Image classification of brain MRI using support vector machine

One of the primary diagnostic and treatment evaluation tools for brain interpretation has been magnetic resonance imaging (MRI). It has been 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 determine a normal and abnormal types of brain. Moreover, the MRI characteristics will help the doctor to avoid the human error in manual interpretation of medical content. Computer-based classification has remained largely experimental work with approaches, one of them is, Support vector machine (SVM). SVM is a pattern recognition algorithm which learns to assign labels to objects through examples. This research paper is an attempt to use SVM to automatically classify brain MRI images under two categories, either normal or abnormal brain which refers to brain tumor. The determination of normal and abnormal brain image is based on symmetry which is exhibited in the axial and coronal images. Using feature vector gained from the MRI images, SVM classifiers are use to classify the images. The process consists of two components which are training phase and a testing phase. Percentage of accuracy on each parameter in SVM will give the idea to choose the best one to be used in further works. Other than that, value of percentage will give the first interpretation either the brain image has the possibility of brain tumor or normal. After all, we are using LabView Advanced Signal Processing Toolkit as the software in our experimental work. We believe with the easiness of this graphical programming and the capabilities of SVM will give a very good result.