Improved Toboggan Segmentation Algorithm for Magnetic Resonance Images

摘要：

The precise segmentation of Magnetic Resonance Images (MRI) is an important subject in both medical and computer science communities. The intrinsic complexity of the images and their relative lack of systematics have brought to develop different approaches to segment the different parts of human head. This paper investigates a novel feature extraction approach to MRI segmentation based on Feed-back Pulse Coupled Neural Network in conjunction with toboggan theory. Due to the dynamics of the FPCNN, multiple unconnected groups of neurons will often pulse at the same time, calling for further processing to identify distinct regions. We locate the objects label by FPCNN. Finally, toboggan automatically partitions the MRI image. The experimental results show that the proposed algorithm performs well compared to the traditional algorithms.

关键词： MRI image segmentation toboggan Pulse coupled neural network.

作者: Li GUO Jianhua WU Zhao-yu PIAN Kun WANG

作者单位: Northeastern University, China

会议类型: 国际会议

会议名称: 2nd IEEE Conference on Industrial Electronics and Applications(ICIEA 2007)(第二届IEEE工业电子与应用国际会议)

会议地点: 哈尔滨

会议语种:英文

在线出版日期: 2007-05-23（万方平台首次上网日期，不代表论文的发表时间）