MR brain image segmentation by growing hierarchical SOM and probability clustering

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MR brain image segmentation by growing hierarchical SOM and probability clustering

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A fully automatic tool to assist the segmentation of brain magnetic resonance images (MRI) is presented. Thus, the figured out regions can be evaluated for the diagnosis of brain disorders. The main problem to be handled consists in discovering different regions on the image without using apriori information. The new approach consists in hybridising multiobjective optimisation for feature selection with a growing hierarchical self-organising map (GHSOM) classifier and a probability clustering method. The segmentation results yield average overlap metric values of 0.32, 0.75 and 0.69 for white matter, grey matter and cerebrospinal fluid, respectively, over the Internet Brain Segmentation Repository database. These results mean an improvement over the values reached by other existing techniques.

Inspec keywords: image segmentation; medical image processing; magnetic resonance imaging; probability

Other keywords: MRI; white matter; probability clustering method; grey matter; Internet Brain Segmentation Repository database; brain magnetic resonance images segmentation; cerebrospinal fluid; self-organising map classifier; brain disorders diagnosis

Subjects: Magnetic resonance spectrometers, auxiliary instruments and techniques; Probability theory, stochastic processes, and statistics; Image processing and restoration; Medical magnetic resonance imaging and spectroscopy; Other topics in statistics; Optical, image and video signal processing

References

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      • V.N. Vapnik . (1998) Statistical learning theory.
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      • Fan, L., Tian, D.: `A brain MR images segmentation method based on SOM neural network', IEEE Int. Conf. on Bioinformatics and Biomedical Engineering, 2007, Wuchang, People's Republic of China.
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      • T. Kohonen . (1995) Self-organizing maps.
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      • Internet Brain Segmentation Repository (IBSR). http://www.cma.mgh.harvard.edu/ibsr.
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http://iet.metastore.ingenta.com/content/journals/10.1049/el.2011.0322
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