Segmentation of Brain Tumor in Multimodal MRI using Histogram Differencing & KNN

Tumor segmentation inside the brain MRI is one of the trickiest and demanding subjects for the research community due to the complex nature and structure of the human brain and the different types of abnormalities that grow inside the brain. A Few common types of tumors are CNS Lymphoma, Meningioma, Glioblastoma, and Metastases. In this research work, our aim is to segment and classify the four most commonly diagnosed types of brain tumors. To segment the four most common brain tumors, we are proposing a new demanding dataset comprising of multimodal MRI along with healthy brain MRI images. The dataset contains 2000 images collected from online sources of about 80 patient cases. Segmentation method proposed in this research is based on histogram differencing with rank filter. Morphology at post-processing is practically implemented to detect the brain tumor more evidently. The KNN classification is applied to classify tumor values into their respective category (i.e. benign and malignant) based on the size value of tumor. The average rate of True Classification Rate (TCR) achieved is 97.3% and False Classification Rate (FCR) is 2.7%.