An efficient technique for speckle noise reduction in ultrasound images
01 Feb 2017-pp 177-180
TL;DR: In this paper, the authors proposed a method that suppresses the speckle and preserves the edges by combining the results of two filters: one filter is an edge filter and another is a smoothing filter.
Abstract: Ultrasound images (US) are very prone to speckle noise, which degrades their quality and make doctor's evaluation difficult. This paper presents method that suppresses the speckle and preserves the edges. The proposed method is based on combining the results of two filters: one filter is an edge filter and another is a smoothing filter. Filters applied are Detail Preserving Anisotropic Diffusion (DPAD) and Optimized Bayesian Non-Local Mean (OBNLM). The outputs of the filters are combined with the help of homogeneity map (HM). HM differentiates homogeneous and edge region of the image. The experiments are conducted using a field II generated synthetic image and a real ultrasound image. The results are obtained for proposed method and compared with other methods based on the parameters such as Peak Signal to Noise Ratio (PSNR) and Edge Keeping Index (EKI).
Citations
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TL;DR: In this article , the de-speckling filter was applied on original and noisy ultrasound images and results were analyzed based on four image quality metrics (i.e., peak signal to noise ratio (PSNR), root mean square error (RMSE), speckle suppression index (SSI), and standard deviation to mean ratio (STM)).
8 citations
01 Jan 2021
TL;DR: In this paper, an automated healthcare system using ultrasound imaging facilitates an advisory system for the diagnosis of people suffering from abnormalities in the organs, which helps in clinical diagnosis in a short duration.
Abstract: Technology-enabled healthcare systems are becoming more intelligent, efficient, and effective with the advancement in machine learning (ML) and deep learning (DL) techniques. The proposed methodology is an application of the healthcare system that falls under the category of medical imaging, which helps in clinical diagnosis. As ultrasound imaging is safe, painless, and the patient is not exposed to ionizing radiation, it allows real-time imaging. Because of these features, ultrasound imaging is the most frequently preferred medical imaging in clinical practice for diagnostic purposes. Understanding and interpreting ultrasound images requires well-trained radiologists, and it requires more time in the diagnosis process. Hence an advisory system is needed that helps in identifying organs and any associated abnormalities in a short duration. The proposed automated healthcare system using ultrasound imaging facilitates an advisory system for the diagnosis of people suffering from abnormalities in the organs. In order to develop an automated healthcare system, exhaustive normal and abnormal intraabdominal ultrasound images are collected and preprocessed. Localization of the region of interest (ROI) is performed with an effective segmentation method. The final step is experimenting with ROI images to extract features to identify intraabdominal organs and their abnormalities. To conclude, in the chapter we have noted various challenges with ultrasound images and ML and DL techniques.
3 citations
01 Jul 2020
TL;DR: The need for despeckling of 2D ultrasound images is studied by evaluating the performance metrics for inpainting based on fast marching algorithm of speckled and despecksled 2 D ultrasound images.
Abstract: Image processing has paved its way through various applications in the medical field, where researches are being carried out to prompt and improvise the care and treatment given to patients in all aspects. Medical image processing applications concerns the need for obtaining images as per the requirement. Inpainting of markers on medical images generated using various modalities is crucial to process the images for numerous applications. One of the inherent and profoundly seen problem in medical images, especially in the ultrasound images is the speckle noise. It alters the edges, fine details and other features of organs that is required for interpretation. In this paper, the need for despeckling of 2D ultrasound images is studied by evaluating the performance metrics for inpainting based on fast marching algorithm of speckled and despeckled 2D ultrasound images.
1 citations
Cites background from "An efficient technique for speckle ..."
...This paper evaluates the need for speckle noise removal before inpainting of images as ultrasound images are prone to speckle noise [2]....
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01 Jan 2021
TL;DR: In this paper, a review of denoising framework for efficient removal of noise from 3D images, different filters which are used so far for removal of the noise are discussed.
Abstract: An image is a distributed amplitude of colors on a plane. An image may be in the form of two-dimensional image or three-dimensional image. Such images are compiled using optical sensors like camera and are processed using various image processing tools for better visualization. Purpose of the image processing is not limited for better visualization, but it is extended to remove noise from the captured image. Noise is a random variation of brightness, contrast and color pallets in an image. In the present discussion through review of denoising framework for efficient removal of noise from 3D images, different filters which are used so far for removal of noise are discussed. The research work is further extended by designing novel denoising framework for efficient removal of noise from the 3D image.
1 citations
01 Jan 2021
TL;DR: In this paper, the authors proposed an advisory system for intra-abdominal ultrasound images that does not contain any exploring information about the patient, and the proposed system is segmented from ultrasound image and identified by using deep neural network, and using shape and texture features, abnormalities are identified if any.
Abstract: Presently deep learning techniques are playing important role in making healthcare systems more intelligent, efficient, and effective. Proposed methodology is an advisory system in medical imaging which helps in clinical diagnosis. In medical imaging, ultrasound imaging is most frequently used as it is safe, painless, not exposed to ionizing radiation, and it allows real-time imaging. Ultrasound imaging takes more time in diagnosis and well-trained radiologist for interpreting and understanding. Hence, proposed system acts as an advisory system in identifying intra-abdominal organs and abnormalities if any. In this proposed system, the data was collected from intra-abdominal ultrasound images that do not contain any exploring information about the patient. Using filters, noise in ultrasound images is removed. Organ is segmented from ultrasound image and is identified by using deep neural network, and using shape and texture features, abnormalities are identified if any. At the end, various challenges that exist with deep neural network and ultrasound images are discussed.
References
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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
"An efficient technique for speckle ..." refers background or methods in this paper
...Anisotropic diffusion filtering [8] is proposed by Perona and Malik is a technique in which image noise is reduced without loss of significant information in the image....
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...Results obtained from proposed methods are also compared with the one obtained from Speckle Reducing Anisotropic Diffusion (SRAD) filtering [14] and Perona Malik Anisotropic Diffusion (PMAD) filtering [8]....
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TL;DR: A general mathematical and experimental methodology to compare and classify classical image denoising algorithms and a nonlocal means (NL-means) algorithm addressing the preservation of structure in a digital image are defined.
Abstract: The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics In spite of the sophistication of the recently proposed methods, m
4,153 citations
"An efficient technique for speckle ..." refers methods in this paper
...Earlier the NL means based noise reduction is proposed for Gaussian noise by Buades [12]....
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TL;DR: It is concluded that the patient's skin should be abraded to reduce impedance, and measurements should be avoided in the first 10 min after electrode placement, to allow satisfactory images.
Abstract: A computer simulation is used to investigate the relationship between skin impedance and image artefacts in electrical impedance tomography. Sets of electrode impedance are generated with a pseudo-random distribution and used to introduce errors in boundary voltage measurements. To simplify the analysis, the non-idealities in the current injection circuit are replaced by a fixed common-mode error term. The boundary voltages are reconstructed into images and inspected. Where the simulated skin impedance remains constant between measurements, large impedances (> 2k omega) do not cause significant degradation of the image. Where the skin impedances 'drift' between measurements, a drift of 5% from a starting impedance of 100 omega is sufficient to cause significant image distortion. If the skin impedances vary randomly between measurements, they have to be less than 10 omega to allow satisfactory images. Skin impedances are typically 100-200 omega at 50 kHz on unprepared skin. These values are sufficient to cause image distortion if they drift over time. It is concluded that the patient's skin should be abraded to reduce impedance, and measurements should be avoided in the first 10 min after electrode placement.
1,976 citations
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
"An efficient technique for speckle ..." refers methods in this paper
...Results obtained from proposed methods are also compared with the one obtained from Speckle Reducing Anisotropic Diffusion (SRAD) filtering [14] and Perona Malik Anisotropic Diffusion (PMAD) filtering [8]....
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TL;DR: The adaptive noise smoothing filter is a systematic derivation of Lee's algorithm with some extensions that allow different estimators for the local image variance and its easy extension to deal with various types of signal-dependent noise.
Abstract: In this paper, we consider the restoration of images with signal-dependent noise. The filter is noise smoothing and adapts to local changes in image statistics based on a nonstationary mean, nonstationary variance (NMNV) image model. For images degraded by a class of uncorrelated, signal-dependent noise without blur, the adaptive noise smoothing filter becomes a point processor and is similar to Lee's local statistics algorithm [16]. The filter is able to adapt itself to the nonstationary local image statistics in the presence of different types of signal-dependent noise. For multiplicative noise, the adaptive noise smoothing filter is a systematic derivation of Lee's algorithm with some extensions that allow different estimators for the local image variance. The advantage of the derivation is its easy extension to deal with various types of signal-dependent noise. Film-grain and Poisson signal-dependent restoration problems are also considered as examples. All the nonstationary image statistical parameters needed for the filter can be estimated from the noisy image and no a priori information about the original image is required.
1,475 citations
"An efficient technique for speckle ..." refers methods in this paper
...DPAD filter as proposed in [10] with the diffusion function derived from Kuan [13] filter....
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