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An information-theoretic detector based scheme for registration of speckled medical images

Wang, Z., Slabaugh, G. G., Unal, G. B. , Zhou, M. & Fang, T. (2007). An information-theoretic detector based scheme for registration of speckled medical images. In: SMC. IEEE International Conference on Systems, Man and Cybernetics, 2007, 07-10-2007 - 10-10-2007, Montreal, Canada. doi: 10.1109/ICSMC.2007.4413864


Several studies dealt with medical ultrasound registration. Their similarity metrics relied on pixel-to-pixel intensity comparisons. Hence, they are not well suited to the case of speckled images. To better handle the speckle noise, our previous work proposed an information-theoretic feature detector-based registration approach. This work aims to extend it to the cases where the image speckle model is Rayleigh or normalized Fisher-Tippett distributed. Using speckle modeling based on these distributions, a speckle-specific information- theoretic feature detector is constructed and applied to provide feature images. Those feature images are then registered using differential equations, the solution of which provides a transformation to bring the images into alignment. Compared to standard gradient-based techniques, the experimental results demonstrate the effectiveness of our method, particularly for low contrast ultrasound images.

Publication Type: Conference or Workshop Item (Paper)
Additional Information: © 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords: Speckle Image, Image registration, information theory, biomedical image processing
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine
Departments: School of Science & Technology > Computer Science
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