Segmentation of Fluorescence Microscopy Cell Images Using Unsupervised Mining

Data Mining Research Laboratory, Department of Computer Science, College of Engineering and Science, Louisiana Tech University, Ruston, LA, USA.
The Open Medical Informatics Journal 08/2010; 4(1):41-9. DOI: 10.2174/1874431101004020041
Source: PubMed


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.

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Available from: Xian du, Feb 25, 2014
    • "More general approaches to manage images from different sources rely on unsupervised data mining. Successful examples are reported for cell segmentation [14] [17] [4]. More complex structures like those shown in this paper, however, present challenges that rule out most unsupervised approaches. "
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    • "Several studies have been proposed for segmenting fluorescent images [4] [7] [9] [10]. In [1], Du et al. developed and evaluated the performance of unsupervised data mining techniques in cell image segmentation. They compared kmeans clustering, Expectation Maximization (EM), Otsu´s threshold method, and Global Minimization by Active Contour (GMAC). "
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    • "To make the validation more accurate and precise, the image segmentation should indicate cells’ membrane, cells’ cytoplasm and/or nuclei and/or other organelles (e.g. the lysosome) efficiently and robustly [7-9]. The error in objects detection ought to be as small as possible and should be given explicitly since it determines errors in features important in the process of diagnosis. "
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