  Feature extraction is a method of capturing visual content of an image. The
feature extraction is the process to represent raw image in its reduced form to
facilitate decision making such as pattern classification. We have tried to
address the problem of classification MRI brain images by creating a robust and
more accurate classifier which can act as an expert assistant to medical
practitioners. The objective of this paper is to present a novel method of
feature selection and extraction. This approach combines the Intensity,
Texture, shape based features and classifies the tumor as white matter, Gray
matter, CSF, abnormal and normal area. The experiment is performed on 140 tumor
contained brain MR images from the Internet Brain Segmentation Repository. The
proposed technique has been carried out over a larger database as compare to
any previous work and is more robust and effective. PCA and Linear Discriminant
Analysis (LDA) were applied on the training sets. The Support Vector Machine
(SVM) classifier served as a comparison of nonlinear techniques Vs linear ones.
PCA and LDA methods are used to reduce the number of features used. The feature
selection using the proposed technique is more beneficial as it analyses the
data according to grouping class variable and gives reduced feature set with
high classification accuracy.
