Brain tumour classification and abnormality detection using neuro-fuzzy technique and Otsu thresholding.
Brain tumour is one of the main causes for an increase in transience among children and adults. This paper proposes an improved method based on Magnetic Resonance Imaging (MRI) brain image classification and image segmentation approach. Automated classification is encouraged by the need of high accuracy when dealing with a human life. The detection of the brain tumour is a challenging problem, due to high diversity in tumour appearance and ambiguous tumour boundaries. MRI images are chosen for detection of brain tumours, as they are used in soft tissue determinations. First of all, image pre-processing is used to enhance the image quality. Second, dual-tree complex wavelet transform multi-scale decomposition is used to analyse texture of an image. Feature extraction extracts features from an image using gray-level co-occurrence matrix (GLCM). Then, the Neuro-Fuzzy technique is used to classify the stages of brain tumour as benign, malignant or normal based on texture features. Finally, tumour location is detected using Otsu thresholding. The classifier performance is evaluated based on classification accuracies. The simulated results show that the proposed classifier provides better accuracy than previous method.
['Renjith|Arokia|A|', 'Manjula|P|P|', 'Mohan Kumar|P|P|']
D000465:Algorithms D001921:Brain D001932:Brain Neoplasms D017143:Fuzzy Logic D006801:Humans D007090:Image Interpretation, Computer-Assisted D008279:Magnetic Resonance Imaging D058067:Wavelet Analysis
Neuro-fuzzy Otsu thresholding MRI Image classification Image segmentation Gray-level co-occurrence matrix Dual tree complex wavelet transform