Segmentation of Specific Tissue in Brain MR Images Based on Weighted Similarity Measurement
Liu Hon · Chinese Journal of Computers · 2014
The multi-atlas based segmentation method provides an effective solution for automatically and accurately segmenting specific tissues such as the hippocampus and amygdala from brain MR images.In order to speed up processing,this method needs to pick out those atlases which are similar to the target segmenting image to be the references of segmentation.Traditional multi-atlas methods generally select atlases in accordance with the intensity similarity between atlas image and target image,without considering the morphology similarity of both images in local of the segmenting subject,making the reference value of selected atlases be unguaranteed.Toaddress this shortage,this paper proposes a segmenting subject centric weighted image similarity measurement.First,the atlas image is registered to the target image globally to obtain the estimated localization of the segmenting subject in target image;and then,the similarity is measured by the local distortion of the segmenting subject between two images.We apply this weighted similarity into multi-atlas based segmentation method,where only those atlases which have high weighted similarities with the target image are picked for segmenting,and the segmentation labels are also fused by taking the weighted similarity as the weight.Experiments on segmenting the putamen tissue of brain MR images from IBSR demonstrate that this weighted similarity based multi-atlas segmentation method can achieve high accuracy.