A comparison of the tissue classification and the segmentation propagation techniques in MRI brain image segmentation

Jinsong Ren; Beatrix Sneller; Daniel Rueckert; Joseph Hajnal; Rolf Heckemann; Stephen Smith; John Vickers; Derek Hill

doi:10.1117/12.595146

29 April 2005 A comparison of the tissue classification and the segmentation propagation techniques in MRI brain image segmentation

Jinsong Ren, Beatrix Sneller, Daniel Rueckert, Joseph Hajnal, Rolf Heckemann, Stephen Smith, John Vickers, Derek Hill

Author Affiliations +

Proceedings Volume 5747, Medical Imaging 2005: Image Processing; (2005) https://doi.org/10.1117/12.595146
Event: Medical Imaging, 2005, San Diego, California, United States

Abstract

Tissue classifications of the MRI brain images can either be obtained by segmenting the images or propagating the segmentations of the atlas to the target image. This paper compares the classification results of the direct segmentation method using FAST with those of the segmentation propagation method using nreg and the MNI Brainweb phantom images. The direct segmentation is carried out by extracting the brain and classifying the tissues by FAST. The segmentation propagation is carried out by registering the Brainweb atlas image to the target images by affine registration, followed by non-rigid registration at different control spacing, then transforming the PVE (partial volume effect) fuzzy membership images of cerebrospinal fluid (CSF), grey matter (GM) and white matter (WM) of the atlas image into the target space respectively. We have compared the running time, reproducibility, global and local differences between the two methods. Direct segmentation is much faster. There is no significant difference in reproducibility between the two techniques. There are significant global volume differences on some tissue types between them. Visual inspection was used to localize these differences. This study had no gold standard segmentations with which to compare the automatic segmentation solutions, but the global and local volume differences suggest that the most appropriate algorithm is likely to be application dependent.

Citation Download Citation

Jinsong Ren, Beatrix Sneller, Daniel Rueckert, Joseph Hajnal, Rolf Heckemann, Stephen Smith, John Vickers, and Derek Hill "A comparison of the tissue classification and the segmentation propagation techniques in MRI brain image segmentation", Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005); https://doi.org/10.1117/12.595146

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