Automatic brain MRI slices classification using hybrid technique

Abstract EN

This paper presents an intelligent classification technique to identify normal and abnormal slices of the magnetic resonance human brain images (MRI).

The prtoposed hybrid technique consists of four subsequent stages ; namely, dimensionality reduction, preprocessing, feature extraction, and classification.

In the initial stages, the enhancement and removed unwanted information are applied to provide a more appropriate image for the subsequent automated stages.

In feature extraction stage, the most efficient features like statistical, and Haar wavelet features are extracted from each slice of brain MR images.

In the classification stage, initially performs classification process by utilizing Fuzzy Inference System (FIS) and secondly Feed Forward Neural Network (FFNN) is used to classify the brain tissue to normal or abnormal.

The proposed automated system is tested on a data set of 572 MRI images using T1 horizontal transverse (axial) section of the brain.

Hybrid method yields high sensitivity of 100 %, specificity of 100 % and overall accuracy of 95.66 % over FIS and FFNN.

The classification result shows that the proposed hybrid techniques are robust and effective compared with other recently work.