@Article{cmc.2020.09878,
AUTHOR = {Jinrong Hu, Zhiqin Lei, Xiaoying Li, Yongqun He, Jiliu Zhou},
TITLE = {Ultrasound Speckle Reduction Based on Histogram Curve  Matching and Region Growing},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {1},
PAGES = {705--722},
URL = {http://www.techscience.com/cmc/v65n1/39590},
ISSN = {1546-2226},
ABSTRACT = {The quality of ultrasound scanning images is usually damaged by speckle 
noise. This paper proposes a method based on local statistics extracted from a histogram
to reduce ultrasound speckle through a region growing algorithm. Unlike single statistical 
moment-based speckle reduction algorithms, this method adaptively smooths the speckle 
regions while preserving the margin and tissue structure to achieve high detectability. 
The criterion of a speckle region is defined by the similarity value obtained by matching 
the histogram of the current processing window and the reference window derived from
the speckle region in advance. Then, according to the similarity value and tissue 
characteristics, the entire image is divided into several levels of speckle-content regions,
and adaptive smoothing is performed based on these classification characteristics and the 
corresponding window size determined by the proposed region growing technique. Tests 
conducted from phantoms and in vivo images have shown very promising results after a 
quantitative and qualitative comparison with existing work.},
DOI = {10.32604/cmc.2020.09878}
}