A semi-automated system based on level sets and invariant spatial interrelation shape features for Caenorhabditis elegans phenotypes |
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| Affiliation: | 1. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116023, China;2. Operation Software and Simulation Institute, Dalian Navy Academy, Dalian 116018, China;1. Department of Mathematics, Shanghai Jiao Tong University, China;2. Department of Mathematics, Tongji University, China;3. Department of Mathematics, Hong Kong Baptist University, Hong Kong;1. College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China;2. Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan;1. School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China;2. School of Electrical Engineering and Automation, Qilu University of Technology, Jinan, Shandong 250353, China;1. National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China;2. School of Computer Science and Technology, Xidian University, Xi’an 710071, China |
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| Abstract: | Caenorhabditis elegans shares several molecular and physiological homologies with humans and thus plays a key role in studying biological processes. As a consequence, much progress has been made in automating the analysis of C. elegans. However, there is still a strong need to achieve more progress in automating the analysis of static images of adult worms. In this paper, a three-phase semi-automated system has been proposed. As a first phase, a novel segmentation framework, based on variational level sets and local pressure force function, has been introduced to handle effectively images corrupted with intensity inhomogeneity. Then, a set of robust invariant symbolic features for high-throughput screening of image-based C. elegans phenotypes are extracted. Finally, a classification model is applied to discriminate between the different subsets. The proposed system demonstrates its effectiveness in measuring morphological phenotypes in individual worms of C. elegans. |
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| Keywords: | Level sets Sign pressure force Axis of least inertia Image-based feature classification Shape-based invariant feature |
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