Automatic Detection and Segmentation of Brain Tumor Using Random Forest Approach

Abstract

Early detection is the key of success in the treatment of tumors. Establishing methods that can identify the presence and position of tumors in their early stage is a current great challenge in medical imaging. This study proposes a machine learning solution based on binary decision trees and random forest technique, aiming at the detection and accurate segmentation of brain tumors from multispectral volumetric MRI records. The training and testing of the proposed method uses twelve selected volumes from the BRATS 2012/13 database. Image volumes were preprocessed to extend the feature set with local information of each voxel. Intending to enhance the segmentation accuracy, each detected tumor pixel is validated or discarded according to a criterion based on neighborhood information. A detailed preliminary investigation is carried out in order to identify and enhance the capabilities of random forests trained with information originating from single image records. The achieved accuracy is generally characterized by a Dice score up to 0.9. Recommendation are formulated for the future development of a complex, random forest based tumor detection and segmentation system.