Efficient cell segmentation tool for confocal microscopy tissue images and quantitative evaluation of FISH signals

In this paper we have presented a semi-automatic method for segmenting 3-D cell nuclei from tissue images obtained using Confocal Laser Scanning Microscope. This microscope can focus at different layers of the specimen and hence a stack of images giving a 3D representation can be obtained. The existing methods for segmenting the cells in 3-D confocal images are highly interactive and, hence, time consuming. We have developed an approach, where, given one segmented image-slice (optical section) of the set of confocal images, the remaining image-slices in the image stack can be automatically segmented in a layered approach. One of the image-slices in an image stack is considered as a representative image-slice. In this image-slice, overlapping boundary pixels are identified interactively while the remaining part of the cell boundary is marked using Laplacian of a Gaussian operator. This interactively traced portion of the boundary is considered as initial boundary for finding the overlapping boundary pixels in the neighboring image-slices. Simple basic search strategy is used for boundary search in the neighboring image-slices. The method minimizes the human interaction and is also found to be efficient and reasonably accurate. Some experimental results are presented to illustrate the usefulness of the technique. We have also given the application of our segmentation method to quantitative evaluation of fluorescence in situ hybridization (FISH) signals. A brief comparative study of visual FISH signal evaluation and the FISH signal counting by automatic image analysis is also given.