  Accurate segmentation of 3-D cell nuclei in microscopy images is essential
for the study of nuclear organization, gene expression, and cell
morphodynamics. Current image segmentation methods are challenged by the
complexity and variability of microscopy images and often over-segment or
under-segment the cell nuclei. Thus, there is a need to improve segmentation
accuracy and reliability, as well as the level of automation. In this paper, we
propose a new automated algorithm for robust segmentation of 3-D cell nuclei
using the concepts of random walk, graph theory, and mathematical morphology as
the foundation. Like other segmentation algorithms, we first use a seed
detection/marker extraction algorithm to find a seed voxel for each individual
cell nucleus. Next, using the concept of random walk on a graph we find the
probability of all the pixels in the 3-D image to reach the seed pixels of each
nucleus identified by the seed detection algorithm. We then generate a 3-D
response image by combining these probabilities for each voxel and use the
marker controlled watershed transform on this response image to obtain an
initial segmentation of the cell nuclei. Finally, we apply local region-based
active contours to obtain final segmentation of the cell nuclei. The advantage
of using such an approach is that it is capable of accurately segmenting highly
textured cells having inhomogeneous intensities and varying shapes and sizes.
The proposed algorithm was compared with three other automated nucleus
segmentation algorithms for segmentation accuracy using overlap measure,
Tanimoto index, Rand index, F-score, and Hausdorff distance measure.
Quantitative and qualitative results show that our algorithm provides improved
segmentation accuracy compared to existing algorithms.
