TY - CHAP
T1 - Unsupervised segmentation for multiple sclerosis lesions in multimodality Magnetic Resonance images
AU - Zeng, Ziming
AU - Chen, Siping
AU - Yin, Lidong
AU - Zwiggelaar, Reyer
PY - 2015
Y1 - 2015
N2 - In this paper, a new unsupervised approach is proposed for the segmentation of Multiple Sclerosis (MS) lesions in multimodality Magnetic Resonance (MR) images. The proposed segmentation scheme is based on joint histogram modelling followed by false positive reduction and alpha matting, which is used to deal with the tissue density overlap problem and partial volume effects in MR images. Firstly, the joint histogram is generated by using fluid-attenuated inversion recovery (Flair), T1-weighted (T1-w) and T2-weighted (T2-w) MRI. Then the region for MS lesions in the joint histogram are located. Sub-sequently, the located region is projected back into the 2D MR images with potential MS lesions. Secondly, priori information is utilized to remove false positive volume of interests. Finally, the partial volume effect is modelled by using an alpha technique provides region level lesion refinement. Validation is performed on real multi-channel T1-w, T2-w, and Flair MR volumes. The experimental results show the proposed method can obtain better results than some state-of-the-art methods
AB - In this paper, a new unsupervised approach is proposed for the segmentation of Multiple Sclerosis (MS) lesions in multimodality Magnetic Resonance (MR) images. The proposed segmentation scheme is based on joint histogram modelling followed by false positive reduction and alpha matting, which is used to deal with the tissue density overlap problem and partial volume effects in MR images. Firstly, the joint histogram is generated by using fluid-attenuated inversion recovery (Flair), T1-weighted (T1-w) and T2-weighted (T2-w) MRI. Then the region for MS lesions in the joint histogram are located. Sub-sequently, the located region is projected back into the 2D MR images with potential MS lesions. Secondly, priori information is utilized to remove false positive volume of interests. Finally, the partial volume effect is modelled by using an alpha technique provides region level lesion refinement. Validation is performed on real multi-channel T1-w, T2-w, and Flair MR volumes. The experimental results show the proposed method can obtain better results than some state-of-the-art methods
UR - http://hdl.handle.net/2160/42860
U2 - 10.1109/BMEI.2015.7401486
DO - 10.1109/BMEI.2015.7401486
M3 - Other chapter contribution
SP - 126
EP - 130
BT - 2015 8th International Conference on BioMedical Engineering and Informatics (BMEI 2015)
PB - IEEE Press
ER -