Automated Brain Tumor Segmentation Using Hsom
P Santha, C. N. Marimuthu · 2013
In this project a forceful segmentation tool for the detection of brain tumour is used to assist clinician and researchers in radio surgery applications. A clustering based approach using hierarchal self- organizing map algorithm is proposed for MR image segmentation. Hierarchal self-organizing map (HSOM) is a dynamically growing neural network model evolves into a hierarchical structure according to the requirements of the input data during an unsupervised training process. This algorithm proved to be exceptionally successful for data visualization applications. HSOM is the extension of the conventional self organizing map which is used to classify the image row by row. This project describe segmentation method consists of two phases. In the first phase, the MRI brain image is acquired from patient database. In that film artifact and noise are removed. In the second phase (MR) image segmentation is to accurately identify the principal tissue structures in these image volumes. Finally the numbers of affected cells are counted using the row and column wise scanning method. Keywords - HSOM, Image analysis, Magnetic Resonance Imaging (MRI) Segmentation, Tumor detection.