In this paper, we present color image segmentation based on automatic seed pixel selection. First, the input RGB color image is transformed into HSVcolor space. Second, the initial seeds are automatically selected based on non-edge and smoothness at pixel’s neighbor as criterion. Third, the seed pixels are merged to form seed region if they are connected. Fourth, a seeded region growing method ...

Many computer aided diagnosis (CAD) systems help radiologist on difficult task of mass detection in a breast mammogram and, besides, they also provide interpretation about detected mass. One of the most crucial information of a mass is its shape and contour, since it provides valuable information about spread ability of a mass. However, accuracy of shape recognition of a mass highly related wit...

A fast hybrid system for the automated detection and verification of active regions (plages) and filaments in solar images is presented in this paper. The system combines automated image processing with machine learning. The imaging part consists of five major stages. The solar disk is detected in the first stage using a morphological hit-miss transform, watershed transform and Filling algorith...

We propose an algorithm of blood vessel segmentation for MRA data in this paper. The generic region growing, as well as thresholding, is not appropriate to extract the whole part of the vessels on MRA data. This is because of the image property of the MRA, where the intensity of each pixel on the blood area depends on the amount of blood flow. Moreover, thin vessels are affected by the partial ...

The goal of image segmentation is to partition a digital image into disjoint regions of interest. Of the many proposed image-segmentation methods, region growing has been one of the most popular. Research on region growing, however, has focused primarily on the design of feature measures and on growing and merging criteria. Most of these methods have an inherent dependence on the order in which...

The performance of segmentation algorithms often depends on numerous parameters such as initial seed and contour placement, threshold selection, and other region-dependent a priori knowledge. While necessary for successful segmentation, appropriate setting of these parameters can be difficult to achieve and requires a user experienced with the algorithm and knowledge of the application field. I...

This paper describes generalization of multi-class region growing algorithm allowing for segmentation of 3D images (series of slices). The multi-class region growing algorithm was proposed in [1]. Additionally, a new method for finding the start region was presented. As its 2D version the new algorithm does not need initial parameters since it features segmentation quality assessment. A series ...