In this chapter, we present investigations on magnetic resonance imaging (MRI) of various states of brain by extracting the most significant features in order to classify brain images into normal and abnormal. We describe a novel method based on the wavelet transform to initially decompose the images, followed by the use of various feature selection algorithms to extract the most significant brain features from the MRI images. This chapter demonstrates the use of different classifiers to detect abnormal brain images from a publicly available neuroimaging dataset. A wavelet-based feature extraction followed by selection of the most significant features using principal component analysis (PCA)/quadratic discriminant analysis (QDA) with classification using learning-based classifiers results in a significant improvement in accuracy as compared with previously reported studies and to better understanding of brain abnormalities.
|Title of host publication||Springer Handbook of Bio-/Neuro-Informatics|
|Place of Publication||Berlin, Germany|
|Number of pages||9|
|Publication status||Published - 2014|