RESEARCH ARTICLE


Segmentation of Fluorescence Microscopy Cell Images Using Unsupervised Mining



Xian Du1, Sumeet Dua*, 1, 2
1 Data Mining Research Laboratory, Department of Computer Science, College of Engineering and Science, Louisiana Tech University, Ruston, LA, USA
2 School of Medicine, Louisiana State Health Sciences Center, New Orleans, LA, USA


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© Du and Dua; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the Data Mining Research Laboratory, Department of Computer Science, College of Engineering and Science, Louisiana Tech University, Ruston, LA 71272, USA; Tel: 318-257-2830; Fax: 318-257-4922; E-mail: sdua@latech.edu


Abstract

The accurate measurement of cell and nuclei contours are critical for the sensitive and specific detection of changes in normal cells in several medical informatics disciplines. Within microscopy, this task is facilitated using fluorescence cell stains, and segmentation is often the first step in such approaches. Due to the complex nature of cell issues and problems inherent to microscopy, unsupervised mining approaches of clustering can be incorporated in the segmentation of cells. In this study, we have developed and evaluated the performance of multiple unsupervised data mining techniques in cell image segmentation. We adapt four distinctive, yet complementary, methods for unsupervised learning, including those based on k-means clustering, EM, Otsu’s threshold, and GMAC. Validation measures are defined, and the performance of the techniques is evaluated both quantitatively and qualitatively using synthetic and recently published real data. Experimental results demonstrate that k-means, Otsu’s threshold, and GMAC perform similarly, and have more precise segmentation results than EM. We report that EM has higher recall values and lower precision results from under-segmentation due to its Gaussian model assumption. We also demonstrate that these methods need spatial information to segment complex real cell images with a high degree of efficacy, as expected in many medical informatics applications.

Keywords: Fluorescence microscope cell image, segmentation, K-means clustering, EM, threshold, GMAC.