A New Seeded Region Growing Algorithm for Large Object Segmentation
The original average contrast and peripheral contrast (ACPC) seeded region growing method performs well in segmenting small object, however, it is not effective to segment large objects with large intensity variations. In this paper, we improve the original ACPC method and propose a new seeded region growing method which is more adaptive to large objects segmentation. Every time, only one pixel which has the largest similar intensity with the average intensity of pixels in current region is picked as the new seed, and it will be added into current region. The optimal segmentation result is the one with the largest average contrast (AC) and peripheral contrast (PC). In the case of objects with serious intensity variation, we can initially set seeds both in high and low intensity regions to improve the segmentation result. In addition, we accelerate the algorithm to reduce the computation cost. The experiment results show that our method is good at segmenting large objects. Besides, the efficiency of the algorithm is greatly increased.
seeded region growing average contrast peripheral contrast external boundary
Yanhui Shan Kunyu Tsai Jian Wu
Research Center of Biomedical Engineering Graduate School of Shenzhen, Tsinghua University Shenzhen, China
国际会议
上海
英文
49-53
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）