A fully automatic segmentation techniques in MRI brain tumor segmentation using fuzzy clustering techniques

Analysis of structural changes in the brain through magnetic resonance imaging can provide useful data for diagnosis and clinical supervision of patients through dementia. While the degree of sophistication reached by the MRI equipment is high, the quantification of tissue structures and has not yet been completely solved. Segmentations that these teams now allow those structures fail where the edges are not clearly defined. In this paper a method of automatic segmentation of MRI brain images based on the use of Generalized Regression Neural Networks using genetic algorithms for parameter settings is presented. The network is trained from a single image and classifies the rest of them when the MRI images were acquired with the same protocol. A method of measuring the progressive atrophy and possible changes before a therapeutic effect should be essentially automatic and therefore independent of the radiologist.