Comparison of EM-based and level set partial volume segmentations of MR brain images

Hemant D. Tagare; Yunmei Chen; Robert K. Fulbright

doi:10.1117/12.770308

11 March 2008 Comparison of EM-based and level set partial volume segmentations of MR brain images

Hemant D. Tagare, Yunmei Chen, Robert K. Fulbright

Proceedings Volume 6914, Medical Imaging 2008: Image Processing; 69140N (2008) https://doi.org/10.1117/12.770308
Event: Medical Imaging, 2008, San Diego, California, United States

Abstract

EM and level set algorithms are competing methods for segmenting MRI brain images. This paper presents a fair comparison of the two techniques using the Montreal Neurological Institute's software phantom. There are many flavors of level set algorithms for segmentation into multiple regions (multi-phase algorithms, multi-layer algorithms). The specific algorithm evaluated by us is a variant of the multi-layer level set algorithm. It uses a single level set function for segmenting the image into multiple classes and can be run to completion without restarting. The EM-based algorithm is standard. Both algorithms have the capacity to model a variable number of partial volume classes as well as image inhomogeneity (bias field). Our evaluation consists of systematically changing the number of partial volume classes, additive image noise, and regularization parameters. The results suggest that the performances of both algorithms are comparable across noise, number of partial volume classes, and regularization. The segmentation errors of both algorithms are around 5 - 10% for cerebrospinal fluid, gray and white matter. The level set algorithm appears to have a slight advantage for gray matter segmentation. This may be beneficial in studying certain brain diseases (Multiple Sclerosis or Alzheimer's disease) where small changes in gray matter volume are significant.

Citation Download Citation

Hemant D. Tagare, Yunmei Chen, and Robert K. Fulbright "Comparison of EM-based and level set partial volume segmentations of MR brain images", Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69140N (11 March 2008); https://doi.org/10.1117/12.770308

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