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
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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
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TL;DR: A robust pre-processing methodology and segmentation approach based on unsupervised Markov Random Field (MRF) model is combined to highlight the sonographic marker for VSD screening from the 2 dimensional ultrasound images.
10 citations
01 Oct 2014
TL;DR: Clinical significance of using speckles to segment is determined by validating on 32 images of the thyroid gland by measuring the overlap with the Ground Truth segmentation obtained from two expert doctors using Dice coefficient as the overlap measure.
Abstract: Contrary to the popular belief of treating speckle related pixels as noise and filtering an ultrasound image for speckle noise removal, the practical importance and use of these pixels in performing a multi-organ segmentation of the thyroid gland is studied in this research work. In this work, speckle related pixels are classified into three echogenic levels and then used to segment an ultrasound image of the thyroid gland into the trachea, carotid, muscles and thyroid. Novel techniques are introduced to estimate the anterior boundaries of the thyroid gland using low pass filtered intensity gradients of the hyperechoic speckle pixels in transverse and longitudinal ultrasound scans, respectively. An energy functional similar to active contour models is defined to segment that carotid artery using hypoechoic speckle pixels. The proposed technique was executed on 88 images of the thyroid gland. Clinical significance of using speckles to segment is determined by validating on 32 images of the thyroid gland by measuring the overlap with the Ground Truth segmentation obtained from two expert doctors using Dice coefficient as the overlap measure.
10 citations
01 Jan 2014
TL;DR: In this article, the authors evaluate and compare different denoising filters based on requirements in medical science according to their applications,merits and demerits, and give the analysis of selection of proper filter according to the required parameters for best result.
Abstract: It is well-known that noise degrades the visual evaluation in ultrasound imaging. Speckle noise is the most common form of noise present in US images.Fast, portable, inexpensive andcapable of real time imaging, but unfortunately, however, accurateultrasound images, lack useful conclusions from the images due to noise degradations. In this paper we are evaluating and comparing the different denoising filters based on requirements in medical science according to their applications,merits and demerits.This review paper gives the analysis of selection of proper filter according to the required parameters for best result and at the same time their comparative study enhances the selection of proper filter as per requirement.
9 citations
Proceedings Article•
11 Mar 2015TL;DR: A comparative study of despeckle filters for ultrasound images have been presented in this paper, done in terms of preserving the texture features and edges.
Abstract: A comparative study of despeckle filters for ultrasound images have been presented in this paper. We know that the ultrasound images are corrupted by speckle noise, which has limited the growth of automatic diagnosis for ultrasound images. This paper compiles twelve despeckling filters for speckle noise reduction. A comparative study has been done in terms of preserving the texture features and edges. Six stabilized evaluation metrics, namely, signal to noise ratio (SNR), root mean square error (RMSE), peak signal to noise ratio (PSNR), structural similarity (SSIM) index, beta metric (β) and figure of merit (FoM) are calculated to investigate the performance of the despeckle filters.
8 citations
Journal Article•
TL;DR: In this paper, a number of filters such as Lee Filter, Frost Filter, Kuan Filter, Weiner Filter, Median Filter and SRAD (Speckle Reducing Anistrophic Diffusion) Filter are applied to the following images such as Photographic, Ultrasound, SAR, PET, CT and MRI.
Abstract: An Image is often corrupted by noise in its acquisition and transmission. Noise is any undesired information that contaminates an image. Speckle or Multiplicative noise, is a signal-dependent form of noise, whose magnitude is related to the value of the original pixel. This tends to reduce the image resolution and contrast. Denoising speckle is one of the most important process to increase the quality of the image. Many filters are widely used to improve the quality of images by despeckling it. This work comprises a number of filters such as Lee Filter, Frost Filter, Kuan Filter, Weiner Filter, Median Filter and SRAD(Speckle Reducing Anistrophic Diffusion) Filter that are applied to the following images such as Photographic, Ultrasound, SAR, PET, CT and MRI. The Statistical measures such as Signal to Noise Ratio (SNR), Peak signal to noise ratio (PSNR), Structural Similarity Index (SSIM), Mean Square Error (MSE), Root Mean Square Error (RMSE) are used to calculate the filtered images and the results are tabulated. These parameters are used to analyze image quality of the filtered images. The results obtained from the statistical measures are used for comparative study and also used to determine the filter name that is well suited for a particular type of image. The results obtained are presented in the form of statistical tables and graphs. Finally the best filter has been proposed based on the statistical and experimental results.
5 citations
"Efficient Speckle Reduction Techniq..." refers background in this paper
...Linear [32] Non Linear [32] Non Data Adaptive [32] Data Adaptive [32] International Conference on Current Trends in Computer, Electrical, Electronics and Communication (ICCTCEEC-2017)...
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...Spatial Domain [32] Frequency/ Transform Domain [32]...
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