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Information-theoretic feature detection in ultrasound images

Slabaugh, G. G., Unal, G. B. & Chang, T. C. (2006). Information-theoretic feature detection in ultrasound images. In: 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2006 (EMBS '06). (pp. 2638-2642). IEEE. doi: 10.1109/IEMBS.2006.260254


The detection of image features is an essential component of medical image processing, and has wide-ranging applications including adaptive filtering, segmentation, and registration. In this paper, we present an information-theoretic approach to feature detection in ultrasound images. Ultrasound images are corrupted by speckle noise, which is a disruptive random pattern that obscures the features of interest. Using theoretical probability density functions of the speckle intensity distributions, we derive analytic expressions that measure the distance between distributions taken from different regions in an ultrasound image and use these distances to detect features. We compare the technique to classic gradient-based feature detection methods.

Publication Type: Book Section
Additional Information: © 2006 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.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Science & Technology > Computer Science
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