During the last few decades, the field of medical image processing has been closely related to neural network methodologies and their applications, especially after the vast successful researches in this field and the advanced newly formulated algorithms as well as the need for accuracy and intelligence in assessing of the medical images in critical diagnosis and oncology care cases. One of these methodologies which is implemented in this work is the Blind Source Separation (BSS) and its application the Independent Component Analysis (ICA) that eases the process of separation of the Region of Interest (ROI) and the fusion of multimodality images to emphasize the benefits of the them and get an image of accurate anatomic structure with the physiological details. In this thesis a 512×512 Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) images are registered to eliminate the dimensionality differences between the two images, then separated both of them using batch approach by fast-fixed point algorithm after truncation of each image in to almost 1000 image patches of 15×15 dimension and transform them to 1-D and order them into row-wise fashion as well as reducing the entered data of lesser interest by Principle component analysis (PCA) in order to minimize the computational complexity, finally applying the fusion process using different methods. The images were taken from Al Hussain General Hospital, Karbala' and the result shows that the differently defined brain regions can be separated using batch approach for both CT and MRI and could be a powerful and accurate diagnostic tool, especially, for surgical and radiotherapy planning and oncology treatment after a suitable fusion process is carried out on it.