In medical image analysis and segmentation, many conventional methods work very well on good quality
tissue section images, but often fail when the images are not of good quality. Active contours or snakes
are widely used in medical image processing applications especially for boundary detection. However, the
problems with initialization and poor performance of snakes on noisy images limit their efficacy. As an
alternative, this research presents an efficient and robust method to segment cell nuclei and their respective
boundaries for low contrast cell images using a combination of a radial search and interpolation methods.
This radial search method can be used in medical image analysis and segmentation applications for images
which are very noisy or whose structural regions are not very clear. The processes in this method consists of
(1) extracting the location of the cell nuclei (2) finding the edge information of the given image (3) applying
radial search on the edge image patch for finding the radial initialization and finally (4) using an interpolation
method to find the desired boundary points, which describe the potential boundary points to best fit to that
candidate shape or cell. The results shown on the images of branch aorta of rabbit are suggesting that the
proposed radial search method correctly finds the boundaries even on very low contrast images, which can
be used for further medical image analysis.