title: SRBF: Speckle reducing bilateral filtering.
abstract: Speckle noise negatively affects medical ultrasound image shape interpretation and boundary detection. Speckle removal filters are widely used to selectively remove speckle noise without destroying important image features to enhance object boundaries. In this article, a fully automatic bilateral filter tailored to ultrasound images is proposed. The edge preservation property is obtained by embedding noise statistics in the filter framework. Consequently, the filter is able to tackle the multiplicative behavior modulating the smoothing strength with respect to local statistics. The in silico experiments clearly showed that the speckle reducing bilateral filter (SRBF) has superior performances to most of the state of the art filtering methods. The filter is tested on 50 in vivo US images and its influence on a segmentation task is quantified. The results using SRBF filtered data sets show a superior performance to using oriented anisotropic diffusion filtered images. This improvement is due to the adaptive support of SRBF and the embedded noise statistics, yielding a more homogeneous smoothing. SRBF results in a fully automatic, fast and flexible algorithm potentially suitable in wide ranges of speckle noise sizes, for different medical applications (IVUS, B-mode, 3-D matrix array US).
authors.affiliation: Computer Vision Center, Bellaterra, Spain. balocco.simone@gmail.com
authors.forename: Simone
authors.initials: S
authors.lastname: Balocco
authors.forename: Carlo
authors.initials: C
authors.lastname: Gatta
authors.forename: Oriol
authors.initials: O
authors.lastname: Pujol
authors.forename: Josepa
authors.initials: J
authors.lastname: Mauri
authors.forename: Petia
authors.initials: P
authors.lastname: Radeva
mesh_terms: D000465:Algorithms; D016477:Artifacts; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D014463:Ultrasonography / Q000379:methods*