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Journal ArticleDOI

A non-linear complex diffusion based filter adapted to Rayleigh’s speckle noise for de-speckling ultrasound images

02 Oct 2012-International Journal of Biomedical Engineering and Technology (Inderscience Publishers Ltd)-Vol. 10, Iss: 2, pp 101
TL;DR: In this article, an edge and structure preserving non-linear complex diffusion based filter adapted to Rayleigh's speckle noise for 2D ultrasound images is proposed and implemented in MATLAB 7.0.
Abstract: In this paper, an edge and structure preserving non-linear complex diffusion based filter adapted to Rayleigh’s speckle noise for speckle reduction from 2D ultrasound images is proposed and implemented in MATLAB 7.0. The initial condition of the proposed filter is the speckle noised ultrasound image and the speckle-reduced image is obtained after certain iterations of the filter till its convergence. For digital implementations, the proposed scheme has been discretised using the finite difference scheme. The performance of the proposed complex diffusion-based filter has been evaluated both qualitatively and quantitatively. A comparative study of the proposed scheme with other standard speckle reduction schemes such as the homomorphic Wiener filter, the Lee filter, the Frost filter, the Kuan filter and the Speckle Reducing Anisotropic Diffusion (SRAD) filter for several ultrasound images with varying amounts of speckle noise variance is also presented. The obtained results show that the proposed non-linear complex diffusion-based scheme performs better than all other schemes in consideration and is also well capable of preserving edges and fine structures from ultrasound images during speckle reduction.
Citations
More filters
Journal Article
TL;DR: In this paper, a hybrid-cascaded framework of Ordered Subsets Expectation Maximization (OSEM) is proposed to address the problem of slow convergence, choice of optimum initial point and ill-posedness.
Abstract: PET/SPECT reconstruction algorithms often used the data contain a high amount of statistical noise, that have been acquired from a limited angular range, or have a limited number of views. Generally, an iterative reconstruction algorithm suffers from the problem of slow convergence, choice of optimum initial point and ill-posedness. To address these aforementioned issues, in this paper a hybrid-cascaded framework of Ordered Subsets Expectation Maximisation (OSEM) is proposed. This allows us to use more than one algorithm for reconstruction and extract the benefits of each. The proposed model includes two steps: primary and secondary. In the primary step, SART method is used as an initial guess for OSEM to deal with the problem of initialisation and convergence. The task of primary step will be to provide an enhanced image to secondary step to be used as an initial estimate for reconstruction process. The secondary step is a hybrid combination of two parts namely the OSEM reconstruction and anisotropic diffusion (AD) as a prior. By incorporating a suitable prior knowledge the problem of ill-posedness is addressed. A comparative analysis of the proposed model with some other standard methods in literature is presented both qualitatively and quantitatively for phantom test data sets. The obtained result justifies the applicability of the proposed model.

9 citations

Journal ArticleDOI
TL;DR: This paper deals with an experimental approach towards the use of metallic strain gauge that produces an appreciable change in resistance with change in volume of urine in the bladder which can be used to study the dysfunction.
Abstract: Urinary bladder dysfunction is a major problem, affecting millions worldwide and causing major devastating medical conditions. The urinary bladder dysfunction not only leads to loss of control over the bladder muscles, but also affects the kidney and its functioning. This paper deals with an experimental approach towards the use of metallic strain gauge that produces an appreciable change in resistance with change in volume of urine in the bladder which can be used to study the dysfunction.

3 citations

Journal ArticleDOI
30 Sep 2020
TL;DR: The proposed approach is capable of handling segmentation problem of blocky artifacts while achieving good tradeoff between Rayleigh noise removal and edge preservation and may be useful for finding additional 33% cases of breast cancer which is missed or not detected by mammography.
Abstract: Received: 9 July 2019 Accepted: 20 June 2020 This paper proposes a single framework for segmentation of abnormalities for breast cancer detection from Ultrasound images in presence of Rayleigh noise i.e. noise removal and segmentation are embedded in single step. It accomplishes dual purpose in a single framework simultaneously for the preprocessing and segmentation. The proposed framework comprises of two terms, first term, is used for segmentation which is a modified fuzzy c-means segmentation (MFCM) approach while second term is an adaptive complex diffusion based non linear filter (ACDPDE) that performs as regularization function for removal of Rayleigh noise, enhancement, and edge preservation of ultrasound Image. The various existing segmentation methods viz. K-Means, Texture based, Fuzzy C-Means (FCM), total variation based FCM (TVFCM), Adaptive fourth order PDE based FCM (AFPDEFCM), and the proposed method are evaluated for 50 sample ultrasound images of breast cancer. The region of interest (ROI) segmented image of ultrasound breast tissue is compared with ground truth images. From the acquired results and its analysis, it is observed that the proposed method is more robust and provides better segmentation result for ultrasound images in terms of various performance measures such as Global Constancy error (GCE), Tanimoto coefficient, Variation of Information (VOI), Probability Random Index (PRI), Jaccard coefficient, accuracy, True Positive Rate (TPR), False Positive Rate (FPR), True Negative Rate (TNR), dice index, False Negative Rate (FNR), and Area under curve (AUC). The proposed approach is capable of handling segmentation problem of blocky artifacts while achieving good tradeoff between Rayleigh noise removal and edge preservation. The proposed method may be useful for finding additional 33% cases of breast cancer which is missed or not detected by mammography.

3 citations

Journal ArticleDOI
TL;DR: The experimental result shows that the proposed bi-modal extended Huber loss function based refined mask regional convolutional neural network is a better suited approach for multi-instance detection, localization and classification of breast cancer.
Abstract: Breast cancer is an extremely aggressive cancer in women. Its abnormalities can be observed in the form of masses, calcification and lumps. In order to reduce the mortality rate of women its detection is needed at an early stage. The present paper proposes a novel bi-modal extended Huber loss function based refined mask regional convolutional neural network for automatic multi-instance detection and localization of breast cancer. To refine and increase the efficacy of the proposed method three changes are casted. First, a pre-processing step is performed for mammogram and ultrasound breast images. Second, the features of the region proposal network are separately mapped for accurate region of interest. Third, to reduce overfitting and fast convergence, an extended Huber loss function is used at the place of SmoothL1(x) in boundary loss. To extend the functionality of Huber loss, the delta parameter is automated by the aid of median absolute deviation with grid search algorithm. It provides the best optimum value of delta instead of user-based value. The proposed method is compared with pre-existing methods in terms of accuracy, true positive rate, true negative rate, precision, F-score, balanced classification rate, Youden’s index, Jaccard Index and dice coefficient on CBIS-DDSM and ultrasound database. The experimental result shows that the proposed method is a better suited approach for multi-instance detection, localization and classification of breast cancer. It can be used as a diagnostic medium that helps in clinical purposes and leads to a precise diagnosis of breast cancer abnormalities.

2 citations

Journal ArticleDOI
TL;DR: In this paper, two despeckling techniques, based on wavelet thresholding Bayes shrinkage and genetic-algorithm-based wavelet denoising technique are implemented.
Abstract: Speckle noise is the primary factor that limits the contrast resolution of diagnostic ultrasound images, thereby limiting the detection of small low-contrast lesions and making the ultrasound images generally difficult for a nonspecialist to interpret Therefore, speckle is more often considered as a dominant source of noise in ultrasound imaging and should be filtered out by a robust despeckling technique In this paper, we compare two despeckling techniques, based on wavelet thresholding Bayes shrinkage and genetic-algorithm-based wavelet denoising despeckling technique are implemented Wavelet functions were studied in detail and the two algorithms were implemented using the Haar wavelet as a wavelet function The application of this new filter on ultrasound images has shown a superior performance over the state-of-the-art wavelet-based denoising methods in terms of visual quality and structural similarity index map

1 citations

References
More filters
Journal ArticleDOI
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Abstract: Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu//spl sim/lcv/ssim/.

40,609 citations

Journal ArticleDOI
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.
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. >

12,560 citations


"A non-linear complex diffusion base..." refers background or methods in this paper

  • ...Therefore, to overcome the diffi culty associated with isotropic diffusion, in this paper, it is proposed to use diffusion-based adaptive fi lters (Perona and Malik, 1990; Gilboa et al., 2004)....

    [...]

  • ...Some popular methods are simple averaging (Jain, 2006), least mean squares (Gonzalez and Wintz, 1987; Jain, 2006), Weiner fi ltering (Jain, 2006), wavelet-based de-noising (Donoho and Johnstone, 1994), anisotropic diffusion-based techniques (Perona and Malik, 1990), the total variation (TV)-based approach (Rudin et al....

    [...]

  • ...Substituting the φ(||∇I||) with Perona and Malik’s (1990) energy functional ||∇I|| in equation (4), it reads...

    [...]

  • ...The diffusion coeffi cient c is defi ned as follows (Perona and Malik, 1990):...

    [...]

  • ...The one suitable choice for the energy term φ(||∇I||) based on the energy functional defi ned by Perona and Malik (1990) for deriving an anisotropic diffusion-based fi lter is:...

    [...]

Book
03 Oct 1988
TL;DR: This chapter discusses two Dimensional Systems and Mathematical Preliminaries and their applications in Image Analysis and Computer Vision, as well as image reconstruction from Projections and image enhancement.
Abstract: Introduction. 1. Two Dimensional Systems and Mathematical Preliminaries. 2. Image Perception. 3. Image Sampling and Quantization. 4. Image Transforms. 5. Image Representation by Stochastic Models. 6. Image Enhancement. 7. Image Filtering and Restoration. 8. Image Analysis and Computer Vision. 9. Image Reconstruction From Projections. 10. Image Data Compression.

8,504 citations

Journal ArticleDOI
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.
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.

1,906 citations

Journal ArticleDOI
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.
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.

1,816 citations


"A non-linear complex diffusion base..." refers background or methods in this paper

  • ..., 1982), the Kuan fi lter (Kuan and Sawchuk, 1987) and the Speckle Reducing Anisotropic Diffusion (SRAD) fi lter (Yu and Acton, 2002)....

    [...]

  • ...Further, k can be approximated as a negative exponential distribution (Yu and Acton, 2002):...

    [...]

  • ..., 1982), the Kuan fi lter (Kuan and Sawchuk, 1987) and the (SRAD) fi lter (Yu and Acton, 2002)....

    [...]

  • ...The performances of the proposed method compared to other methods available in the literature (Lee, 1981, 1983; Frost et al., 1982; Kuan et al., 1987; Yu and Acton, 2002) have been compared in terms of MSE, PSNR, CP and MSSIM for varying amounts of speckle noise variance....

    [...]