Overview of Automatic Seed Selection Methods
for Biomedical Images Segmentation
Ahlem Melouah, and Soumia Layachi
Department
of Informatics, Badji-Mokhtar Annaba University, Algeria
Abstract:
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.
Keywords: Automatic seed selection, biomedical
image, region growing segmentation, region of interest, region extraction, edge
extraction, feature extraction.