approach can consistently analyze images with uneven illuminations with unparalleled accuracy and successfully solve various problems associated with uneven illumination. It can be used to process the microscopy images captured from multi-well plates and detect experimental subjects from an unevenly illuminated background. Key words: microscopy image, uneven illumination, bright field, multi-well plate, image segmentation I NTRODUCTION Bio-image informatics is becoming an increasingly impor- tant component of various biological studies (de Chaumont et al., 2012; Myers, 2012; Xian et al., 2013; Chen et al., 2015; Weissleder & Nahrendorf, 2015). Bio-image processing techniques are widely used to automatically detect and quantify biological phenotypes (Shamir et al., 2009; Neumann et al., 2010; Rihel et al., 2010; Swierczek et al., 2011; Wang et al., 2013; Yemini et al., 2013; Zhou et al., 2014; Chen & Han, 2015; Kirsanova et al., 2015; Chen et al., 2016). A wide variety of processing techniques are now available to researchers in order to achieve these desired results. Among these options, image segmentation is a vital processing technique well suited for biological image analysis. Image segmentation is the prerequisite for phenotype quantifica- tion and is central to almost all applications related to bio-image informatics (Peng, 2008). For evenly illuminated images, Otsu’s (1979) method is the commonly used approach to first determine a gray intensity threshold and subsequently segment the image. Held et al. (2011) provided a parameter optimization method to improve the image segmentation performance. The component tree method was a later development which could be applied to segment time-lapse microscopy images and track moving cells (Xiao et al., 2011). Multi-well plates are commonly used to perform high-throughput screening utilizing organisms such as Caenorhabditis elegans (Wahlby et al., 2012; O’Reilly et al., 2014), larval zebrafish (Rihel et al., 2010), or cell culture (Balcarcel & Clark, 2003). Image capturing and processing are required for these experiments to collect meaningful data at the necessary speed and accuracy needed for high- throughput screening. One major limitation of previous multi-well plate experiments has been the inherent variation of illumination observed by bright-field microscopy (Fig. 1). The variation is introduced by the surface of the individual wells and their relation to the microscope light source. These uneven illuminations introduce a factor that increases the difficulty associated with image segmentation. This presents a major obstruction to the applications of multi-well plates in high-throughput experiments which rely on the cultured model organisms or cells to be automatically distinguished from the background in experimental images. For example, Figure 1 shows the typical bright-field microscopy image of a multi-well plate. The regions outside of the well have a lower gray intensity, and the gray intensity increases from well *Corresponding authors. chenweiyang@picb.ac.cn; d-xj@163.com Received January 23, 2017; accepted June 21, 2017 https://www.cambridge.org/core/terms. https://doi.org/10.1017/S1431927617012375 Downloaded from https://www.cambridge.org/core. IP address: 191.96.45.123, on 17 Apr 2019 at 21:51:29, subject to the Cambridge Core terms of use, available at