Ultrasound images are affected by the speckle phenomenon, a multiplicative noise that degrades image quality. Several methods for denoising have been proposed in recent years, based on different approaches. The so-called non-local mean is considered the state-of-the-art method; the idea is to find similar patches across the image and exploit them to regularize the image. The method proposed here is in the non-local family, although instead of partitioning the target image in patches, it works pixelwise. The similarity between pixels is evaluated by analyzing their statistical behavior, in particular, by measuring the Kolmogorov-Smirnov distance between their distributions. To make this possible, a stack of acquired images is required. The proposed method has been tested on both simulated and real data sets and compared with other widely adopted techniques. Performance is interesting, with quality parameters and visual inspection confirming such findings.
|Titolo:||Ultrasound Image Despeckling Based on Statistical Similarity|
|Autori interni:||BASELICE, FABIO|
|Data di pubblicazione:||2017|
|Rivista:||ULTRASOUND IN MEDICINE AND BIOLOGY|
|Appare nelle tipologie:||1.1 Articolo in rivista|