Vol.65, No.1, 2020, pp.705-722, doi:10.32604/cmc.2020.09878
Ultrasound Speckle Reduction Based on Histogram Curve Matching and Region Growing
  • Jinrong Hu1, Zhiqin Lei1, Xiaoying Li2, *, Yongqun He3, Jiliu Zhou1
1 Department of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China.
2 Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, 100020, China.
3 Medical School, University of Michigan, Michigan, 48109-0168, USA.
* Corresponding Author: Xiaoying Li. Email: lixiaoying@imicams.ac.cn.
Received 23 January 2020; Accepted 02 June 2020; Issue published 23 July 2020
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.
Ultrasound speckle, histogram matching, speckle reduction, tissue characterization, region growing.
Cite This Article
Hu, J., Lei, Z., Li, X., He, Y., Zhou, J. (2020). Ultrasound Speckle Reduction Based on Histogram Curve Matching and Region Growing. CMC-Computers, Materials & Continua, 65(1), 705–722.
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