Abstract:
Diagnosis of brain tumor is very complicated and critical process usually
due to the precautions of the invasive operation on this area.
This is analytical study aimed to classify the brain tumors using images
textural analysis the result of this study presents a highly efficient method to
identify, diagnosis and classify the intracranial tumors. The study had been
worked up on one hundred and nine patients have different types of brain
tumors, it done in Khartoum and Gazera state using different types of CT
machines including GE and Neusoft scanner.
This study consists of two methodology; first order statistical analysis and
higher order statistical analysis for images classification.
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.
