Automatic Segmentation of Brain Tumour from Multiple Images of Brain MRI

Computational applications are gaining significant importance in the day-to-day life. Specifically, the usage of the computeraided systems for computational biomedical applications has been explored to a higher extent. Detection of Brain tumour is the most common fatality in the current scenario of health care society. Automated brain disorder diagnosis with MR images is one of the specific medical image analysis methodologies. Image segmentation is used to extract the abnormal tumour portion in brain. The fusion of information is a domain of research in full effervescence these last years. Because of increasing diversity in the techniques of images acquisitions, the applications of medical images segmentation, in which we are interested, necessitate most of the time to carry out the fusion of various data sources to have information with high quality. In this paper we propose a system of image registration and data fusion theory adapted for the segmentation of MR images. This system provides an efficient and fast way for diagnosis of the brain tumour. Proposed system consists of multiple phases. First phase consists of registration of multiple MR images of the brain taken along adjacent layers of brain. In the second phase, these registered images are fused to produce high quality image for the segmentation. Finally, segmentation is done by improved K means algorithm with dual localization methodology. Applications on a brain model shows very promising results on simulated data and a great concordance between the true segmentation and the proposed system.