title: Multiresolution edge detection using enhanced fuzzy c-means clustering for ultrasound image speckle reduction.
abstract: OBJECTIVE
  Speckle suppression in ultrasound (US) images of various anatomic structures via a novel speckle noise reduction algorithm.
  
  
  METHODS
  The proposed algorithm employs an enhanced fuzzy c-means (EFCM) clustering and multiresolution wavelet analysis to distinguish edges from speckle noise in US images. The edge detection procedure involves a coarse-to-fine strategy with spatial and interscale constraints so as to classify wavelet local maxima distribution at different frequency bands. As an outcome, an edge map across scales is derived whereas the wavelet coefficients that correspond to speckle are suppressed in the inverse wavelet transform acquiring the denoised US image.
  
  
  RESULTS
  A total of 34 thyroid, liver, and breast US examinations were performed on a Logiq 9 US system. Each of these images was subjected to the proposed EFCM algorithm and, for comparison, to commercial speckle reduction imaging (SRI) software and another well-known denoising approach, Pizurica's method. The quantification of the speckle suppression performance in the selected set of US images was carried out via Speckle Suppression Index (SSI) with results of 0.61, 0.71, and 0.73 for EFCM, SRI, and Pizurica's methods, respectively. Peak signal-to-noise ratios of 35.12, 33.95, and 29.78 and edge preservation indices of 0.94, 0.93, and 0.86 were found for the EFCM, SIR, and Pizurica's method, respectively, demonstrating that the proposed method achieves superior speckle reduction performance and edge preservation properties. Based on two independent radiologists' qualitative evaluation the proposed method significantly improved image characteristics over standard baseline B mode images, and those processed with the Pizurica's method. Furthermore, it yielded results similar to those for SRI for breast and thyroid images significantly better results than SRI for liver imaging, thus improving diagnostic accuracy in both superficial and in-depth structures.
  
  
  CONCLUSIONS
  A new wavelet-based EFCM clustering model was introduced toward noise reduction and detail preservation. The proposed method improves the overall US image quality, which in turn could affect the decision-making on whether additional imaging and/or intervention is needed.
authors.affiliation: Department of Medical Physics, School of Medicine, University of Patras, Rion, GR 26504, Greece.
authors.forename: Stavros
authors.initials: S
authors.lastname: Tsantis
authors.affiliation: Department of Radiology, School of Medicine, University of Patras, Rion, GR 26504, Greece.
authors.forename: Stavros
authors.initials: S
authors.lastname: Spiliopoulos
authors.affiliation: Department of Energy Technology Engineering, Technological Education Institute of Athens, Athens 12210, Greece.
authors.forename: Aikaterini
authors.initials: A
authors.lastname: Skouroliakou
authors.affiliation: Department of Radiology, School of Medicine, University of Patras, Rion, GR 26504, Greece.
authors.forename: Dimitrios
authors.initials: D
authors.lastname: Karnabatidis
authors.affiliation: Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030.
authors.forename: John D
authors.initials: JD
authors.lastname: Hazle
authors.affiliation: Department of Medical Physics, School of Medicine, University of Patras, Rion, GR 26504, Greece and Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030.
authors.forename: George C
authors.initials: GC
authors.lastname: Kagadis
mesh_terms: D000465:Algorithms; D016477:Artifacts; D016000:Cluster Analysis; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D008099:Liver / Q000000981:diagnostic imaging; D059629:Signal-To-Noise Ratio; D012984:Software; D013961:Thyroid Gland / Q000000981:diagnostic imaging; D014463:Ultrasonography / Q000379:methods*; D016217:Ultrasonography, Mammary / Q000379:methods; D058067:Wavelet Analysis