Information-theoretic feature detection in ultrasound images.
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.
Slabaugh
Greg
G
Siemens Corporate Res., Princeton, NJ 08540, USA. greg.slabaugh@siemens.com
Unal
Gozde
G
Chang
Ti-Chiun
TC
D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D007257:Information Theory; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014463:Ultrasonography / Q000379:methods*