Abstract
This paper presents a fast region-based level set approach for extraction of white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) boundaries from two dimensional magnetic resonance slices of the human brain. The raw contour was placed inside the image, which was later pushed or pulled towards the convoluted brain topology. The forces in the level set approach used three kinds of speed control functions based on region, edge and curvature. Regional speed functions were determined based on the fuzzy membership function computed using the fuzzy clustering technique, while the edge and curvature speed functions were based on gradient and signed distance transform functions, respectively. The level set algorithm was implemented to run in the “narrow band” using a “fast marching method.” The system was tested on synthetic convoluted shapes, and real magnetic resonance images of the human brain. The entire system took around a minute to estimate the WM/GM boundaries on XP1000 running Linux Operating System, when the raw contour was placed half way from its goal. The system took a few seconds if the raw contour was placed closed to the goal boundary which resulted with one hundred percent accuracy.
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Suri, J.S. (2001). White Matter/Gray Matter Boundary Segmentation Using Geometric Snakes: A Fuzzy Deformable Model. In: Singh, S., Murshed, N., Kropatsch, W. (eds) Advances in Pattern Recognition — ICAPR 2001. ICAPR 2001. Lecture Notes in Computer Science, vol 2013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44732-6_34
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DOI: https://doi.org/10.1007/3-540-44732-6_34
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