Open AccessNon‐subsampled contourlet transform based image Denoising in ultrasound thyroid images using adaptive binary morphological operations
Author(s) -
Jai Jaganath Babu Jayachandiran,
Sudha Gnanou Florence
Publication year - 2014
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2014.0008
Subject(s) - contourlet , artificial intelligence , speckle noise , computer vision , computer science , binary image , speckle pattern , noise reduction , pattern recognition (psychology) , noise (video) , filter (signal processing) , image processing , wavelet transform , image (mathematics) , wavelet
Speckle noise reduction is an important preprocessing stage for ultrasound medical image processing. In this paper, a despeckling algorithm is proposed based on non‐subsampled contourlet transform. This transform has the property of high directionality, anisotropy and translation invariance, which can be controlled by non‐subsampled filter banks. This study aims to denoise the speckle noise in ultrasound images using adaptive binary morphological operations, in order to preserve edges, contours and textures. In morphological operations, structural element plays an important role for image enhancement. In this work, different shapes of structural element have been analysed and filtering parameters have been changed adaptively depending on the nature of the image and the amount of noise in the image. Experimental results of proposed method for natural images, Field II simulated images and real ultrasound images, show that the proposed method is able to preserve edges and image structural details compared with existing methods.