A Clustering Based Approach For Meningioma Tumors Extraction From Brain MRI Images

Recent era witness a high mortality rate from brain cancer. Effective and early diagnosis using computer vision approach can be utilized for further therapeutics. The broad classifications of brain-related tumors identified so far are Meningiomas, Gliomas and Pituitary tumors. In this paper, an unsupervised computer vision approach is presented for effective extraction of Meningiomas tumors from Tl-weighted MRI images. The proposed approach utilizes a simple k-means clustering with thresholding approach followed by a set of morphological approaches in effective extraction of the tumor mass from the MRI images. The proposed approach is tested on a widely available benchmark dataset and compared with various state of art recent computer vision approaches using different performance measures for validation. The results show that the proposed approach archives 99.47% accuracy for the dataset indicating its superiority compared to others.