title: Multiresolution generalized N dimension PCA for ultrasound image denoising.
abstract: BACKGROUND
  Ultrasound images are usually affected by speckle noise, which is a type of random multiplicative noise. Thus, reducing speckle and improving image visual quality are vital to obtaining better diagnosis.
  
  
  METHODS
  In this paper, a novel noise reduction method for medical ultrasound images, called multiresolution generalized N dimension PCA (MR-GND-PCA), is presented. In this method, the Gaussian pyramid and multiscale image stacks on each level are built first. GND-PCA as a multilinear subspace learning method is used for denoising. Each level is combined to achieve the final denoised image based on Laplacian pyramids.
  
  
  RESULTS
  The proposed method is tested with synthetically speckled and real ultrasound images, and quality evaluation metrics, including MSE, SNR and PSNR, are used to evaluate its performance.
  
  
  CONCLUSIONS
  Experimental results show that the proposed method achieved the lowest noise interference and improved image quality by reducing noise and preserving the structure. Our method is also robust for the image with a much higher level of speckle noise. For clinical images, the results show that MR-GND-PCA can reduce speckle and preserve resolvable details.
authors.forename: Danni
authors.initials: D
authors.lastname: Ai
authors.affiliation: Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing 100081, China. jyang@bit.edu.cn.
authors.forename: Jian
authors.initials: J
authors.lastname: Yang
authors.forename: Yang
authors.initials: Y
authors.lastname: Chen
authors.forename: Weijian
authors.initials: W
authors.lastname: Cong
authors.forename: Jingfan
authors.initials: J
authors.lastname: Fan
authors.forename: Yongtian
authors.initials: Y
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D016477:Artifacts; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008962:Models, Theoretical; D025341:Principal Component Analysis / Q000379:methods*; D014465:Ultrasonics
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