Improved Adaptive Wavelet Thresholding for Effective Speckle Noise Reduction in Low Contrast Medical Images

2019 ◽  
Vol 28 (10) ◽  
pp. 1950176 ◽  
Author(s):  
P. Sreelatha ◽  
M. Ezhilarasi
Keyword(s):  
Contrast Enhancement ◽  
Speckle Noise ◽  
Noise Removal ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Gamma Correction ◽  
Significant Information ◽  
Adaptive Wavelet ◽  
Low Contrast ◽  
Image Contrast Enhancement

Informative images endure from poor contrast and noise during image acquisition. Significant information retrieval necessitates image contrast enhancement and removal of noise as a prerequisite before any further processing can be done. Dominant applications with low contrast images affected by speckle noise are medical ultrasound images. The objective of this work is to improve the effectiveness of the preprocessing stage in medical ultrasound images by enhancing the image while retaining its structural characteristics. For image enhancement, this work proposes to develop an automatic contrast enhancement technique using cumulative histogram equalization and gamma correction based on the image. For noise removal, this work proposes an algorithm Gamma Correction with Exponentially Adaptive Threshold (GCEAT) which suggests the use of GC for contrast enhancement along with a new wavelet-based adaptive soft thresholding technique for noise removal. The proposed GCEAT-based image de-noising is validated with other enhancement and noise removal techniques. Experimental results with low contrast synthetic and actual ultrasound images show that the suggested proposed system performs better than existing contrast enhancement techniques. Encouraging results were obtained with medical ultrasound images in terms of Peak-Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Structural Similarity Index Measure (SSIM) and Average Intensity (AI).

scholarly journals A Novel Speckle Noise Removal Algorithm Based on ADMM and Energy Minimization Method

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Bo Chen ◽  
Yan Lv ◽  
Jinbin Zou ◽  
Wensheng Chen ◽  
Binbin Pan
Keyword(s):  
Energy Minimization ◽  
Speckle Noise ◽  
Noise Removal ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Minimization Method ◽  
Alternating Direction ◽  
Step Effect ◽  
New Energy ◽  
Series Of Experiments

Speckle noise removal in medical ultrasound images is a challenging task. In this paper, a new model is proposed to removal speckle noise, alternating direction method of multipliers algorithm is employed to solve the new energy minimization model. The convexity, existence, and uniqueness of the new energy minimization model’s solution are proved. Series of experiments are designed in this paper. Numerical results show that the new algorithm can reduce the step effect effectively obtain good results in visual effect and quantitative measures by comparing with some traditional models.

scholarly journals MFNR: Multi-Frame Method for Complete Speckle Noise Removal in Ultrasound images Using KDE

2020 ◽  
Author(s):  
Awais Nazir ◽  
Muhammad Shahzad Younis ◽  
Muhammad Khurram Shahzad
Keyword(s):  
Signal To Noise Ratio ◽  
Low Cost ◽  
Speckle Noise ◽  
Noise Removal ◽  
General Purpose ◽  
Imaging Systems ◽  
Ultrasound Images ◽  
Frame Method ◽  
Probability Density Function Pdf ◽  
Removal System

Speckle noise is one of the most difficult noises to remove especially in medical applications. It is a nuisance in ultrasound imaging systems which is used in about half of all medical screening systems. Thus, noise removal is an important step in these systems, thereby creating reliable, automated, and potentially low cost systems. Herein, a generalized approach MFNR (Multi-Frame Noise Removal) is used, which is a complete Noise Removal system using KDE (Kernal Density Estimation). Any given type of noise can be removed if its probability density function (PDF) is known. Herein, we extracted the PDF parameters using KDE. Noise removal and detail preservation are not contrary to each other as the case in single-frame noise removal methods. Our results showed practically complete noise removal using MFNR algorithm compared to standard noise removal tools. The Peak Signal to Noise Ratio (PSNR) performance was used as a comparison metric. This paper is an extension to our previous paper where MFNR Algorithm was showed as a general purpose complete noise removal tool for all types of noises

scholarly journals Denoising of Medical Ultrasound Images using Wavelet Transform with Bilateral Filter

2019 ◽  
Vol 8 (4) ◽  
pp. 8113-8116
Keyword(s):  
Wavelet Transform ◽  
Bilateral Filter ◽  
Intrinsic Noise ◽  
Speckle Noise ◽  
Computer Assisted ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Edge Preservation ◽  
Neighborhood Relationship ◽  
Speckle Filtering

Medical image degradation contains a significant impact on image quality and therefore affects the human interpretation and also the accuracy of computer assisted diagnostics techniques, unfortunately ultrasound images are principally degraded by an intrinsic noise known as speckle noise. Therefore, de- speckle filtering may be pre-processing step in medical ultrasound images. During this paper we propose a new image de-noising technique is the combination of bilateral filter and wavelet transform. The main contribution of this paper is within the use of a new neighborhood relationship to develop a new multi-scale bilateral filter. Experimental outcomes validate the usefulness and also the correctness of the proposed filter in edge preservation and speckle noise reduction for medical ultrasound images.

Denoising Ultrasound Medical Images

2017 ◽  
pp. 761-775
Author(s):  
A.S.C.S. Sastry ◽  
P.V.V. Kishore ◽  
Ch. Raghava Prasad ◽  
M.V.D. Prasad
Keyword(s):  
Image Quality ◽  
Medical Images ◽  
Ultrasound Image ◽  
Similarity Index ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Soft Thresholding ◽  
Block Based

Medical ultrasound imaging has revolutioned the diagnostics of human body in the last few decades. The major drawback of ultrasound medical images is speckle noise. Speckle noise in ultrasound images is because of multiple reflections of ultrasound waves from hard tissues. Speckle noise degrades the medical ultrasound images lessening the visible quality of the image. The aim of this paper is to improve the image quality of ultrasound medical images by applying block based hard and soft thresholding on wavelet coefficients. Medical ultrasound image transformation to wavelet domain uses debauchee's mother wavelet. Divide the approximate and detailed coefficients into uniform blocks of size 8×8, 16×16, 32×32 and 64×64. Hard and soft thresholding on these blocks of approximate and detailed coefficients reduces speckle noise. Inverse transformation to original spatial domain produces a noise reduced ultrasound image. Experiments on medical ultrasound images obtained from diagnostic centers in Vijayawada, India show good improvements to ultrasound images visually. Quality of improved images in measured using peak signal to noise ratio (PSNR), image quality index (IQI), structural similarity index (SSIM).

Modified Distance Regularized Level Set Segmentation Based Analysis for Kidney Stone Detection

2015 ◽  
Vol 2 (2) ◽  
pp. 24-41 ◽  
Author(s):  
K. Viswanath ◽  
R. Gunasundari
Keyword(s):  
Energy Level ◽  
Level Set ◽  
High Speed ◽  
Gabor Filter ◽  
Energy Levels ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Low Contrast ◽  
Level Set Function ◽  
Level Set Segmentation

The abnormalities of the kidney can be identified by ultrasound imaging. The kidney may have structural abnormalities like kidney swelling, change in its position and appearance. Kidney abnormality may also arise due to the formation of stones, cysts, cancerous cells, congenital anomalies, blockage of urine etc. For surgical operations it is very important to identify the exact and accurate location of stone in the kidney. The ultrasound images are of low contrast and contain speckle noise. This makes the detection of kidney abnormalities rather challenging task. Thus preprocessing of ultrasound images is carried out to remove speckle noise. In preprocessing, first image restoration is done to reduce speckle noise then it is applied to Gabor filter for smoothening. Next the resultant image is enhanced using histogram equalization. The preprocessed ultrasound image is segmented using distance regularized level set segmentation (DR-LSS), since it yields better results. It uses a two-step splitting methods to iteratively solve the DR-LSS equation, first step is iterating LSS equation, and then solving the Sign distance equation. The second step is to regularize the level set function which is the obtained from first step for better stability. The DR is included for LSS for eliminating of anti-leakages on image boundary. The DR-LSS does not require any expensive re-initialization and it is very high speed of operation. The RD-LSS results are compared with distance regularized level set evolution DRLSE1, DRLSE2 and DRLSE3. Extracted region of the kidney after segmentation is applied to Symlets (Sym12), Biorthogonal (bio3.7, bio3.9 & bio4.4) and Daubechies (Db12) lifting scheme wavelet subbands to extract energy levels. These energy level gives an indication about presence of stone in that particular location which significantly vary from that of normal energy level. These energy levels are trained by Multilayer Perceptron (MLP) and Back Propagation (BP) ANN to identify the type of stone with an accuracy of 98.6%.

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