title: Complex wavelet structural similarity: a new image similarity index.
abstract: We introduce a new measure of image similarity called the complex wavelet structural similarity (CW-SSIM) index and show its applicability as a general purpose image similarity index. The key idea behind CW-SSIM is that certain image distortions lead to consistent phase changes in the local wavelet coefficients, and that a consistent phase shift of the coefficients does not change the structural content of the image. By conducting four case studies, we have demonstrated the superiority of the CW-SSIM index against other indices (e.g., Dice, Hausdorff distance) commonly used for assessing the similarity of a given pair of images. In addition, we show that the CW-SSIM index has a number of advantages. It is robust to small rotations and translations. It provides useful comparisons even without a preprocessing image registration step, which is essential for other indices. Moreover, it is computationally less expensive.
authors: Advanced Imaging in Multiple Sclerosis Laboratory, Department of Neurology, University of California San Francisco, San Francisco, CA 94107, USA. mehul.sampat@ieee.org
authors: Mehul P
authors: MP
authors: Sampat
authors: Zhou
authors: Z
authors: Wang
authors: Shalini
authors: S
authors: Gupta
authors: Alan Conrad
authors: AC
authors: Bovik
authors: Mia K
authors: MK
authors: Markey
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D005145:Face / Q000033:anatomy & histology; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D009369:Neoplasms / Q000000981:diagnostic imaging; D010363:Pattern Recognition, Automated / Q000379:methods*; D012372:ROC Curve; D011859:Radiography; D012815:Signal Processing, Computer-Assisted