Extraction of Parasagittal Meningioma Tumor using Skull Stripping Method and Multilevel Thresholding

Tumors are abnormal growth of tissues in the body, which can be benign or malignant. Meningioma is a type of tumor in the Brain that emerges from the layer of tissue known as meninges and surrounds the brain and the spine inside the skull. Parasagittal meningioma's appears along the superior sagittal sinus and are attached to the dura that run between the left and the right sides of the brain. Growth rates for parasagittal meningioma are slow and might take years before it gets detected. Due to its location and the possibility of injuring the sinus, surgery of parasagittal meningioma tumors are complicated and require early detection. In this paper we propose a simple algorithm to extract parasagittal meningioma tumor from magnetic resonance imaging and computed tomography scanned images using skull stripping and multilevel thresholding method. The use of machine learning techniques in diagnosing or analyzing biomedical issues are trending and rapidly growing due its ease, accuracy, cost effectiveness and less time requirement in detecting a disease from MRI or CT scanned images as well as other form of images gained from medical imaging technologies. The algorithm has been tested on 150 DICOM images and the result showed the accuracy of the algorithm and its efficiency in correctly detecting parasagittal meningioma tumors in the brain.