| dc.description.abstract | Brain tumor or cancer is one of the most dangerous types of cancer because it affects the main nervous system of the human body, and the detection of brain tumors is a complicated and sensitive task that implied the experience of the classifier.
This thesis has suggested method of "Brain tumor detection by multi focus image fusion based on wavelet transform" .It combined Magnetic Resonance Imaging( MRI) and Computed Tomography (CT) image in order to enhance the tumor detection. The reason to incorporate multi -focus image is to help clinicians obtain support in diagnosing. The algorithm based on seven wavelets has been implemented, bior2.2, coif2, db2, dmey, rbio2.2, sym4 and haar respectively to get a variety of results. This algorithm is effectively used the information provided by the CT image and MRI images to obtain a resultant fused image which increases the efficiency of tumor detection by using MATLAB. The effectiveness of the algorithm was evaluated by changing the wavelet fusion parameters such as the number of decompositions and image quality; The scale that was used to measure its image quality is to calculate the signal-to-noise ratio (PSNR) and the calculation of the factor of randomness (Factor Entropy). It has been observed that the haar waves give the best results with the calculation of the signal to noise ratio (PSNR) and the dmay waves give the best results with the calculation of the factor of randomness (Factor Entropy) to detect the tumor. In the image segmentation has been applied to discover the tumor portion and indicate the tumor growth area. Finally results will be presented and discussed . | en_US |
