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• W2103732007 abstract "Several algorithms have been proposed in the literature for image denoising but none exhibit optimal performance for all range and types of noise and for all image acquisition modes. We describe a new general framework, built from four-neighborhood clique system, for denoising medical images. The kernel quantifies smoothness energy of spatially continuous anatomical structures. Scalar and vector valued quantification of smoothness energy configures images for Bayesian and variational denoising modes, respectively. Within variational mode, the choice of norm adapts images for either total variation or Tikhonov technique. Our proposal has three significant contributions. First, it demonstrates that the four-neighborhood clique kernel is a basic filter, in same class as Gaussian and wavelet filters, from which state-of-the-art denoising algorithms are derived. Second, we formulate theoretical analysis, which connects and integrates Bayesian and variational techniques into a two-layer structured denoising system. Third, our proposal reveals that the first layer of the new denoising system is a hitherto unknown form of Markov random field model referred to as single-layer Markov random field (SLMRF). The new model denoises a specific type of medical image by minimizing energy subject to knowledge of mathematical model that describes relationship between the image smoothness energy and noise level but without reference to a classical prior model. SLMRF was applied to and evaluated on two real brain magnetic resonance imaging datasets acquired with different protocols. Comparative performance evaluation shows that our proposal is comparable to state-of-the-art algorithms. SLMRF is simple and computationally efficient because it does not incorporate a regularization parameter. Furthermore, it preserves edges and its output is devoid of blurring and ringing artifacts associated with Gaussian-based and wavelet-based algorithms. The denoising system is potentially applicable to speckle reduction in ultrasound images and extendable to three-layer structure that account for texture features in medical images. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 224–238, 2014" @default.
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• W2103732007 date "2014-08-13" @default.
• W2103732007 modified "2023-09-26" @default.
• W2103732007 title "Four-neighborhood clique kernel: A general framework for Bayesian and variational techniques of noise reduction in magnetic resonance images of the brain" @default.
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• W2103732007 doi "https://doi.org/10.1002/ima.22098" @default.
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