Overview of Automatic Seed Selection Methods for Biomedical Images Segmentation

In biomedical image processing, image segmentation is a relevant research area due to its wide spread usage and application. Seeded region growing is very attractive for semantic image segmentation by involving the high-level knowledge of image components in the seed point selection procedure. However, the seeded region growing algorithm suffers from the problems of automatic seed point generation. A seed point is the starting point for region growing and its selection is very important for the success of segmentation process. This paper presents an extensive survey on works carried out in the area of automatic seed point selection for biomedical images segmentation by seeded region growing algorithm. The main objective of this study is to provide an overview of the most recent trends for seed point selection in biomedical image segmentation.