scholarly journals The Biopsy of Boar Testes Using Ultrasonographic Examination

2014 ◽  
Vol 37 (1) ◽  
pp. 69-74
Author(s):  
Laima Liepa ◽  
Zigmunds Bruveris ◽  
Mara Mangale ◽  
Ilmars Duritis ◽  
Vita Antane ◽  
...  
Keyword(s):  
Reproductive System ◽  
Needle Biopsy ◽  
Ultrasound Guidance ◽  
Ultrasound Image ◽  
Testicular Biopsy ◽  
Skin Incision ◽  
Ultrasound Images ◽  
Live Animal ◽  
Ultrasonographic Examination

Abstract The biopsy of live animal testes is an important clinical manipulation to control spermatogenesis and reproductive system pathologies. The aim was to develop a method of boar testes biopsy using a biopsy gun with ultrasound guidance and to investigate the influence of this procedure on the boar testes parenchyma and quality of ejaculate. The biopsy was carried out in six 8-month-old boars. Fourteen days prior to and 21 days after biopsy, the quality of ejaculate was examined (weight of ejaculate; concentration and motility of spermatozoa) with a seven-day intervals. Ultrasound images of the testes parenchyma were recorded three times: directly before and 15 minutes after the biopsy, then 21 days after the procedure. The testes biopsies of generally anesthetized boars were performed with the biopsy gun for needle biopsy with a 12cm long, disposable 16-gauge needle 1.8mm in diameter (Vitesse) through 1cm skin incision in the depth of 1.2-1.6cm of parenchyma. Fifteen minutes after the biopsy, macroscopic injures of the parenchyma of all the boar testes were not detected in the ultrasound image. Twenty one days after biopsy, the hyperechogenic line 0.1-0.2cm in diameter was seen in the testes parenchyma of six boars in the depth of 1.2-1.6cm. The biopsy of boar testes did not influence the quality of boars ejaculate. The ultrasonographic examination of boar testicles before the biopsy reduced possibilities to traumatize large blood vessels of the testes. A perfect boar testicular biopsy was easy to perform using ultrasonographic examination in the pigsty conditions.

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).

scholarly journals Early detection of joint abnormalities from ultrasound images

2018 ◽  
Vol 7 (2.25) ◽  
pp. 105
Author(s):  
R J. Hemalatha ◽  
Dr V. Vijaybaskar ◽  
A Josephin Arockia Dhivya ◽  
. .
Keyword(s):  
Early Detection ◽  
Ultrasound Image ◽  
Computer Software ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Optimization Approach ◽  
Ultrasound Images ◽  
Processing Techniques ◽  
Modern Image

Musculoskeletal ultrasound is effective for the early detection of joint abnormalities like erosion, effusion, synovitis and inflammation. Computer software is developed for segmentation of joint ultrasound image to diagnose the defect. The objective of developing this paper is to achieve early diagnosis of joint disorders by segmentation of ultrasound image with different algorithms. Ultrasound machine with high resolution probe can be used for development & findings of joints by the orthopaedician, rheumatologist and sports physician. These find-ings are done by processing the ultrasound images of patient joint using modern image processing techniques. Therefore algorithms has been designed and developed for analysis of medical images that is musculo ultrasound image based on optimization approach, using genet-ic algorithm and PSO algorithm. To improve the better quality of the image many improvisation techniques have been introduced. Hence, these algorithms perform better segmentation and identification of joint abnormalities. The analysis of ultrasound image is directly based on image segmentation steps, pre-processing, filtering, feature extraction and analysis of these extracted features by finding the output using different optimization techniques. In proposed method, efforts have been made to exhibit the procedure for finding and segmenting the mus-culoskeletal images of abnormal joints. The present approaches are segmentation operation on ultrasound images by applying genetic and PSO algorithm. The comparison between these algorithms is done, such that the algorithm itself analyses the whole image and perform the segmentation and detection of abnormalities perfectly   

scholarly journals Empirical Analysis of Deep Convolutional Generative Adversarial Network for Ultrasound Image Synthesis

2021 ◽  
Vol 15 (1) ◽  
pp. 71-77
Author(s):  
Dheeraj Kumar ◽  
Mayuri A. Mehta ◽  
Indranath Chatterjee
Keyword(s):  
Empirical Analysis ◽  
Ultrasound Imaging ◽  
Ultrasound Image ◽  
Generative Adversarial Networks ◽  
Ultrasound Images ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Synthetic Images

Introduction: Recent research on Generative Adversarial Networks (GANs) in the biomedical field has proven the effectiveness in generating synthetic images of different modalities. Ultrasound imaging is one of the primary imaging modalities for diagnosis in the medical domain. In this paper, we present an empirical analysis of the state-of-the-art Deep Convolutional Generative Adversarial Network (DCGAN) for generating synthetic ultrasound images. Aims: This work aims to explore the utilization of deep convolutional generative adversarial networks for the synthesis of ultrasound images and to leverage its capabilities. Background: Ultrasound imaging plays a vital role in healthcare for timely diagnosis and treatment. Increasing interest in automated medical image analysis for precise diagnosis has expanded the demand for a large number of ultrasound images. Generative adversarial networks have been proven beneficial for increasing the size of data by generating synthetic images. Objective: Our main purpose in generating synthetic ultrasound images is to produce a sufficient amount of ultrasound images with varying representations of a disease. Methods: DCGAN has been used to generate synthetic ultrasound images. It is trained on two ultrasound image datasets, namely, the common carotid artery dataset and nerve dataset, which are publicly available on Signal Processing Lab and Kaggle, respectively. Results: Results show that good quality synthetic ultrasound images are generated within 100 epochs of training of DCGAN. The quality of synthetic ultrasound images is evaluated using Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM). We have also presented some visual representations of the slices of generated images for qualitative comparison. Conclusion: Our empirical analysis reveals that synthetic ultrasound image generation using DCGAN is an efficient approach. Other: In future work, we plan to compare the quality of images generated through other adversarial methods such as conditional GAN, progressive GAN.

scholarly journals Using the method of renormalization with limitation for medical ultrasound image processing.

2020 ◽  
Vol 2020 (10) ◽  
Author(s):  
A.V. Kokoshkin ◽  
Keyword(s):  
Image Processing ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Bilateral Filtering ◽  
Ultrasound Image Processing ◽  
Images Processing

This article proposes the application of the technique of combining the modernized method of renormalization with limitation with subsequent bilateral filtering to improve the quality of medical ultrasound images. Processing according to this algorithm increases the overall contrast of the image, smoothes out speckle noise and makes it possible to cope well with determining the localization of significant objects. The presented results indicate a significant improvement in the quality of ultrasound images, which can serve as an auxiliary tool for medical workers when clarifying the diagnosis.

scholarly journals Refinement for Ocular Ultrasound Images Quality by Utilizing Combination of Enhancement Techniques

2016 ◽  
Vol 9 (3) ◽  
pp. 37
Author(s):  
Zinah Rajab Hussein ◽  
Zaid Rajab Hussein
Keyword(s):  
Wavelet Coefficient ◽  
Low Cost ◽  
Ultrasound Image ◽  
Mapping Function ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Diagnostic Modality ◽  
Ocular Ultrasound ◽  
Wide Availability

Ultrasound has been used as a diagnostic modality for many intraocular diseases, due its safety, low cost, real time and wide availability. Unfortunately, ultrasound images suffer from speckle artifact that are tissue dependent. In this work, we will offer a method to reduce speckle noise and improve ultrasound image to raise the human diagnostic performance. This method combined undecimated wavelet transform with a wavelet coefficient mapping function: where UDWT used to eliminate the noise and a wavelet coefficient mapping function used to enhance the contrast of denoised images obtained from the first component. This methods can be used not only as a means for improving visual quality of medical images but also as a preprocessing module for computer-aided detection/diagnosis systems to improve the performance of screening and detecting regions of interest in images. The proposed method is experimentally evaluated via 60 ultrasound images of eye. It is demonstrated that the proposed method can further improve the image quality of ocular ultrasound; the results reveal the effectiveness and superiority of the proposed method.

scholarly journals Compressed Sensing Image Reconstruction of Ultrasound Image for Treatment of Early Traumatic Myositis Ossificans of Elbow Joint by Electroacupuncture

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yi Zhu ◽  
Mengyuan Sheng ◽  
Yuanming Ouyang ◽  
Lichang Zhong ◽  
Kun Liu ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Heterotopic Ossification ◽  
Ultrasound Imaging ◽  
External Fixator ◽  
Elbow Joint ◽  
Acoustic Scattering ◽  
Ultrasound Image ◽  
Ultrasound Images ◽  
Image Library

This article conducts a retrospective analysis of 500 patients with posttraumatic elbow dysfunction admitted to our department from March 2019 to September 2020. The average time from injury to operation is 11 months (2–20 months). We adopt a personalized treatment method to completely remove the hyperplastic adhesion tissue and heterotopic ossification around the joint, remove part of the joint capsule and ligament, and release it to achieve maximum function. After the operation, an external fixator was used to stabilize the loosened elbow joint, and the patient was guided to perform rehabilitation exercises with the aid of a hinged external fixator, and celecoxib was used to prevent heterotopic ossification. Mayo functional scoring system was used to evaluate the curative effect before and after surgery. The rapid realization of ultrasound imaging under the framework of compressed sensing is studied. Under the premise of ensuring the quality of ultrasound imaging reconstruction, the theory of ultrasound imaging is improved, and a plane wave acoustic scattering ultrasound echo model is established. On this basis, the theory of compressed sensing is introduced, the mathematical model of compressed sensing reconstruction is established, and the fast iterative shrinkage thresholding algorithm (FISTA) of compressed sensing reconstruction is improved to reduce the computational complexity and the number of iterations. This article uses FISTA directly to reconstruct medical ultrasound images, and the reconstruction results are not ideal. Therefore, a simulation model of FISTA training and testing was established using the standard image library. By adding different intensities of noise to all images in the image library, the influence of noise intensity on the quality of FISTA reconstructed images is analyzed, and it is found that the FISTA model has requirements for the quality of the images to be reconstructed and the training set images. In this paper, Rob’s blind deconvolution restoration algorithm is used to preprocess the original ultrasound image. The clarity of the texture details of the restored ultrasound image is significantly improved, and the image quality is improved, which meets the above requirements. This paper finally formed a reconstruction model suitable for ultrasound images. The reconstruction strategy verified by the ultrasound images provided by the Institute of Ultrasound Imaging of a medical university has achieved a significant improvement in the quality of ultrasound images.

Denoising Ultrasound Medical Images

2015 ◽  
Vol 5 (1) ◽  
pp. 1-14
Author(s):  
A.S.C.S.Sastry ◽  
P.V.V.Kishore MIEE ◽  
Ch.Raghava Prasad ◽  
M.V.D.Prasad
Keyword(s):  
Image Quality ◽  
Medical Images ◽  
Ultrasound Image ◽  
Similarity Index ◽  
Speckle Noise ◽  
Inverse Transformation ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Soft Thresholding

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).

scholarly journals Development and assessment of a telesonography system for musculoskeletal imaging

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Mohammed Obaid ◽  
Qianwei Zhang ◽  
Scott J. Adams ◽  
Reza Fotouhi ◽  
Haron Obaid
Keyword(s):  
Degrees Of Freedom ◽  
Ultrasound Images ◽  
Ultrasound Probe ◽  
Rural And Remote ◽  
Significant Delay ◽  
Remote Communities ◽  
Internet Connection ◽  
Good For ◽  
4 Degrees Of Freedom

Abstract Background Telesonography systems have been developed to overcome barriers to accessing diagnostic ultrasound for patients in rural and remote communities. However, most previous telesonography systems have been designed for performing only abdominal and obstetrical exams. In this paper, we describe the development and assessment of a musculoskeletal (MSK) telesonography system. Methods We developed a 4-degrees-of-freedom (DOF) robot to manipulate an ultrasound probe. The robot was remotely controlled by a radiologist operating a joystick at the master site. The telesonography system was used to scan participants’ forearms, and all participants were conventionally scanned for comparison. Participants and radiologists were surveyed regarding their experience. Images from both scanning methods were independently assessed by an MSK radiologist. Results All ten ultrasound exams were successfully performed using our developed MSK telesonography system, with no significant delay in movement. The duration (mean ± standard deviation) of telerobotic and conventional exams was 4.6 ± 0.9 and 1.4 ± 0.5 min, respectively (p = 0.039). An MSK radiologist rated quality of real-time ultrasound images transmitted over an internet connection as “very good” for all telesonography exams, and participants rated communication with the radiologist as “very good” or “good” for all exams. Visualisation of anatomic structures was similar between telerobotic and conventional methods, with no statistically significant differences. Conclusions The MSK telesonography system developed in this study is feasible for performing soft tissue ultrasound exams. The advancement of this system may allow MSK ultrasound exams to be performed over long distances, increasing access to ultrasound for patients in rural and remote communities.

Automatic Measurement of Pennation Angle from Ultrasound Images using Resnets

2021 ◽  
Vol 43 (2) ◽  
pp. 74-87
Author(s):  
Weimin Zheng ◽  
Shangkun Liu ◽  
Qing-Wei Chai ◽  
Jeng-Shyang Pan ◽  
Shu-Chuan Chu
Keyword(s):  
Muscle Fibers ◽  
Ultrasound Image ◽  
Automatic Measurement ◽  
Pennation Angle ◽  
Reference Line ◽  
Ultrasound Images ◽  
Single Image ◽  
Angle Measuring ◽  
Sequential Image ◽  
Robust Measurements

In this study, an automatic pennation angle measuring approach based on deep learning is proposed. Firstly, the Local Radon Transform (LRT) is used to detect the superficial and deep aponeuroses on the ultrasound image. Secondly, a reference line are introduced between the deep and superficial aponeuroses to assist the detection of the orientation of muscle fibers. The Deep Residual Networks (Resnets) are used to judge the relative orientation of the reference line and muscle fibers. Then, reference line is revised until the line is parallel to the orientation of the muscle fibers. Finally, the pennation angle is obtained according to the direction of the detected aponeuroses and the muscle fibers. The angle detected by our proposed method differs by about 1° from the angle manually labeled. With a CPU, the average inference time for a single image of the muscle fibers with the proposed method is around 1.6 s, compared to 0.47 s for one of the image of a sequential image sequence. Experimental results show that the proposed method can achieve accurate and robust measurements of pennation angle.

274 Comparison of Two Ultrasound Systems for Estimation of Carcass Attributes in Feedlot Steers

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 142-143
Author(s):  
Noah P Jesko ◽  
Thomas L Perkins ◽  
Ty E Lawrence ◽  
John Richeson ◽  
Charles Looney
Keyword(s):  
Ultrasound Images ◽  
Marbling Score ◽  
The West ◽  
Live Animal ◽  
West Texas ◽  
Feedlot Steers ◽  
A Value ◽  
Animal Data ◽  
Ribeye Area ◽  
Highly Correlated

Abstract Sixty-eight (68) crossbred steers were evaluated using two real-time, B-mode ultrasound units to estimate final carcass attributes. The cattle were ultrasounded at the West Texas A&M research feedlot (Canyon, Texas) and carcass data was collected at the West Texas A&M Meats Laboratory (Canyon, Texas) or Tyson Fresh Meats (Amarillo, Texas). Objectives of the study were 1) to compare ultrasound live animal data to carcass attributes at harvest 2) to evaluate the performance differences between the ALOKA 500 (ALK) and the EVO ultrasound units All ultrasound images were captured by the same Ultrasound Guidelines Council (UGC) certified technician with images being processed at the UltraInsights Laboratory (Pierce, Colorado). The correlations between 12th rib fat thickness of the carcass (FTC) and ultrasound (FTU) were 0.84 for the ALK and 0.85 for the EVO, with no differences being found between the two units (P = 0.15). Correlations between the 12-13th rib carcass ribeye area (REAC) and ultrasound ribeye area (REAU) were 0.69 for the ALK and 0.66 for the EVO. There was no difference in REAU size between the two units. Carcass marbling score (MS) and ultrasound intramuscular fat (IMFU) correlations were 0.78 for the ALK and 0.84 for the EVO. The IMF data were found to be different between the two units, with the EVO measuring a mean IMFU value of 6.03 and the ALK a value of 5.26 with the change of 0.77 being different (P < 0.01). It is concluded that both ultrasound units performed favorably when predicting FTC and marbling score but were not highly correlated for REAU. It was found that the predictions between the two units on FT and REA were the same, though the EVO was higher on both. The IMFU values between the ALK and EVO were different, with the EVO again predicting higher values.

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