TY - GEN
T1 - A multi-objectively-optimized graph-based segmentation method for breast ultrasound image
AU - Zhang, Qiangzhi
AU - Zhao, Xia
AU - Huang, Qinghua
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014
Y1 - 2014
N2 - Segmentation of medical image, as the most essential and important step in the computer-aided diagnosis system, can greatly influence the system performance. Better segmentation to a great extent means better performance. Among many proposed segmentation algorithms, graph-based segmentation has become a hot one in the past few years because of the simple structure and rich theories. After the robust graph-based segmentation method (RGB) was introduced in 2010, a parameter-automatically-optimized robust graph-based segmentation method (PAORGB) was presented in 2013 as well, to optimize the two key parameters of RGB utilizing the particle swarm optimization algorithm (PSO). However, single-objectively-optimized PAORGB cannot well guarantee the global optimization. Therefore, this paper continues the work of PAORGB and proposes a multi-objectively-optimized robust graph-based segmentation method (MOORGB) to further improve the performance of RGB. Experimental results have shown that MOORGB can get better segmentation results from breast ultrasound images compared to PAORGB.
AB - Segmentation of medical image, as the most essential and important step in the computer-aided diagnosis system, can greatly influence the system performance. Better segmentation to a great extent means better performance. Among many proposed segmentation algorithms, graph-based segmentation has become a hot one in the past few years because of the simple structure and rich theories. After the robust graph-based segmentation method (RGB) was introduced in 2010, a parameter-automatically-optimized robust graph-based segmentation method (PAORGB) was presented in 2013 as well, to optimize the two key parameters of RGB utilizing the particle swarm optimization algorithm (PSO). However, single-objectively-optimized PAORGB cannot well guarantee the global optimization. Therefore, this paper continues the work of PAORGB and proposes a multi-objectively-optimized robust graph-based segmentation method (MOORGB) to further improve the performance of RGB. Experimental results have shown that MOORGB can get better segmentation results from breast ultrasound images compared to PAORGB.
KW - Breast tumor
KW - Graph-based segmentation algorithm
KW - Multi-objective optimization
KW - Particle swarm optimization
KW - Ultrasound image segmentation
UR - https://www.scopus.com/pages/publications/84942513488
U2 - 10.1109/BMEI.2014.7002754
DO - 10.1109/BMEI.2014.7002754
M3 - 会议稿件
AN - SCOPUS:84942513488
T3 - Proceedings - 2014 7th International Conference on BioMedical Engineering and Informatics, BMEI 2014
SP - 116
EP - 120
BT - Proceedings - 2014 7th International Conference on BioMedical Engineering and Informatics, BMEI 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 7th International Conference on BioMedical Engineering and Informatics, BMEI 2014
Y2 - 14 October 2014 through 16 October 2014
ER -