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  4. Despeckle Filtering for Multiscale Amplitude-Modulation Frequency-Modulation (AM-FM) Texture Analysis of Ultrasound Images of the Intima-Media Complex
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Despeckle Filtering for Multiscale Amplitude-Modulation Frequency-Modulation (AM-FM) Texture Analysis of Ultrasound Images of the Intima-Media Complex

Journal
International Journal of Biomedical Imaging
Date Issued
2014
Author(s)
Loizou, Christos P.  
Murray, Víctor  
Pattichis, Marios S.  
Pantziaris, Marios  
Nicolaides, Andrew N.  
Pattichis, Constantinos S.  
DOI
10.1155/2014/518414
Abstract
The intima-media thickness (IMT) of the common carotid artery (CCA) is widely used as an early indicator of cardiovascular disease (CVD). Typically, the IMT grows with age and this is used as a sign of increased risk of CVD. Beyond thickness, there is also clinical interest in identifying how the composition and texture of the intima-media complex (IMC) changed and how these textural changes grow into atherosclerotic plaques that can cause stroke. Clearly though texture analysis of ultrasound images can be greatly affected by speckle noise, our goal here is to develop effective despeckle noise methods that can recover image texture associated with increased rates of atherosclerosis disease. In this study, we perform a comparative evaluation of several despeckle filtering methods, on 100 ultrasound images of the CCA, based on the extracted multiscale Amplitude-Modulation Frequency-Modulation (AM-FM) texture features and visual image quality assessment by two clinical experts. Texture features were extracted from the automatically segmented IMC for three different age groups. The despeckle filters hybrid median and the homogeneous mask area filter showed the best performance by improving the class separation between the three age groups and also yielded significantly improved image quality.
Subjects

Intima-media thicknes...

Ultrasound image

Common carotid artery...

Intima-media complex

Multiscale Amplitude-...

B-mode scan

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518414 (1).pdf

Size

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Format

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Checksum (MD5)

16a8c7b22754bb21c3f54d70755b93d0

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