Classification of Brain Tumor Using Discrete Wavelet Transform, Principal Component Analysis and Probabilistic Neural Network

The project proposes an automatic support system fo r stage classification using artificial neural netw ork (learning machine) and to detect Brain Tumor through k-means clustering methods for medical imaging application. The detection o f the Brain Tumor is a challenging problem, due to the structure of the Tu mor cells. This project presents a segmentation met hod, k-means clustering algorithm, for segmenting Magnetic Resonance images to detect the Brain Tumor in its early stages and to analyze anatomical structures. The artificial neural network will be u sed to classify the stage of Brain Tumor that is be nign, malignant or normal. The segmentation results will be used as a base for a C omputer Aided Diagnosis (CAD) system for early detection of Brain Tumor which will improves the chances of survival for the patient. This paper describes an efficient method for automatic brain tumor segmentation for the extraction of tumor tissues fr om MR images. In this method segmentation is carried out using K-means clustering algorithm for better performance. A well -known segmentation problem within MRI is the task of labeling the tissue type which include White Matter (WM), Grey Matter (GM), Cerebrospinal Fluid (CSF) and sometimes pathological tissues like tumor etc. Probabilistic Neural Network with radial basis function will be employed to implement an au tomated Brain Tumor classification. Decision making was performed in tw o stages: feature extraction using GLCM and PCA and the classification using PNN-RBF network. The performance of this classifier was evaluated in terms of training performan ce and classification accuracies. The simulated results shown that classi fier and segmentation algorithm provides better acc uracy than previous method.