Detection and characterization of brain tumor using segmentation based on HSOM, wavelet packet feature spaces and ANN

Abstract

The method to segment a human brain of MRI image is proposed in which tumor detection and characterization are considered using HSOM and Wavelet packets feature spaces. In the first phase, the MRI brain image is acquired from patient's database, In that film artifact and noise are removed, and Hierarchical Self Organizing Map (HSOM) is applied for image segmentation. The HSOM is the extension of the conventional self-organizing map used to classify the image row by row. In this lowest level of weight vector, a high value of tumor pixels and computation speed is achieved by the HSOM with vector quantization. In the second phase, the feature of the MRI image is extracted first. Using the ANN and wavelet packets we determine the abnormal spectra and type of abnormality. The MRI analysis results were correct 97% of the time when classifying the spectra of the clinical MRI data into normal tissue, tumor, and radiation necrosis. They were correct 72% and 83% of the time when determined tumor types using the clinical and simulated MRI data, respectively.