  A good segmentation result depends on a set of "correct" choice for the
seeds. When the input images are noisy, the seeds may fall on atypical pixels
that are not representative of the region statistics. This can lead to
erroneous segmentation results. In this paper, an automatic seeded region
growing algorithm is proposed for cellular image segmentation. First, the
regions of interest (ROIs) extracted from the preprocessed image. Second, the
initial seeds are automatically selected based on ROIs extracted from the
image. Third, the most reprehensive seeds are selected using a machine learning
algorithm. Finally, the cellular image is segmented into regions where each
region corresponds to a seed. The aim of the proposed is to automatically
extract the Region of Interests (ROI) from in the cellular images in terms of
overcoming the explosion, under segmentation and over segmentation problems.
Experimental results show that the proposed algorithm can improve the segmented
image and the segmented results are less noisy as compared to some existing
algorithms.
