Adaptive speckle reduction in ultrasound images using fuzzy logic on Coefficient of Variation. (January 2016)

• Record Type: Journal Article
• Authors: Jai Jaganath Babu, Jayachandran; Florence Sudha, Gnanou
• Is Part Of: Biomedical signal processing and control. Volume 23(2016)
• Page Start: 93
• Page End: 103
• Publication Date: 2016-01
• Subjects: Fuzzy logic -- Speckle noise -- Coefficient of variation -- Structural similarity; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2015.08.001
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A speckle reduction filter for ultrasound images using Fuzzy logic on Coefficient of Variation (CV) is proposed. In the first level of adaptation, fuzzy logic is applied to classify noisy pixels into three classes based on CV. In the second level, the most suitable filter is applied on Homogenous, Detail and Edge defined classes based on Structural Similarity Index Measure of the image. Experimental results and comparison with existing works shows appreciable improvement in noise suppression and preservation of image structural details. Abstract: Speckle reduction is an important pre-processing stage for ultrasound medical image processing. In this paper, an adaptive fuzzy logic approach for speckle noise reduction in ultrasound images is presented. In the proposed method, adaptiveness is incorporated at two levels. In the first level, applying fuzzy logic on the coefficients of variation computed from the noisy image, image regions are classified. The best suitable filter for the particular image region is adaptively selected by the system yielding appreciable improvement in noise suppression and preservation of image structural details. At the second level, to distinguish between edges and noise, the proposed method uses a weighted averaging filter. The structural similarity measure, which depends on the nature of image and quantity of noise present in the image, is used as the tuning parameter. Thus with two levels of adaptiveness, the proposed method has better edge preservation compared to existing methods. Experimental results of the proposed method for natural images, Field II simulated images and real ultrasound images, show that proposed denoising algorithm has better noise suppression and is able to preserve edges and image structural details compared with existing methods.