Study of Fetal Anatomy using Ultrasound Images

2014 ◽  
Vol 3 (2) ◽  
pp. 1-13 ◽  
Author(s):  
Sandeep Kumar E. ◽  
N. Sriraam
Keyword(s):  
Image Analysis ◽  
Ultrasound Imaging ◽  
Information Source ◽  
Ultrasound Image ◽  
Background Information ◽  
Ultrasound Images ◽  
Medical Field ◽  
Fetal Ultrasound ◽  
Medical Background ◽  
Major Emphasis

Ultrasound imaging has widespread applications in medical field, especially for the study and assessment of fetus in the womb of pregnant woman. This paper highlights the background information regarding basics of ultrasound imaging, the various anatomical terminologies related to human body and fetus, and various steps to identify the organs of fetus using ultrasound images with major emphasis on the fetus heart detection. The paper serves an information source for any engineering person from non-medical background to carry out his/ her research in fetal ultrasound image analysis.

Postnatal ultrasound follow-up in neonates with prenatal hydronephrosis

Diagnosis ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Elham Kebriyaei ◽  
Ali Davoodi ◽  
Seyed Alinaghi Kazemi ◽  
Zahra Bazargani
Keyword(s):  
Ultrasound Imaging ◽  
Maternal Education ◽  
Left Kidney ◽  
Ultrasound Images ◽  
Fetal Ultrasound ◽  
Renal Anomalies ◽  
Cross Sectional ◽  
Fetal Hydronephrosis ◽  
Kidney Involvement

Abstract Objectives Renal anomalies are the most common fetal abnormalities that occur during prenatal development, and are typically detected by observing hydronephrosis on fetal ultrasound imaging. Follow-up with post-natal ultrasound is important to detect clinically-important obstruction, because many of the pre-natal abnormalities resolve spontaneously. This study aimed to evaluate the postnatal hydronephrosis follow-up rate, and reasons for non follow-up in affected neonates. Methods In this cross-sectional study all neonates born during a period of one year at Ayatollah Mousavi Hospital with hydronephrosis on fetal ultrasound imaging were recruited. All mothers were also given face-to-face information about fetal hydronephrosis and its postnatal outcomes, and follow-up with at least a postnatal ultrasound was recommended from the fourth day of their neonates’ birth until the end of the fourth week. The neonates were subsequently observed for one month to determine the postnatal ultrasound follow-up rate and to reflect on diagnostic test results, reasons for failure to follow-up, as well as causes of hydronephrosis. Results In this study, 71 cases (1.2%) out of 5,952 neonates had fetal hydronephrosis on prenatal ultrasound images. The postnatal ultrasound imaging showed kidney involvement in 18 neonates (25%), particularly in the left kidney (61.1%). Seven neonates had no follow-up at one month (10%). No significant relationship was found between lack of follow-up and the neonates’ place of residence (p=0.42), maternal education (p=0.90), number of siblings (p=0.33), or gender (p=0.64). Conclusions Postnatal ultrasound follow-up rate in these neonates with a history of fetal hydronephrosis was incomplete even though parents had been provided with education and advice at their birth time. Accordingly, it is recommended to perform postnatal ultrasound once neonates are discharged from hospitals.

scholarly journals An Ultrasound Imaging-Guided Robotic HIFU Ablation Experimental System and Accuracy Evaluations

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Chih Yu An ◽  
Jia Hao Syu ◽  
Ching Shiow Tseng ◽  
Chih-Ju Chang
Keyword(s):  
Standard Deviation ◽  
Ultrasound Imaging ◽  
Focused Ultrasound ◽  
Ultrasound Image ◽  
Experimental System ◽  
High Intensity Focused Ultrasound ◽  
Ultrasound Images ◽  
Tumor Treatment ◽  
Positioning Control ◽  
Hifu Ablation

In recent years, noninvasive thermal treatment by using high-intensity focused ultrasound (HIFU) has high potential in tumor treatment. The goal of this research is to develop an ultrasound imaging-guided robotic HIFU ablation system for tumor treatment. The system integrates the technologies of ultrasound image-assisted guidance, robotic positioning control, and HIFU treatment planning. With the assistance of ultrasound image guidance technology, the tumor size and location can be determined from ultrasound images as well as the robotic arm can be controlled to position the HIFU transducer to focus on the target tumor. After the development of the system, several experiments were conducted to measure the positioning accuracy of this system. The results show that the average positioning error is 1.01 mm with a standard deviation 0.34, and HIFU ablation accuracy is 1.32 mm with a standard deviation 0.58, which means this system is confirmed with its possibility and accuracy.

scholarly journals Ultrasound Medical Images Classification Based on Deep Learning Algorithms: A Review

2021 ◽  
pp. 29-42
Author(s):  
admin admin ◽  
◽  
◽  
Adnan Mohsin Abdulazeez
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Ultrasound Image ◽  
Smart Devices ◽  
Learning Approach ◽  
Ultrasound Images ◽  
Medical Field ◽  
Modern Methods

With the development of technology and smart devices in the medical field, the computer system has become an essential part of this development to learn devices in the medical field. One of the learning methods is deep learning (DL), which is a branch of machine learning (ML). The deep learning approach has been used in this field because it is one of the modern methods of obtaining accurate results through its algorithms, and among these algorithms that are used in this field are convolutional neural networks (CNN) and recurrent neural networks (RNN). In this paper we reviewed what have researchers have done in their researches to solve fetal problems, then summarize and carefully discuss the applications in different tasks identified for segmentation and classification of ultrasound images. Finally, this study discussed the potential challenges and directions for applying deep learning in ultrasound image analysis.

scholarly journals Aestheticization of Ultrasound Images

2021 ◽  
Author(s):  
Prasanta Pal ◽  
David R. Vago ◽  
Amardip Ghosh ◽  
Judson Brewer
Keyword(s):  
Imaging Modality ◽  
Imaging Techniques ◽  
Ultrasound Image ◽  
Significant Loss ◽  
Imaging Method ◽  
Ultrasound Images ◽  
Fetal Ultrasound ◽  
Visual Aesthetics ◽  
Scientific Accuracy ◽  
Aesthetic Values

<div> <div> <div> <p>Ultrasound imaging is one of the most versatile imaging method in order to observe inner workings of human- body. Due to its simplicity, cost-effectiveness, easy availability and portability, a diverse set of applications are influenced by this very popular imaging modality. Despite its popularity as one of the most widely used imaging techniques, it has some serious limitations including lack of image clarity as well as complete absence of any visual aesthetics. Although, commonplace data filters can potentially make ultrasound images smoother looking, however, there is a significant loss of information introduced by the smoothing filters. In this article, we developed a method to enhance the image clarity as well as a protocol for enhancing image aesthetics for ultrasound modality using modern data-curation tool SOCKS. We performed few case studies using various color schemas applied on a publicly available fetal ultrasound image. The outlined technique can be easily generalized to any other kind of ultrasound images. We hypothesize that, our method would not only provide us with enhanced scientific accuracy, visual clarity of ultrasound images but also add additional layers of visual clarity coupled with artistic and aesthetic values. Our method calls for an complete rethinking of how we present ultrasound images </p> </div> </div> </div>

QUANTITATIVE ASSESSMENT OF GINGIVAL INFLAMMATION USING HIGH-RESOLUTION ULTRASOUNDEX-VIVO

2018 ◽  
Vol 18 (03) ◽  
pp. 1850012 ◽  
Author(s):  
AHMED M. SAYED ◽  
RACHEL LAMARCK ◽  
ELISA CRUZ ◽  
EROS CHAVES ◽  
OSAMA M. MUKDADI
Keyword(s):  
High Resolution ◽  
Ultrasound Imaging ◽  
Ex Vivo ◽  
Ultrasound Image ◽  
Strong Relationship ◽  
Ultrasound Images ◽  
Gingival Inflammation ◽  
Cross Sectional ◽  
Diagnostic Potential ◽  
Image Pairs

This study investigates the feasibility of using high-resolution ultrasound imaging echogenicity to quantitatively diagnose gingival inflammation. Gingival samples were extracted from the study participants during gingivectomy procedures. Ultrasound mechanical scanning of the samples was immediately conducted ex-vivo to render cross-sectional images of high resolution, at different locations. Samples’ histological preparation and analysis was followed after performing ultrasound imaging. Histological sections were then matched with ultrasound images at different sections for each gingival sample. The matched image pairs were used to estimate two quantitative measures; relative inflammation area and ultrasound image echogenicity. These parameters were employed to judge the diagnostic potential of gingival ultrasound imaging. The results show that ultrasound images exhibited low intensity levels at the inflamed gingival regions, while healthy layers showed higher intensity levels. The relative area parameter implied a strong relationship between ultrasound and histological images. Ultrasound echogenicity was found to be statistically significant in differentiating between some inflammation degrees in the studied gingival samples. In summary, ultrasound imaging has the potential to be a noninvasive adjunct diagnostic tool for gingival inflammation, and may help assess the stage of the disease and ultimately limit periodontal disease occurrence; taking into consideration the limits of this study.

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 Aestheticization of Ultrasound Images

2021 ◽  
Author(s):  
Prasanta Pal ◽  
David R. Vago ◽  
Amardip Ghosh ◽  
Judson Brewer
Keyword(s):  
Imaging Modality ◽  
Imaging Techniques ◽  
Ultrasound Image ◽  
Significant Loss ◽  
Imaging Method ◽  
Ultrasound Images ◽  
Fetal Ultrasound ◽  
Visual Aesthetics ◽  
Scientific Accuracy ◽  
Aesthetic Values

<div> <div> <div> <p>Ultrasound imaging is one of the most versatile imaging method in order to observe inner workings of human- body. Due to its simplicity, cost-effectiveness, easy availability and portability, a diverse set of applications are influenced by this very popular imaging modality. Despite its popularity as one of the most widely used imaging techniques, it has some serious limitations including lack of image clarity as well as complete absence of any visual aesthetics. Although, commonplace data filters can potentially make ultrasound images smoother looking, however, there is a significant loss of information introduced by the smoothing filters. In this article, we developed a method to enhance the image clarity as well as a protocol for enhancing image aesthetics for ultrasound modality using modern data-curation tool SOCKS. We performed few case studies using various color schemas applied on a publicly available fetal ultrasound image. The outlined technique can be easily generalized to any other kind of ultrasound images. We hypothesize that, our method would not only provide us with enhanced scientific accuracy, visual clarity of ultrasound images but also add additional layers of visual clarity coupled with artistic and aesthetic values. Our method calls for an complete rethinking of how we present ultrasound images </p> </div> </div> </div>

Ultrasonic monitoring of decompression procedures

1984 ◽  
Vol 304 (1118) ◽  
pp. 153-175 ◽  
Keyword(s):  
Image Analysis ◽  
Early Detection ◽  
Ultrasound Imaging ◽  
Decompression Sickness ◽  
Bubble Formation ◽  
Correct Prediction ◽  
Ultrasound Images ◽  
Ultrasonic Monitoring ◽  
Peripheral Tissues ◽  
Time Integral

Early detection of bubbles may provide clues to the mechanism of their formation, and a knowledge of their extent during a decompression may allow the prevention of decompression sickness. We have used ultrasound imaging to study bubble formation in peripheral tissues. The results suggest that: ( a ) a threshold supersaturation for bubble formation exists; ( b ) the earliest bubbles are intravascular; ( c ) before signs of decompression sickness a substantial accumulation of stationary bubbles occurs. Despite the success of Doppler methods in detecting moving bubbles after decompressions normally considered safe, recent studies have shown that the correlation between number of bubbles detected and symptoms of decompression sickness is often poor. We have used a time integral of the ultrasound images, which avoids laborious image analysis, to follow the extent of both moving and stationary bubbles. Human trials involving a wide variety of decompressions suggest that correct prediction of symptoms is possible.

scholarly journals Noise diminution in Fetal Ultrasound Pictures

2017 ◽  
Vol 4 (2) ◽  
pp. 5-8
Author(s):  
Michael R ◽  
Ibrahim M
Keyword(s):  
Wavelet Transform ◽  
Ultrasound Imaging ◽  
Input Image ◽  
The Other ◽  
Ultrasound Images ◽  
Fetal Ultrasound ◽  
Undecimated Wavelet Transform ◽  
Directional Filter ◽  
Undecimated Wavelet

This study offers an inventive way to decrease the stain sound which linked with the ultrasound imaging. This in revolve leads to humanizing the input image and raise the human analytical presentation. It uses an Undecimated Wavelet Transform UWT to decay the image signal to the coefficients, to take the oblique coefficient and applied Independent Directional Mask after replacing the value of each pixel in this coefficient with the comparable pixel in the innovative image, this result yields after applying middle filter in container the value of the pixel is smaller than the entrance value which is pre-specified. We suitable the Directional filter and transmit the design of the new oblique coefficient, and then make the converse wavelet renovate to form the resulting image after dropping the sound of it. The method showed a clear enhancement on numerous ultrasound images via conventional arithmetical measurements and outperformed the other methods which used in common to diminish the noise.

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