title: Ultrasound speckle reduction based on fractional order differentiation.
abstract: OBJECTIVE
  Ultrasound images show a granular pattern of noise known as speckle that diminishes their quality and results in difficulties in diagnosis. To preserve edges and features, this paper proposes a fractional differentiation-based image operator to reduce speckle in ultrasound.
  
  
  METHODS
  An image de-noising model based on fractional partial differential equations with balance relation between k (gradient modulus threshold that controls the conduction) and v (the order of fractional differentiation) was constructed by the effective combination of fractional calculus theory and a partial differential equation, and the numerical algorithm of it was achieved using a fractional differential mask operator.
  
  
  RESULTS
  The proposed algorithm has better speckle reduction and structure preservation than the three existing methods [P-M model, the speckle reducing anisotropic diffusion (SRAD) technique, and the detail preserving anisotropic diffusion (DPAD) technique]. And it is significantly faster than bilateral filtering (BF) in producing virtually the same experimental results.
  
  
  CONCLUSIONS
  Ultrasound phantom testing and in vivo imaging show that the proposed method can improve the quality of an ultrasound image in terms of tissue SNR, CNR, and FOM values.
authors.affiliation: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China.
authors.forename: Dangguo
authors.initials: D
authors.lastname: Shao
authors.affiliation: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China.
authors.forename: Ting
authors.initials: T
authors.lastname: Zhou
authors.affiliation: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China.
authors.forename: Fan
authors.initials: F
authors.lastname: Liu
authors.affiliation: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China.
authors.forename: Sanli
authors.initials: S
authors.lastname: Yi
authors.affiliation: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Xiang
authors.affiliation: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China. 23014260@qq.com.
authors.forename: Lei
authors.initials: L
authors.lastname: Ma
authors.affiliation: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Xiong
authors.affiliation: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China.
authors.forename: Jianfeng
authors.initials: J
authors.lastname: He
mesh_terms: D000465:Algorithms; D006801:Humans; D007668:Kidney / Q000000981:diagnostic imaging; D008099:Liver / Q000000981:diagnostic imaging; D019047:Phantoms, Imaging; D014463:Ultrasonography / Q000379:methods*
keywords: Anisotropic diffusion; Fractional order differentiation; Speckle reduction; Ultrasound image