Abstract
Nowadays, the improvements in Magnetic Resonance Imaging systems (MRI) provide new and aditional ways to diagnose some brain disorders such as schizophrenia or the Alzheimer’s disease. One way to figure out these disorders from a MRI is through image segmentation. Image segmentation consist in partitioning an image into different regions. These regions determine diferent tissues present on the image. This results in a very interesting tool for neuroanatomical analyses. Thus, the diagnosis of some brain disorders can be figured out by analyzing the segmented image. In this paper we present a segmentation method based on a supervised version of the Self-Organizing Maps (SOM). Moreover, a probability-based clustering method is presented in order to improve the resolution of the segmented image. On the other hand, the comparisons with other methods carried out using the IBSR database, show that our method ourperforms other algorithms.
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Ortiz, A., Gorriz, J.M., Ramirez, J., Salas-Gonzalez, D. (2011). MRI Brain Image Segmentation with Supervised SOM and Probability-Based Clustering Method. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds) New Challenges on Bioinspired Applications. IWINAC 2011. Lecture Notes in Computer Science, vol 6687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21326-7_6
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DOI: https://doi.org/10.1007/978-3-642-21326-7_6
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