Segmentation and shape classification of nuclei in DAPI images

This paper addresses issues of analysis of DAPI-stained microscopy images of cell samples, particularly classification of objects as single nuclei, nuclei clusters or nonnuclear material. First, segmentation is significantly improved compared to Otsu’s method [5] by choosing a more appropriate threshold, using a cost-function that explicitly relates to the quality of resulting boundary, rather than image histogram. This method applies ideas from active contour models to threshold-based segmentation, combining the local image sensitivity of the former with the simplicity and lower computational complexity of the latter. Secondly, we evaluate some novel measurements that are useful in classification of resulting shapes. Particularly, analysis of central distance profiles provides a method for improved detection of notches in nuclei clusters. Error rates are reduced to less than half compared to those of the base system, which used Fourier shape descriptors alone.