An efficient combined approach for medical brain tumour segmentation
Brain tumour is known to be the major setback which counts as the major fatality cause for human beings. Proper and timely diagnosis can prevent the life of a person to an extent. Segmentation is one of the important techniques to find the location and size of tumour in the brain. Automatic tumour detection in MRI is very important in today's world health scenario which boosts up the required medical application. The major road block in MR Images is lucidity and sensitivity for a better vivid image. In recent years, technology has opened a wide range of options to understand and improve segmentation of brain tumour. The diagnosis method consists of three stages: preprocessing of Magnetic Resonance images, feature extraction and segmentation. The methods like thresholding, k means and others whose limitations are being curbed into a more efficient and accurate measure of location and size of the tumour. Brain tumour is detected by the new proposed algorithm known as Spatial Fuzzy C Mean Clustering (SFCM) The proposed hybrid technique highlights the advancement and reliability of front end image segmentation and detection.
2017 International Conference on Communication and Signal Processing (ICCSP)