Model for Enhancement and Segmentation of Magnetic Resonance Images for Brain Tumor Classification

Many a times, the quality of Magnetic Resonance Imaging (MRI) based images get affected by the machines that are utilized to compute them. Hence, in several cases, it is necessary to process each image in order to obtain a corresponding better image so that the patient can be treated with ease and accuracy. According to traditional methods, this objective can be achieved by contrast enhancement or histogram stretching, etc. This paper give a naive approach that can enhance the MRI image, classify the brain tumor type and segment the tumor region for better analysis. For the enhancement, the concept of Discrete Wavelet is used in which an image is decomposed in four sub-bands and each sub-band is processed separately after which Inverse Discrete Wavelet Transform (IDWT) is applied in order to acquire a better quality image. Convolutional Neural Network (CNN) is used to classify the type of tumor present in MRI image. Lastly, after the tumor is detected, it is segmented by using a very efficient method known as K-means Clustering.