Comparison between the First Order Statistical Analysis and Higher Order Statistical Analysis on Characterization of Brain Tumor on CT Images

This study had been worked up on hundred and nine CT images of different types of brain tumor. For the first order statistical analysis the procedure contain dual phase, phase one contain textural feature extraction from the selected images (features included mean, skewness, energy and entropy), phase two classification of images component according to their extracted textural features using linear discriminant analysis, the results showed that the proposed method have achieved high accuracy in recognizing the intracranial tumor from the normal surrounding tissues. The accuracy of classification was 90.4 %. For the higher order statistical analysis nine features include, long run emphasis, low gray-level run emphasis, high gray-Level, Run emphasis, short run low gray-level emphasis, short run high gray-Level emphasis, long run low gray-level emphasis, long run high gray-level emphasis, gray-level non-uniformity, run length non-uniformity, run percentage had been used for image classification. The analyzed data showed accuracy percentage beyond to 94 %. That exploring the priority of higher order statistical analysis method upon the first order on classification of brain tumor when the classification process based on the tumor textural feature.