An adaptive nonlinear diffusion algorithm for filtering medical images

Jin, Jesse S.; Wang, Y.; Hiller, J.

doi:10.1109/4233.897062

Cited by 63 publications

(30 citation statements)

References 27 publications

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“…Therefore, they introduced a diffusion coefficient which can automatically adjust the diffusivity according to varied image content. This simple diffusion model has been applied to de-speckling of medical ultrasound images [22].…”

Section: Perona-malik Diffusion Filtermentioning

confidence: 99%

“…The PDE methods applied to achieve high-quality image de-noising have attracted extensive attention [7,22]. The diffusion filtering category includes isotropic model such as Perona-Malik diffusion (PMD) filter [23], anisotropic model such as Weickert J Diffusion (WJD) filter [24], and another diffusion model such as Total Variation (TV) filter which was first proposed by Rudin et al [25].…”

Section: Introductionmentioning

confidence: 99%

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Systematic Evaluation on Speckle Suppression Methods in Examination of Ultrasound Breast Images

2016

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Abstract:Breast ultrasound is an important tool used in the medical treatment and diagnosis of breast tumor. However, noise defined as speckles are generated inevitably. Although the existence of speckle may be beneficial to diagnosis if used by a well-trained observer, it often causes disturbance which negatively affects clinical diagnosis, not only by reducing resolution and contrast of ultrasound images, but also by adding difficulties to recognize tumor region accurately. In this paper, we investigate a number of popular de-speckling algorithms, including filters based on frequency domain, filters based on local statistical properties, filters based on minimum mean square error (MMSE), and filters based on Partial Differential Equation (PDE). Two visual measurement evaluation criteria, i.e., Mean to Variance Ratio (VMR) and Laplace Response of Domain (LRD), are chosen for the performance comparison of those filters in the application of ultrasound breast image filtering. Moreover, the filtering effect is further evaluated with respect to the segmentation accuracy of tumor regions. According to the evaluation results, we conclude that Bilateral Filter (BF) achieves the best visual effect. Although Weickert J Diffusion (WJD) and Total Variation (TV) can also obtain good visual effect and segmentation accuracy, they are very time-consuming.

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Section: Perona-malik Diffusion Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Systematic Evaluation on Speckle Suppression Methods in Examination of Ultrasound Breast Images

2016

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“…Based on a linear speckle noise model and the minimum mean square error (MMSE) design approach, the filter produces the enhanced data according to (5) where is the mean value of the intensity within the filter window ; and is the adaptive filter coefficient determined by (6) Here, (7) and is a constant for a given image and can be determined by either (8) or (9) where is the effective number of looks of the noisy image, and are the intensity variance and mean over a homogeneous area of the image, respectively.…”

Section: B Adaptive Speckle Filtersmentioning

confidence: 99%

“…Let . We then use a three-stage approach [7] to calculate the right hand side of the SRAD PDE. In the first stage, we calculate the derivative approximations and the Laplacian approximation as (52) (53) (54) with symmetric boundary conditions Finally, by approximating time derivative with forward differencing, the numerical approximation to the differential equation is given by (61) Equation (61) is called the SRAD update function.…”

Section: Discretizationmentioning

confidence: 99%

Speckle reducing anisotropic diffusion

Acton

2002

IEEE Trans. on Image Process.

1,436

234

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Abstract-This paper provides the derivation of speckle reducing anisotropic diffusion (SRAD), a diffusion method tailored to ultrasonic and radar imaging applications. SRAD is the edge-sensitive diffusion for speckled images, in the same way that conventional anisotropic diffusion is the edge-sensitive diffusion for images corrupted with additive noise. We first show that the Lee and Frost filters can be cast as partial differential equations, and then we derive SRAD by allowing edge-sensitive anisotropic diffusion within this context. Just as the Lee and Frost filters utilize the coefficient of variation in adaptive filtering, SRAD exploits the instantaneous coefficient of variation, which is shown to be a function of the local gradient magnitude and Laplacian operators. We validate the new algorithm using both synthetic and real linear scan ultrasonic imagery of the carotid artery. We also demonstrate the algorithm performance with real SAR data. The performance measures obtained by means of computer simulation of carotid artery images are compared with three existing speckle reduction schemes. In the presence of speckle noise, speckle reducing anisotropic diffusion excels over the traditional speckle removal filters and over the conventional anisotropic diffusion method in terms of mean preservation, variance reduction, and edge localization.

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“…It reveals that TF provides the largest increase (> 50%) in CNR, as compared to the other two filters. In further studies, a double-Gaussian mixture model [11] has been used to segment the aforesaid filtered images. The segmented images are shown in Fig.…”

Section: D Medical Imagementioning

confidence: 99%

A nonlinear and non-iterative noise reduction technique for medical images: concept and methods comparison

Wong

Chung

2004

International Congress Series

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Abstract.Filtering is a preliminary process in many medical image processing applications. It is aiming at reducing noise in images. Any post-processing tasks may benefit from the reduction of noise. In this contribution, a method for edge-preserving smoothing of 2D and 3D medical images is described. The proposed method uses a narrow spatial window and takes only a single iteration to denoise an image. It integrates geometric, photometric and local structural similarities to achieve non-linear noise reduction. We have applied this novel method to medical images and compared its denoising capability with other noise reduction techniques. The experimental results have shown that our method is capable of reducing severe noise, and is an adequate pre-process to improving the quality of segmentation and facilitating the feature extraction process.

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An adaptive nonlinear diffusion algorithm for filtering medical images

Cited by 63 publications

References 27 publications

Systematic Evaluation on Speckle Suppression Methods in Examination of Ultrasound Breast Images

Systematic Evaluation on Speckle Suppression Methods in Examination of Ultrasound Breast Images

Speckle reducing anisotropic diffusion

A nonlinear and non-iterative noise reduction technique for medical images: concept and methods comparison

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