Speckle reducing anisotropic diffusion

doi:10.1109/TIP.2002.804276

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Speckle reducing anisotropic diffusion

Journal Article•DOI•

Speckle reducing anisotropic diffusion

Yongjian Yu¹, Scott T. Acton¹•Institutions (1)

University of Virginia¹

01 Nov 2002-IEEE Transactions on Image Processing (IEEE)-Vol. 11, Iss: 11, pp 1260-1270

TL;DR: This paper provides the derivation of speckle reducing anisotropic diffusion (SRAD), a diffusion method tailored to ultrasonic and radar imaging applications, and validates the new algorithm using both synthetic and real linear scan ultrasonic imagery of the carotid artery.

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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 (1980, 1981, 1986) and Frost (1982) 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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Journal Article•DOI•

Ultrasound image segmentation: a survey

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J.A. Noble¹, Djamal Boukerroui²•Institutions (2)

University of Oxford¹, University of Technology of Compiègne²

24 Jul 2006-IEEE Transactions on Medical Imaging

TL;DR: This paper reviews ultrasound segmentation methods, in a broad sense, focusing on techniques developed for medical B-mode ultrasound images, and presents a classification of methodology in terms of use of prior information.

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Abstract: This paper reviews ultrasound segmentation methods, in a broad sense, focusing on techniques developed for medical B-mode ultrasound images. First, we present a review of articles by clinical application to highlight the approaches that have been investigated and degree of validation that has been done in different clinical domains. Then, we present a classification of methodology in terms of use of prior information. We conclude by selecting ten papers which have presented original ideas that have demonstrated particular clinical usefulness or potential specific to the ultrasound segmentation problem

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1,150 citations

Cites background or methods from "Speckle reducing anisotropic diffus..."

...In the second stage, a Speckle Reducing Anisotropic Diffusion, (SRAD [113]), was applied to enhance the images and the instantaneous coefficient of variation (ICOV) was utilized as an edge cue in the derivation...
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...It is reported in [113], [114] that the ICOV allows for balanced and well-localized edge strength measurements in bright as well as in dark regions...
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...There is also an extensive literature on speckle reduction which has been proposed as a pre-segmentation step; recent works include wavelets based methods [199]–[202], anisotropic diffusion methods [60], [113], [203]–[205] and others [86], [180], [206]–[210]....
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Journal Article•DOI•

A performance study of general-purpose applications on graphics processors using CUDA

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Shuai Che¹, Michael Boyer¹, Jiayuan Meng¹, David Tarjan¹, Jeremy W. Sheaffer¹, Kevin Skadron¹ - Show less +2 more•Institutions (1)

University of Virginia¹

01 Oct 2008-Journal of Parallel and Distributed Computing

TL;DR: This paper uses NVIDIA's C-like CUDA language and an engineering sample of their recently introduced GTX 260 GPU to explore the effectiveness of GPUs for a variety of application types, and describes some specific coding idioms that improve their performance on the GPU.

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660 citations

Cites background from "Speckle reducing anisotropic diffus..."

...as speckles, without destroying important image features [31]....
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Journal Article•DOI•

Automated breast cancer detection and classification using ultrasound images: A survey

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Heng-Da Cheng¹, Juan Shan¹, Wen Ju¹, Yanhui Guo¹, Ling Zhang² - Show less +1 more•Institutions (2)

Utah State University¹, Shandong University²

01 Jan 2010-Pattern Recognition

TL;DR: Generally, a CAD system consists of four stages: preprocessing, segmentation, feature extraction and selection, and classification, and their advantages and disadvantages are discussed.

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628 citations

Cites background from "Speckle reducing anisotropic diffus..."

...[45] proposed a directional adaptive mean filter based on 2D texture homogeneity histogram to suppress speckles in ultrasound images....
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...Most filters are traditional techniques in spatial domain and can be categorized as linear and nonlinear filters....
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Journal Article•DOI•

Automated 3-D Intraretinal Layer Segmentation of Macular Spectral-Domain Optical Coherence Tomography Images

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Mona K. Garvin¹, Michael D. Abràmoff, Xiaodong Wu¹, Stephen R. Russell, Trudy L. Burns, Milan Sonka¹ - Show less +2 more•Institutions (1)

University of Iowa¹

10 Mar 2009-IEEE Transactions on Medical Imaging

TL;DR: A graph-theoretic segmentation method for the simultaneous segmentation of multiple 3-D surfaces that is guaranteed to be optimal with respect to the cost function and that is directly applicable to the segmentations of 3- D spectral OCT image data is reported.

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Abstract: With the introduction of spectral-domain optical coherence tomography (OCT), much larger image datasets are routinely acquired compared to what was possible using the previous generation of time-domain OCT. Thus, the need for 3-D segmentation methods for processing such data is becoming increasingly important. We report a graph-theoretic segmentation method for the simultaneous segmentation of multiple 3-D surfaces that is guaranteed to be optimal with respect to the cost function and that is directly applicable to the segmentation of 3-D spectral OCT image data. We present two extensions to the general layered graph segmentation method: the ability to incorporate varying feasibility constraints and the ability to incorporate true regional information. Appropriate feasibility constraints and cost functions were learned from a training set of 13 spectral-domain OCT images from 13 subjects. After training, our approach was tested on a test set of 28 images from 14 subjects. An overall mean unsigned border positioning error of 5.69 plusmn 2.41 mum was achieved when segmenting seven surfaces (six layers) and using the average of the manual tracings of two ophthalmologists as the reference standard. This result is very comparable to the measured interobserver variability of 5.71 plusmn 1.98 mum.

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618 citations

Cites methods from "Speckle reducing anisotropic diffus..."

...After applying a speckle-reducing anisotropic diffusion method [22], the surfaces on the flattened and truncated volumetric spectral images were segmented in two groups: surfaces 1, 6, and 7 were segmented simultaneously, followed by the...
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Journal Article•DOI•

Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks

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Moi Hoon Yap¹, Gerard Pons², Joan Martí³, Sergi Ganau, Melcior Sentís, Reyer Zwiggelaar⁴, Adrian K. Davison⁵, Robert Martí³ - Show less +4 more•Institutions (5)

Manchester Metropolitan University¹, Open University of Catalonia², University of Girona³, Aberystwyth University⁴, University of Manchester⁵

01 Jul 2018-IEEE Journal of Biomedical and Health Informatics

TL;DR: This paper proposes the use of deep learning approaches for breast ultrasound lesion detection and investigates three different methods: a Patch-based LeNet, a U-Net, and a transfer learning approach with a pretrained FCN-AlexNet.

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Abstract: Breast lesion detection using ultrasound imaging is considered an important step of computer-aided diagnosis systems. Over the past decade, researchers have demonstrated the possibilities to automate the initial lesion detection. However, the lack of a common dataset impedes research when comparing the performance of such algorithms. This paper proposes the use of deep learning approaches for breast ultrasound lesion detection and investigates three different methods: a Patch-based LeNet, a U-Net, and a transfer learning approach with a pretrained FCN-AlexNet. Their performance is compared against four state-of-the-art lesion detection algorithms (i.e., Radial Gradient Index, Multifractal Filtering, Rule-based Region Ranking, and Deformable Part Models). In addition, this paper compares and contrasts two conventional ultrasound image datasets acquired from two different ultrasound systems. Dataset A comprises 306 (60 malignant and 246 benign) images and Dataset B comprises 163 (53 malignant and 110 benign) images. To overcome the lack of public datasets in this domain, Dataset B will be made available for research purposes. The results demonstrate an overall improvement by the deep learning approaches when assessed on both datasets in terms of True Positive Fraction, False Positives per image, and F-measure.

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564 citations

Cites methods from "Speckle reducing anisotropic diffus..."

...The SRAD method processes the image iteratively with adaptable weighted filters to reduce noise and preserve edges....
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...In addition, the iteration time t was set to 50 in the speckle reducing anisotropic diffusion (SRAD) process....
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...They first used speckle reducing anisotropic diffusion (SRAD) [17]....
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References

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Journal Article•DOI•

A Computational Approach to Edge Detection

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John Canny¹•Institutions (1)

Massachusetts Institute of Technology¹

01 Jun 1986-IEEE Transactions on Pattern Analysis and Machine Intelligence

TL;DR: There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.

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Abstract: This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge.

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28,073 citations

"Speckle reducing anisotropic diffus..." refers methods in this paper

...Instead of using different edge detector that maximizes for each despeckle scheme, we apply the same detector, the Canny detector [5], to provide a fair comparison of algorithms....
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Journal Article•DOI•

Scale-space and edge detection using anisotropic diffusion

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Pietro Perona¹, Jitendra Malik¹•Institutions (1)

University of California, Berkeley¹

01 Jul 1990-IEEE Transactions on Pattern Analysis and Machine Intelligence

TL;DR: A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced, chosen to vary spatially in such a way as to encourage intra Region smoothing rather than interregion smoothing.

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Abstract: A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced. The diffusion coefficient is chosen to vary spatially in such a way as to encourage intraregion smoothing rather than interregion smoothing. It is shown that the 'no new maxima should be generated at coarse scales' property of conventional scale space is preserved. As the region boundaries in the approach remain sharp, a high-quality edge detector which successfully exploits global information is obtained. Experimental results are shown on a number of images. Parallel hardware implementations are made feasible because the algorithm involves elementary, local operations replicated over the image. >

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12,560 citations

"Speckle reducing anisotropic diffus..." refers background or methods in this paper

...So, SRAD is the edge sensitive extension of conventional adaptive speckle filter, in the same manner that the original Perona and Malik anisotropic diffusion [15] is the edge sensitive extension of the average filter....
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...So, the original anisotropic diffusion algorithm [15] may be viewed as the edge-sensitive PDE extension of the Gaussian-weighted averaging filter....
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...Perona and Malik [15] proposed the following nonlinear PDE for smoothing image on a continuous domain: (1) where is the gradient operator, the divergence operator, denotes the magnitude, the diffusion coefficient, and the initial image....
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...To compare SRAD to conventional anisotropic diffusion [15], we implemented diffusion in a homomorphic manner, since the conventional algorithm cannot eliminate multiplicative noise....
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...Perona and Malik [15] proposed the following nonlinear PDE for smoothing image on a continuous domain:...
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Journal Article•DOI•

Digital Image Enhancement and Noise Filtering by Use of Local Statistics

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Jong-Sen Lee¹•Institutions (1)

United States Naval Research Laboratory¹

01 Feb 1980-IEEE Transactions on Pattern Analysis and Machine Intelligence

TL;DR: Experimental results show that in most cases the techniques developed in this paper are readily adaptable to real-time image processing.

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Abstract: Computational techniques involving contrast enhancement and noise filtering on two-dimensional image arrays are developed based on their local mean and variance. These algorithms are nonrecursive and do not require the use of any kind of transform. They share the same characteristics in that each pixel is processed independently. Consequently, this approach has an obvious advantage when used in real-time digital image processing applications and where a parallel processor can be used. For both the additive and multiplicative cases, the a priori mean and variance of each pixel is derived from its local mean and variance. Then, the minimum mean-square error estimator in its simplest form is applied to obtain the noise filtering algorithms. For multiplicative noise a statistical optimal linear approximation is made. Experimental results show that such an assumption yields a very effective filtering algorithm. Examples on images containing 256 × 256 pixels are given. Results show that in most cases the techniques developed in this paper are readily adaptable to real-time image processing.

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2,701 citations

"Speckle reducing anisotropic diffus..." refers background or methods in this paper

...1) Lee Filter [9]: The Lee filter is designed to eliminate speckle noise while preserving edges and point features in radar imagery....
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...The most widely cited and applied filters in this category include the Lee [9]–[11], Frost [6], Kuan [8], and Gamma MAP filters [12]....
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Journal Article•DOI•

A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise

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Victor S. Frost¹, J. A. Stiles¹, K. S. Shanmugan¹, Julian C. Holtzman¹•Institutions (1)

University of Kansas¹

01 Feb 1982-IEEE Transactions on Pattern Analysis and Machine Intelligence

TL;DR: A model for the radar imaging process is derived and a method for smoothing noisy radar images is presented and it is shown that the filter can be easily implemented in the spatial domain and is computationally efficient.

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Abstract: Standard image processing techniques which are used to enhance noncoherent optically produced images are not applicable to radar images due to the coherent nature of the radar imaging process. A model for the radar imaging process is derived in this paper and a method for smoothing noisy radar images is also presented. The imaging model shows that the radar image is corrupted by multiplicative noise. The model leads to the functional form of an optimum (minimum MSE) filter for smoothing radar images. By using locally estimated parameter values the filter is made adaptive so that it provides minimum MSE estimates inside homogeneous areas of an image while preserving the edge structure. It is shown that the filter can be easily implemented in the spatial domain and is computationally efficient. The performance of the adaptive filter is compared (qualitatively and quantitatively) with several standard filters using real and simulated radar images.

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1,906 citations

Journal Article•DOI•

Adaptive speckle filters and scene heterogeneity

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A. Lopes, Ridha Touzi, E. Nezry

01 Nov 1990-IEEE Transactions on Geoscience and Remote Sensing

TL;DR: The most well known adaptive filters for speckle reduction are analyzed and it is shown that they are based on a test related to the local coefficient of variation of the observed image, which describes the scene heterogeneity.

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Abstract: The presence of speckle in radar images makes the radiometric and textural aspects less efficient for class discrimination. Many adaptive filters have been developed for speckle reduction, the most well known of which are analyzed. It is shown that they are based on a test related to the local coefficient of variation of the observed image, which describes the scene heterogeneity. Some practical criteria are introduced to modify the filters in order to make them more efficient. The filters are tested on a simulated synthetic aperture radar (SAR) image and an SAR-580 image. As was expected, the new filters perform better, i.e. they average the homogeneous areas better and preserve texture information, edges, linear features, and point target responses better at the same time. Moreover, they can be adapted to features other than the coefficient of variation to reduce the speckle while preserving the corresponding information. >

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954 citations

"Speckle reducing anisotropic diffus..." refers background or methods in this paper

...The most widely cited and applied filters in this category include the Lee [9]–[11], Frost [6], Kuan [8], and Gamma MAP filters [12]....
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...3 shows the smoothed images in experiment I, processed by using four speckle reduction schemes: enhanced Lee filter [12], enhanced Frost [12], anisotropic diffusion-based homomorphic filtering (denoted by “AD-homomorph” in the results), and SRAD, respectively....
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...More recently, the Gamma maximum a posteriori (MAP) and extended versions of the Lee filter and the Frost filter have been introduced to alter performance locally according to three cases [12], [13]....
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...The Lee filter and enhanced Lee filter [12] process a current pixel based on its intensity and the intensities of neighboring pixels inside a fixed square window....
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..., the enhanced Lee filter [12], the enhanced Frost filter [12], and anisotropic diffusion [15] applied as a homomorphic filter....
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