A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
An improved seeded region growing algorithm
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Seeded region growing: an extensive and comparative study
Pattern Recognition Letters
A Bayes-Based Region-Growing Algorithm for Medical Image Segmentation
Computing in Science and Engineering
Automatic image segmentation by integrating color-edge extraction and seeded region growing
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
Considering the segmentation results of region growing depend on two key factors: seed selection and growing strategy, this paper proposed a method of adaptive seeded region growing based on edge detection, texture extraction and cloud model. Our work included two aspects. Firstly, we proposed a new method to extract region seeds automatically based on spectrum features, edge information and texture features. According to two conditions defined by us, region seeds could be extracted as accurately as possible. Secondly, we proposed an adaptive region growing strategy based on cloud model. Our strategy consisted of three major stages: expressing region by cloud model, calculating the qualitative region concept based on the backward cloud generator, and region growing based on cloud synthesis. The experiment results demonstrate seed extraction based on spectrum features, edge information and texture features has a good accuracy, and almost all of the extracted seeds are located at the homogeneous objects inner. The experiment results also demonstrate the adaptive region growing strategy based on cloud model makes regions grow not only in a simultaneous way but also with inner homogeneity.