Knowledge abstraction from textural features of brain MRI images for diagnosing brain tumor using statistical techniques and associative classification

This paper presents a methodology for finding the association rules using associative classification which can be used to abstract knowledge from brain MRI images. Reducing the size of images using different thresholds help to reduce the complexity of the proposed system without affecting the correctness of these images. Textural features are taken into consideration because when there is a wide variation of features of discrete gray tone, the texture dominates more. Gray-Tone Spatial-Dependence matrices are calculated from images in which textural information is contained. The system uses a supervised learning approach for selecting the important features from different textural features. Using associative classification, the rules are generated from selected textural features which abstract the knowledge from the images.