RESUMEN
This study aims to develop a semi-automatic system for brain tumor segmentation in 3D MR images. For a given image, noise was corrected using SUSAN algorithm first. A specific region of interest (ROI) that contains tumor was identified and then the intensity non-uniformity in ROI was corrected via the histogram normalization and intensity scaling. Each voxel in ROI was presented using 22 features and then was categorized as tumor or non-tumor by a multiple-classifier system. T1- and T2-weighted images and fluid-attenuated inversion recovery (FLAIR) were examined. The system performance in terms of Dice index (DI), sensitivity (SE) and specificity (SP) was evaluated using 150 simulated and 30 real images from the BraTS 2012 database. The results showed that the presented system with an average DI > 0.85, SE > 0.90, and SP > 0.98 for simulated data and DI > 0.80, SE > 0.84, and SP > 0.98 for real data might be used for accurate extraction of the brain tumors. Moreover, this system is 6 times faster than a similar system that processes the whole image. In comparison with two state-of-the-art tumor segmentation methods, our system improved DI (e.g., by 0.31 for low-grade tumors) and outperformed these algorithms. Considering the costs of imaging procedures, tumor identification accuracy and computation times, the proposed system that augmented general pathological information about tumors and used only 4 features of FLAIR images can be suggested as a brain tumor segmentation system for clinical applications.
