Open AccessDespeckling of ultrasound images using directionally decimated wavelet packets with adaptive clustering
Author(s) -
Chinnathambi Vimalraj,
Sankaralingam Esakkirajan,
Thangaraj Veerakumar,
Padma Sreevidya
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5011
Subject(s) - speckle noise , artificial intelligence , speckle pattern , wavelet packet decomposition , pattern recognition (psychology) , computer science , wavelet , thresholding , peak signal to noise ratio , wavelet transform , filter (signal processing) , computer vision , discrete wavelet transform , noise reduction , noise (video) , cluster analysis , median filter , image processing , image (mathematics)
Two‐dimensional transforms are extensively used for speckle noise reduction (despeckling) in ultrasound images. This work proposes a double filter bank structure consisting of a discrete wavelet packet transform (DWPT) and directional filter bank (DFB) along with a fuzzy‐based clustering technique to despeckle ultrasound images. Wavelet packet transform can efficiently reject noises based on grey scale relational thresholding and DFB can efficiently preserve edge information. In this study, instead of conventional thresholding methods, fuzzy‐based clustering techniques are applied for noise rejection. The algorithm provides a consistent improvement over the competing state‐of‐the‐art speckle reduction algorithms due to the improved ability to preserve geometric features while rejecting speckle noise adaptively. The authors claim is validated by applying a number of clinical images with performance indices such as peak signal to noise ratio, mean structural similarity, signal to mean square error, speckle signal to noise ratio, speckle suppression index and edge preservation index.