Seeds classification for image segmentation based on 3-D affine moment invariants

Abstract

Purpose Image segmentation is a crucial topic in computer vision and medical image processing. However, accurate image segmentation is still a challenging task for many medical applications. The region growing based image segmentation process starts by selecting seed points within the region of interest. Hence, the segmentation algorithm is sensitive to the initial seeds and the result can be influenced greatly by the accuracy of seed selection process. Manual seed selection can be time-consuming and requires an expert to complete the selection. In this paper, we propose an innovative approach to automating the initialization process of the liver segmentation of magnetic resonance images. The seed points, which are needed to initialize the segmentation process we proposed in [1], are extracted and classified by using affine invariant moments and artificial neural network.