Support vector machine (SVM) classifier for brain tumor detection

Abstract

One of the primary diagnostic and treatment evaluation tools for brain tumors has been magnetic resonance (MR) imaging. MR imaging has become a widely-used method of high quality medical imaging, especially in brain imaging where MR's soft tissue contrast and non-invasiveness are clear advantages. MR images can also be used to track the size of a brain tumor as it responds (or doesn't) to treatment. A reliable method for segmenting tumor would clearly be a useful tool. Currently, however, there is no method widely accepted in clinical practice for quantitating tumor volumes from MR images. Tese manual measurements, have shown poor reproducibility and tumor response criteria based on these manual estimations have shown poor correlation with quantitative 2D and 3D metrics. Computer-based brain tumor segmentation has remained largely experimental work, with approaches including multi-spectral analysis, edge detection, neural networks, and knowledge-based techniques. We propose a real time design for brain tumor detection that a combination of knowledge-based techniques and multi-spectral analysis based on Support Vector Machines. The results shows that the system could effectively detect pathology and label normal transaxial slices.