BRAIN CANCER PREDICTION THROUGH SEGMENTATION OF IMAGES USING BIO-GEOGRAPHY BASED OPTIMIZATION

Brain is an organ that regulates all parts of the body activities. Detection of glioma from MRI image was an important method in medical field. In order to better interpret the medical image segmentation is generally done as a fundamental step for further processing. This work proposed a segmentation algorithm for the MRI image in which the entire work was structured into two parts. The first section of the proposed model involved pre-processing of the MRI image through weiner filter that removes noise after that extraction of skull portion took place . In second section of the model, Bio-Geography algorithm was applied which takes brain portion of pre processed input MRI image. This genetic algorithm performs crossover and mutation operation as per immigration, emigration rate values. So pixel set selection for the non tumor and tumor segment get improved. Output of Bio-geographic genetic algorithm was cluster representing pixels which segment brain into the portion of the tumor and non tumor. Experiment was done on real dataset of MRI images. Result shows that proposed model has increased the detection accuracy rate by 4.22%, while precision was improved by 2.206%.