Automatic Tissue Classification for High-resolution Breast CT Images Based on Bilateral Filtering
Yang X, Sechopoulos I, Fei B. Automatic Tissue Classification for High-resolution Breast CT Images Based on Bilateral Filtering. Proc SPIE Int Soc Opt Eng. 2011 Mar 14;7962:79623H. doi: 10.1117/12.877881. PMID: 24027608; PMCID: PMC3766982.
Abstract
Breast tissue classification can provide quantitative measurements of breast composition, density and tissue distribution for diagnosis and identification of high-risk patients. In this study, we present an automatic classification method to classify high-resolution dedicated breast CT images. The breast is classified into skin, fat and glandular tissue. First, we use a multiscale bilateral filter to reduce noise and at the same time keep edges on the images. As skin and glandular tissue have similar CT values in breast CT images, we use morphologic operations to get the mask of the skin based on information of its position.