Amini, N., Fatemizadeh, E., Behnam, H. (2014). MRI and PET Image Fusion by Using Curvelet Transform. Journal of Advances in Computer Research, 5(4), 23-30.
Nasrin Amini; Emad Fatemizadeh; Hamid Behnam. "MRI and PET Image Fusion by Using Curvelet Transform". Journal of Advances in Computer Research, 5, 4, 2014, 23-30.
Amini, N., Fatemizadeh, E., Behnam, H. (2014). 'MRI and PET Image Fusion by Using Curvelet Transform', Journal of Advances in Computer Research, 5(4), pp. 23-30.
Amini, N., Fatemizadeh, E., Behnam, H. MRI and PET Image Fusion by Using Curvelet Transform. Journal of Advances in Computer Research, 2014; 5(4): 23-30.
MRI and PET Image Fusion by Using Curvelet Transform
1Biomedical engineering department, Science and Research branch, Islamic Azad University, Tehran, Iran
2Biomedical Signal and Image Processing Lab (BiSIPL), Dept. Electrical EngineeringSharif University of TechnologyTehran, Iran
3Bio-Medical Engineering group, Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
Abstract
The fusion of medical images is very useful for clinical application. Generally, the PET image indicates the function of tissue and the MRI image shows the anatomy of tissue. In this article we fused MRI and PET images and the purpose is adding structural information from MRI to PET image. The images decomposed with Curvelet Transform, and then two images fused with applying fusion rules. We used MATLAB software for fused images and evaluated the result. The data set consists 34 images of color PET images and high resolution MRI images. The brain images are classified into two groups, normal (Coronal, Sagittal and Transaxial) and Alzeimer’s disease dataset images. Finally we used visual and quantitative criteria to evaluate the fusion result. In quantitative evaluation we used entropy, discrepancy and overall performance. Results show the amount of entropy, achieved by the proposed method, was the highest and amount of discrepancy and overall performance was the lowest. The small amount of discrepancy, overall performance and high amount of entropy means high quality.
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