Ganasala Padma, Kumar Vinod
Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India, 247667,
J Digit Imaging. 2014 Jun;27(3):407-18. doi: 10.1007/s10278-013-9664-x.
Fusion of CT and MR images allows simultaneous visualization of details of bony anatomy provided by CT image and details of soft tissue anatomy provided by MR image. This helps the radiologist for the precise diagnosis of disease and for more effective interventional treatment procedures. This paper aims at designing an effective CT and MR image fusion method. In the proposed method, first source images are decomposed by using nonsubsampled contourlet transform (NSCT) which is a shift-invariant, multiresolution and multidirection image decomposition transform. Maximum entropy of square of the coefficients with in a local window is used for low-frequency sub-band coefficient selection. Maximum weighted sum-modified Laplacian is used for high-frequency sub-bands coefficient selection. Finally fused image is obtained through inverse NSCT. CT and MR images of different cases have been used to test the proposed method and results are compared with those of the other conventional image fusion methods. Both visual analysis and quantitative evaluation of experimental results shows the superiority of proposed method as compared to other methods.
CT图像与MR图像的融合能够同时呈现CT图像所提供的骨解剖细节以及MR图像所提供的软组织解剖细节。这有助于放射科医生对疾病进行精确诊断，并实施更有效的介入治疗程序。本文旨在设计一种有效的CT与MR图像融合方法。在所提出的方法中，首先利用非下采样轮廓波变换（NSCT）对源图像进行分解，NSCT是一种平移不变、多分辨率且多方向的图像分解变换。局部窗口内系数平方的最大熵用于低频子带系数选择。最大加权和修正拉普拉斯用于高频子带系数选择。最后通过逆NSCT获得融合图像。使用不同病例的CT和MR图像对所提出的方法进行测试，并将结果与其他传统图像融合方法的结果进行比较。实验结果的视觉分析和定量评估均表明，与其他方法相比，所提出的方法具有优越性。
