Information-Theoretic Feature Detection in Ultrasound Images

Greg Slabaugh, Gozde Unal, Ti-Chiun Chang
2006 IEEE Engineering in Medicine and Biology Society. Conference Proceedings  
This is the accepted version of the paper. This version of the publication may differ from the final published version. Permanent repository link: Link to published version: http://dx. Abstract The detection of image features is an essential component of medical image processing, and has wideranging applications including adaptive filtering, segmentation, and registration. In this paper, we present an information-theoretic approach to feature detection in
more » ... sound 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.
doi:10.1109/iembs.2006.4397988 fatcat:vhetlwbtjnearbwodamnadbx2y