Efficient Speckle Reduction Techniques in Ultrasound Video Quality Enhancement – A Comparative Evaluation
01 Sep 2017-
TL;DR: In the present work seven different filters are listed and experimental work is carried out on LEE and Hybrid Median filter (HMF), the experimental result of PSNR and MSE values are considered for evaluation purpose.
Abstract: Amongst many imaging modalities available now a days, Ultrasound is the most widely used because of the reasons like radiation free, real time, non-invasive and non-ionizing During patient’s care, medical images, videos & other physiological signals, become an integral part of diagnostic and treatment phases Ultrasound is more advantageous and cheaper than other modalities like MRI, CT, PET Speckle noise present in US hampers the quality of images which in turn considerably increases the difficulty in medical visual inspection The diagnostic accuracy will be more if image is less noisy, this necessitates the use of efficient despeckling filter which will take care of edge detection, loss of information and improve visual evaluation MSE assess the image quality, if the value is high then image quality is poor PSNR is another quality metrics, high value indicates the better denoising algorithm In the present work seven different filters are listed and experimental work is carried out on LEE and Hybrid Median filter (HMF) The experimental result of PSNR and MSE values are considered for evaluation purpose
Citations
More filters
01 Feb 2021
TL;DR: A comparative analysis of well established despeckling filters compiled for speckle suppression and feature enhancement shows that feature enhanced Fast Non-Local Means (FNLM) filter outperforms other state-of-art filtering techniques in the aspect of higher PSNR value and preserves structural similarity in terms of SSIM value near to unity.
Abstract: Rheumatic heart disease has a substantial impact on morbidity and mortality for both men and women in developing countries. It is a complication of autoimmune phenomenon known as acute rheumatic fever in response to group A streptococcus bacteria. It often causes damage to valves and lacks its functionality. Initial manifestations of prolapsed valves are evident in echocardiography in the form of valve bulging, commissural fusion and restricted leaflet motion. However, these echocardiogram images are inevitably degraded by multiplicative noise known as speckle noise at the time of image acquisition and transmission. Hence despeckling is of vital importance for enhancing ultrasound image quality. In this paper, a comparative analysis of well established despeckling filters compiled for speckle suppression and feature enhancement has been performed. In addition to this, the performance of these filters is validated using different quantitative metrics. The experimental result shows that feature enhanced Fast Non-Local Means (FNLM) filter outperforms other state-of-art filtering techniques in the aspect of higher PSNR value and preserves structural similarity in terms of SSIM value near to unity. In conclusion, the preprocessing intends to suppress speckle noise and enhancing perceived visual quality that aids for further development of virtual cardiac model.
TL;DR: Support vector machine (SVM) has outperformed other technique on sensitivity and time complexity, hence chosen for abnormality classification in this work and an algorithm has been devised to use combination of RHOOF and SVM for the detection of atrial septal defect (ASD).
Abstract: In the medical field various motion tracking techniques like block matching, optical flow, and histogram of oriented optical flow (HOOF) are being experimented for the abnormality detection. The information furnished by the existing techniques is inadequate for medical diagnosis. This technique has an inherent drawback, as the entire image is considered for motion vector calculation, increasing the time complexity. Also, the motion vectors of unwanted objects are getting accounted during abnormality detection, leading to misidentification / misdiagnosis. In this research, our main objective is to focus more on the region of abnormality by avoiding the unwanted motion vectors from the rest of the portion of the heart, allowing better time complexity. Proposed a region-based HOOF (RHOOF) for blood motion tracking and estimation; after experimentation, it is observed that RHOOF is four times faster than HOOF. The performance of supervised machine learning techniques was evaluated based on accuracy, precision, sensitivity, specificity, and area under the curve. In the medical field more importance is given to the sensitivity than accuracy. Support vector machine (SVM) has outperformed other technique on sensitivity and time complexity, hence chosen for abnormality classification in this work. An algorithm has been devised to use combination of RHOOF and SVM for the detection of atrial septal defect (ASD).
References
More filters
TL;DR: An anisotropic diffusion filter with a probabilistic-driven memory mechanism to overcome the over-filtering problem by following a tissue selective philosophy is proposed and results both in synthetic and real US images support the inclusion of the Probabilistic memory mechanism for maintaining clinical relevant structures, which are removed by the state-of-the-art filters.
Abstract: Ultrasound (US) imaging exhibits considerable difficulties for medical visual inspection and for development of automatic analysis methods due to speckle, which negatively affects the perception of tissue boundaries and the performance of automatic segmentation methods. With the aim of alleviating the effect of speckle, many filtering techniques are usually considered as a preprocessing step prior to automatic analysis methods or visual inspection. Most of the state-of-the-art filters try to reduce the speckle effect without considering its relevance for the characterization of tissue nature. However, the speckle phenomenon is the inherent response of echo signals in tissues and can provide important features for clinical purposes. This loss of information is even magnified due to the iterative process of some speckle filters, e.g., diffusion filters, which tend to produce over-filtering because of the progressive loss of relevant information for diagnostic purposes during the diffusion process. In this paper, we propose an anisotropic diffusion filter with a probabilistic-driven memory mechanism to overcome the over-filtering problem by following a tissue selective philosophy. In particular, we formulate the memory mechanism as a delay differential equation for the diffusion tensor whose behavior depends on the statistics of the tissues, by accelerating the diffusion process in meaningless regions and including the memory effect in regions where relevant details should be preserved. Results both in synthetic and real US images support the inclusion of the probabilistic memory mechanism for maintaining clinical relevant structures, which are removed by the state-of-the-art filters.
119 citations
"Efficient Speckle Reduction Techniq..." refers background in this paper
...Many filters reduce the speckle noise without taking into account the characteristics and nature of tissues [7]....
[...]
...Where, A(x, y) is corrupted image, b(x, y) is the original image and c(x, y) is the noise [7]....
[...]
...Ultrasound video has speckle noise it is non Gaussian and multiplicative, more difficult to remove than additive, as the noise intensity varies with the image intensity [7]....
[...]
01 Nov 2007
TL;DR: The results of this study show that the integrated system investigated in this study can be successfully used for the automated video segmentation of the CCA plaque in ultrasound videos.
Abstract: In this paper, we propose and evaluate an integrated system for the segmentation of atherosclerotic plaque in ultrasound imaging of the carotid artery based on normalization, speckle reduction filtering, and four different snakes segmentation methods. These methods are the Williams and Shah, Balloon, Lai and Chin, and the gradient vector flow (GVF) snake. The performance of the four different plaque snakes segmentation methods was tested on 80 longitudinal ultrasound images of the carotid artery using receiver operating characteristic (ROC) analysis and the manual delineations of an expert. All four methods were very satisfactory and similar in all measures evaluated, with no significant differences between them; however, the Lai and Chin snakes segmentation method gave slightly better results. Concluding, it is proposed that the integrated system investigated in this study could be used successfully for the automated segmentation of the carotid plaque.
108 citations
TL;DR: The results of this study showed that ultrasound image normalization and speckle reduction filtering are important preprocessing steps favoring image quality, and should be further investigated.
Abstract: Image quality is important when evaluating ultrasound images of the carotid for the assessment of the degree of atherosclerotic disease, or when transferring images through a telemedicine channel, and/or in other image processing tasks. The objective of this study was to investigate the usefulness of image quality evaluation based on image quality metrics and visual perception, in ultrasound imaging of the carotid artery after normalization and speckle reduction filtering. Image quality was evaluated based on statistical and texture features, image quality evaluation metrics, and visual perception evaluation made by two experts. These were computed on 80 longitudinal ultrasound images of the carotid bifurcation recorded from two different ultrasound scanners, the HDI ATL-3000 and the HDI ATL-5000 scanner, before (NF) and after (DS) speckle reduction filtering, after normalization (N), and after normalization and speckle reduction filtering (NDS). The results of this study showed that: (1) the normalized speckle reduction, NDS, images were rated visually better on both scanners; (2) the NDS images showed better statistical and texture analysis results on both scanners; (3) better image quality evaluation results were obtained between the original (NF) and normalized (N) images, i.e. NF-N, for both scanners, followed by the NF-DS images for the ATL HDI-5000 scanner and the NF-DS on the HDI ATL-3000 scanner; (4) the ATL HDI-5000 scanner images have considerable higher entropy than the ATL HDI-3000 scanner and thus more information content. However, based on the visual evaluation by the two experts, both scanners were rated similarly. The above findings are also in agreement with the visual perception evaluation, carried out by the two vascular experts. The results of this study showed that ultrasound image normalization and speckle reduction filtering are important preprocessing steps favoring image quality, and should be further investigated.
108 citations
TL;DR: It is concluded that the optimal method is the OSRAD diffusion filter, capable of strong speckle suppression, increasing the average SNRA of the simulated images by a factor of two, and may be efficiently implemented.
Abstract: In this paper, a detailed description and comparison of speckle reduction of medical ultrasound, and in particular echocardiography, is presented. Fifteen speckle reduction filters are described in a detailed fashion to facilitate implementation for research and evaluation. The filtering techniques considered include anisotropic diffusion, wavelet denoising, and local statistics. Common nomenclature and notation are adopted, to expedite comparison between approaches. Comparison of the filters is based on their application to simulated images, clinical videos, and a computational requirement analysis. The ultrasound simulation method provides a realistic model of the image acquisition process, and permits the use of a noise-free reference image for comparison. Application of objective quality metrics quantifies the preservation of image edges, overall image distortion, and improvement in image contrast. The computational analysis quantifies the number of operations required for each speckle reduction method. A speed-accuracy analysis of discretization methods for anisotropic diffusion is included. It is concluded that the optimal method is the OSRAD diffusion filter. This method is capable of strong speckle suppression, increasing the average SNRA of the simulated images by a factor of two. This method also shows favorable edge preservation and contrast improvement, and may be efficiently implemented.
102 citations
TL;DR: It is proposed that the integrated system investigated in this study could be used successfully for the automated segmentation of the carotid plaque.
Abstract: The robust border identification of atherosclerotic carotid plaque, the corresponding degree of stenosis of the common carotid artery (CCA), and also the characteristics of the arterial wall, including plaque size, composition, and elasticity, have significant clinical relevance for the assessment of future cardiovascular events. To facilitate the follow-up and analysis of the carotid stenosis in serial clinical investigations, we propose and evaluate an integrated system for the segmentation of atherosclerotic carotid plaque in ultrasound videos of the CCA based on video frame normalization, speckle reduction filtering, M-mode state-based identification, parametric active contours, and snake segmentation. Initially, the cardiac cycle in each video is identified and the video M-mode is generated, thus identifying systolic and diastolic states. The video is then segmented for a time period of at least one full cardiac cycle. The algorithm is initialized in the first video frame of the cardiac cycle, with human assistance if needed, and the moving atherosclerotic plaque borders are tracked and segmented in the subsequent frames. Two different initialization methods are investigated in which initial contours are estimated every 20 video frames. In the first initialization method, the initial snake contour is estimated using morphology operators; in the second initialization method, the Chan-Vese active contour model is used. The performance of the algorithm is evaluated on 43 real CCA digitized videos from B-mode longitudinal ultrasound segments and is compared with the manual segmentations of an expert, available every 20 frames in a time span of 3 to 5 s, covering, in general, 2 cardiac cycles. The segmentation results were very satisfactory, according to the expert objective evaluation, for the two different methods investigated, with true-negative fractions (TNF-specificity) of 83.7 ± 7.6% and 84.3 ± 7.5%; true-positive fractions (TPF-sensitivity) of 85.42 ± 8.1% and 86.1 ± 8.0%; and between the ground truth and the proposed segmentation method, kappa indices (KI) of 84.6% and 85.3% and overlap indices of 74.7% and 75.4%. The segmentation contours were also used to compute the cardiac state identification and radial, longitudinal, and shear strain indices for the CCA wall and plaque between the asymptomatic and symptomatic groups were investigated. The results of this study show that the integrated system investigated in this study can be successfully used for the automated video segmentation of the CCA plaque in ultrasound videos.
98 citations
"Efficient Speckle Reduction Techniq..." refers background in this paper
...In papers [11], [15], [17], authors evaluated the results of common carotid using Lsmv, speckle reduction is carried before image normalization to get better quality image....
[...]