Proceedings Article | 27 March 2009
Proc. SPIE. 7259, Medical Imaging 2009: Image Processing
KEYWORDS: Magnetic resonance imaging, Image segmentation, Image processing, Diffusion, Distortion, Spinal cord, Chemical elements, Mathematical morphology, Anisotropy, Diffusion tensor imaging
This paper presents a segmentation technique for diffusion tensor imaging (DTI). This technique is based on a
tensorial morphological gradient (TMG), defined as the maximum dissimilarity over the neighborhood. Once
this gradient is computed, the tensorial segmentation problem becomes an scalar one, which can be solved
by conventional techniques, such as watershed transform and thresholding. Similarity functions, namely the
dot product, the tensorial dot product, the J-divergence and the Frobenius norm, were compared, in order to
understand their differences regarding the measurement of tensor dissimilarities. The study showed that the dot
product and the tensorial dot product turned out to be inappropriate for computation of the TMG, while the
Frobenius norm and the J-divergence were both capable of measuring tensor dissimilarities, despite the distortion
of Frobenius norm, since it is not an affine invariant measure. In order to validate the TMG as a solution for DTI
segmentation, its computation was performed using distinct similarity measures and structuring elements. TMG
results were also compared to fractional anisotropy. Finally, synthetic and real DTI were used in the method
validation. Experiments showed that the TMG enables the segmentation of DTI by watershed transform or by a
simple choice of a threshold. The strength of the proposed segmentation method is its simplicity and robustness,
consequences of TMG computation. It enables the use, not only of well-known algorithms and tools from the
mathematical morphology, but also of any other segmentation method to segment DTI, since TMG computation
transforms tensorial images in scalar ones.
