Automatic segmentation of bright-field cell images is important to cell biologists, but is difficult to achieve due to the complex nature of the cells in bright-field images (poor contrast, broken halo, missing boundaries). The standard segmentation techniques, such as the level set method and active contours, are not able to overcome these features of bright-field images. Consequently, poor segmentation results are produced. In this paper, we present a robust segmentation method, which combines the techniques of graph cut, multiresolution, and Bhattacharyya measure, performed in a multiscale framework, to locate multiple cells in bright-field images. The issue of low contrast in bright-field images is addressed by determining the difference in intensity profiles of the cells and the background. The resulting segmentation on the entire image frame provides global information. Then a local segmentation at different regions of interest is performed to obtain finer details of the segmentation result. We illustrate the effectiveness of the method by presenting the segmentation results of C2C12 (muscle) cells in bright-field images.

Date Published: 13 March 2013
PDF: 8 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86693S (13 March 2013); doi: 10.1117/12.2007002

Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)