scholarly journals EXTRACTION OF FETAL FEATURES FROM B MODE ULTRASONOGRAMS FOR EFFICIENT DIAGNOSIS OF DOWN SYNDROME IN FIRST AND SECOND TRIMESTER

2019 ◽  
Vol 12 (3) ◽  
pp. 1135-1139
Author(s):  
O. Jeba Shiney ◽  
J. Amar Pratap Singh ◽  
Priestly Shan B
Keyword(s):  
Down Syndrome ◽  
Wavelet Transform ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Morphological Operations ◽  
Second Trimester ◽  
Otsu’S Method ◽  
Averaging Filter ◽  
Occurrence Matrix ◽  
Random Nature

Segmentation of ultrasound images has been found to be a tedious task due to the presence of speckle and other artifacts. The random nature of the multiplicative speckle noise and lack of demarcation of information in ultrasound images makes the segmentation a highly complex one. In this paper a modified watershed based method has been proposed for segmentation of features from Ultrasound images towards efficient diagnosis of Down Syndrome in first and second trimester. The pixels are grouped based on the pixel differences and the co- occurrence matrix is formed based on the energy and contrast. If global scheme is adopted for segmentation the high frequency edges may appear as artifacts. Hence to overcome this the wavelet transform of co-occurrence matrix is obtained and each decomposed band is subjected to an averaging filter. The process bands are thresholded using Otsu’s method and a binary image is obtained. The isolated pixels are removed by using suitable morphological operations. Then inverse wavelet transform is performed to obtain the image skeleton. The resultant image is subjected to watershed segmentation using gradients. Using the above mentioned approach we can see that the regions of interest are clearly segmented and is producing reproducible results.

scholarly journals Deep Learning-Based Wavelet Threshold Function Optimization on Noise Reduction in Ultrasound Images

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhuxiang Shen ◽  
Wei Li ◽  
Hui Han
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
Similarity Index ◽  
Speckle Noise ◽  
Disease Diagnosis ◽  
Function Optimization ◽  
Threshold Function ◽  
Ultrasound Images ◽  
Better Than

To explore the utilization of the convolutional neural network (CNN) and wavelet transform in ultrasonic image denoising and the influence of the optimized wavelet threshold function (WTF) algorithm on image denoising, in this exploration, first, the imaging principle of ultrasound images is studied. Due to the limitation of the principle of ultrasound imaging, the inherent speckle noise will seriously affect the quality of ultrasound images. The denoising principle of the WTF based on the wavelet transform is analyzed. Based on the traditional threshold function algorithm, the optimized WTF algorithm is proposed and applied to the simulation experiment of ultrasound images. By comparing quantitatively and qualitatively with the traditional threshold function algorithm, the advantages of the optimized WTF algorithm are analyzed. The results suggest that the image is denoised by the optimized WTF. The mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM) of the images are 20.796 dB, 34.294 dB, and 0.672 dB, respectively. The denoising effect is better than the traditional threshold function. It can denoise the image to the maximum extent without losing the image information. In addition, in this exploration, the optimized function is applied to the actual medical image processing, and the ultrasound images of arteries and kidneys are denoised separately. It is found that the quality of the denoised image is better than that of the original image, and the extraction of effective information is more accurate. In summary, the optimized WTF algorithm can not only remove a lot of noise but also obtain better visual effect. It has important value in assisting doctors in disease diagnosis, so it can be widely applied in clinics.

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.

scholarly journals Research on ultrasound image of interventional catheterization processing method based on wavelet transform and fuzzy theory

Filomat ◽  
2020 ◽  
Vol 34 (15) ◽  
pp. 5187-5194
Author(s):  
Chenyang Liang ◽  
Ning He
Keyword(s):  
Wavelet Transform ◽  
Fuzzy Theory ◽  
Ultrasound Image ◽  
Low Frequency ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Wavelet Coefficients ◽  
Image Processing Algorithm ◽  
Exponential Transformation ◽  
Interventional Catheterization

Interventional catheterization can help patients to accurately assess the condition, early diagnosis and intervention. Confirming the location of catheter by ultrasound has the advantages of real-time imaging, non-invasive, radiative, fast and convenient. Due to speckle noise and similar acoustic impedance, ultrasound images are not clear. In this paper an ultrasonic image processing algorithm based on wavelet transform and fuzzy theory is proposed. First, logarithmic transformation of ultrasound images is used to convert multiplicative noise into additive noise. Then the wavelet coefficients of the image are obtained by multiscale wavelet transform. The high frequency wavelet coefficients of the image are denoised by thresholding, and the low-frequency wavelet coefficients of the image are processed by fuzzy enhancement. Finally, the processed image is obtained through wavelet reconstruction and exponential transformation. Experiments show that this proposed method can effectively improve the visual effect of images.

scholarly journals Segmentation of Lymph Nodes in Ultrasound Images using U-Net Convolutional Neural Networks and Gabor-based Anisotropic Diffusion

2021 ◽  
Author(s):  
Haobo Chen ◽  
Yuqun Wang ◽  
Jie Shi ◽  
Jingyu Xiong ◽  
Jianwei Jiang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Lymph Node ◽  
Lymph Nodes ◽  
Convolutional Neural Networks ◽  
Anisotropic Diffusion ◽  
Speckle Noise ◽  
Accurate Method ◽  
Ultrasound Images ◽  
Morphological Operations ◽  
Sensitivity Specificity

Abstract Objective Automated segmentation of lymph nodes (LNs) in ultrasound images is a challenging task mainly due to the presence of speckle noise and echogenic hila. In this paper, we propose a fully automatic and accurate method for LN segmentation in ultrasound. Methods The proposed segmentation method integrates diffusion-based despeckling, U-Net convolutional neural networks and morphological operations. Firstly, we suppress speckle noise and enhance lymph node edges using the Gabor-based anisotropic diffusion (GAD). Secondly, a modified U-Net model is proposed to segment LNs excluding echogenic hila. Finally, morphological operations are adopted to segment entire LNs by filling the regions of echogenic hila.Results A total of 531 lymph nodes from 526 patients were included to evaluate the proposed method. Quantitative metrics of segmentation performance, including the accuracy, sensitivity, specificity, Jaccard similarity and Dice coefficient, reached 0.934, 0.939, 0.937, 0.763 and 0.865, respectively.Conclusion The proposed method automatically and accurately segments LNs in ultrasound, which may assist artificially intelligent diagnosis of lymph node diseases.

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