A Variable Order Fractional Differential-Based Texture Enhancement Algorithm with Application in Medical Imaging

Yu, Qiang, Vegh, Viktor, Liu, Fawang and Turner, Ian (2015) A Variable Order Fractional Differential-Based Texture Enhancement Algorithm with Application in Medical Imaging. PLoS One, 10 7: e0132952-e0132952. doi:10.1371/journal.pone.0132952

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Author Yu, Qiang
Vegh, Viktor
Liu, Fawang
Turner, Ian
Title A Variable Order Fractional Differential-Based Texture Enhancement Algorithm with Application in Medical Imaging
Journal name PLoS One   Check publisher's open access policy
ISSN 1932-6203
Publication date 2015-07
Year available 2015
Sub-type Article (original research)
DOI 10.1371/journal.pone.0132952
Open Access Status DOI
Volume 10
Issue 7
Start page e0132952
End page e0132952
Total pages 35
Place of publication San Francisco, CA United States
Publisher Public Library of Science
Collection year 2016
Language eng
Formatted abstract
Texture enhancement is one of the most important techniques in digital image processing and plays an essential role in medical imaging since textures discriminate information. Most image texture enhancement techniques use classical integral order differential mask operators or fractional differential mask operators using fixed fractional order. These masks can produce excessive enhancement of low spatial frequency content, insufficient enhancement of large spatial frequency content, and retention of high spatial frequency noise. To improve upon existing approaches of texture enhancement, we derive an improved Variable Order Fractional Centered Difference (VOFCD) scheme which dynamically adjusts the fractional differential order instead of fixing it. The new VOFCD technique is based on the second order Riesz fractional differential operator using a Lagrange 3-point interpolation formula, for both grey scale and colour image enhancement. We then use this method to enhance photographs and a set of medical images related to patients with stroke and Parkinson’s disease. The experiments show that our improved fractional differential mask has a higher signal to noise ratio value than the other fractional differential mask operators. Based on the corresponding quantitative analysis we conclude that the new method offers a superior texture enhancement over existing methods.
Keyword Advection Diffusion Equation
Numerical Methods
Edge Detection
Digital Image
Space
Approximation
Entropy
Models
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2016 Collection
Centre for Advanced Imaging Publications
 
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