Paper
6 April 2016 Analysis of cancer cell morphology in fluorescence microscopy image exploiting shape descriptor
Mi-Sun Kang, Hye-Ryun Kim, Sudong Kim, Gyu Ha Ryu, Myoung-Hee Kim
Author Affiliations +
Abstract
Cancer cell morphology is closely related to their phenotype and activity. These characteristics are important in drug-response prediction for personalized cancer therapeutics. We used multi-channel fluorescence microscopy images to analyze the morphology of highly cohesive cancer cells. First, we detected individual nuclei regions in single-channel images using advanced simple linear iterative clustering. The center points of the nuclei regions were used as seeds for the Voronoi diagram method to extract spatial arrangement features from cell images. Human cancer cell populations form irregularly shaped aggregates, making their detection more difficult. We overcame this problem by identifying individual cells using an image-based shape descriptor. Finally, we analyzed the correlation between cell agglutination and cell shape.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mi-Sun Kang, Hye-Ryun Kim, Sudong Kim, Gyu Ha Ryu, and Myoung-Hee Kim "Analysis of cancer cell morphology in fluorescence microscopy image exploiting shape descriptor", Proc. SPIE 9711, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues IX, 97110O (6 April 2016); https://doi.org/10.1117/12.2213829
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KEYWORDS
Image segmentation

Cancer

Shape analysis

Image analysis

Microscopy

3D image processing

Principal component analysis

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