scholarly journals Body Navigation-loaded Ultrasound Acquisition Technology: a Pilot Comparison With Conventional Ultrasound

2021 ◽  
Author(s):  
Ki Choon Sim ◽  
Beom Jin Park ◽  
Byunjun Kim ◽  
Yeo Eun Han ◽  
Na Yeon Han ◽  
...  
Keyword(s):  
Imaging System ◽  
Ultrasound Image ◽  
Abdominal Ultrasound ◽  
The Body ◽  
Rank Test ◽  
Ultrasound Images ◽  
Rater Agreement ◽  
Kappa Test ◽  
Institutional Review ◽  
Conventional Ultrasound

Abstract Background: To investigate the usefulness of body navigation-loaded ultrasound including a real time transducer location and the inspection site compared with conventional ultrasound images.Methods: Under the approval of institutional review board, we prospectively enrolled total 29 healthy adult volunteers. One gastrointestinal radiologist performed abdominal ultrasound simultaneously using Ultrasound Navigation Image Convergence System developed by researchers. Subsequently, an equivalent conventional ultrasound image set was prepared. Three radiologists independently evaluated the two ultrasound image sets regarding the recognition of the target organ (2-points), the transducer location (2-points), and the transducer orientation (1-point). At intervals of two-weeks, conventional ultrasound images were analyzed first, and body navigation-loaded images were later analyzed. The score differences between the first and second evaluations were compared using the Wilcoxon signed rank test. Inter-rater agreement of three reviewers was obtained by the Fleiss’ Kappa test.Results: A total of 1402 navigation-loaded ultrasound images were obtained. Ultrasound operator carefully selected a total of 203 images for analysis. In all three reviewers, the interpretation score of each evaluation was significantly increased in the second analysis using the body navigation-loaded ultrasound image (in reviewer A, from 4.07±1.56 to 4.79±0.69 points; in reviewer B, from 3.83±1.59 to 4.49±0.88 points; in reviewer C, from 3.43±1.60 to 4.19±1.01 points; P<.0001). The inter-rater agreement of each evaluation also increased significantly in the second analysis using the body navigation-loaded ultrasound image (P<.0001).Conclusion: The body navigation-loaded ultrasound imaging system allows other medical staffs to easily and accurately interpret ultrasound images.

scholarly journals Segmentation of Ultrasound Abdominal Images to extract Region of Interest

2019 ◽  
Vol 8 (2) ◽  
pp. 5480-5483
Keyword(s):  
Performance Test ◽  
Ultrasound Image ◽  
Region Of Interest ◽  
Abdominal Ultrasound ◽  
Disease Diagnosis ◽  
The Body ◽  
Ultrasound Images ◽  
Contour Segmentation ◽  
Pull Out ◽  
Kidney Region

Ultrasound imaging is one of the safest techniques for disease diagnosis which can be used in any part of the body. One of the major reason for using ultrasound images is the cost when compared with MRI, PET etc. Further, it is free from any radiation exposure and is an efficient technique for initial diagnosis. This paper concentrates on segmentation of kidney from abdominal ultrasound images. There are many common ailments affecting kidney and hence conducting study on this segmented image becomes easy with an efficient segmentation technique. Various algorithms to pull out kidney regions from abdominal ultrasound images which are discussed by many researchers are also investigated in this paper. One of the major drawback of ultrasound image is that due to the complicated internal organs of the abdominal region, extraction of only kidney region is very challenging. This paper proposes a new technique where the collected abdominal ultrasound image is cleaned, to remove unwanted noise produced due to various interferences. After applying the filtering technique, kidney region is segmented. This extracted kidney image is subjected to Region indicator contour segmentation method to extract the renal calculi which is the region of interest in this study. The method is experimented with a reasonable number of dataset and applied the statistical performance test to check for the accuracy.

scholarly journals A Classification Algorithm of Abdominal Ultrasound Images in Medical Practice for Secondary Uses

2010 ◽  
Vol 14 (2) ◽  
pp. 128-134 ◽  
Author(s):  
Yutaka Hatakeyama ◽  
◽  
Hiromi Kataoka ◽  
Noriaki Nakajima ◽  
Teruaki Watabe ◽  
...  
Keyword(s):  
Medical Practice ◽  
Abdominal Ultrasound ◽  
Classification Algorithm ◽  
The Body ◽  
Ultrasound Images ◽  
Secondary Use ◽  
Abdominal Organs ◽  
Target Organs ◽  
Liver Region ◽  
Real Test

A classification algorithm for abdominal organs in ultrasonic test images based on the operator’s knowledge is proposed. This is in order to use the medical images included in medical charts for secondary uses, e.g., medical data analysis. It makes a correlation between target organs in test images and search unit information on the body mark region. In the central region of abdominal images, target organs are uniquely determined through recognition of the liver region and in consideration of the location of the diaphragm. A classification experiment, done using 600,000 real test images taken at the Kochi Medical School Hospital from 2004 to 2008, was carried out to evaluate the performance of the proposed system in terms of accuracy rate of detection of the body mark region and diaphragm region. The proposed algorithm constitutes an essential classification system for the secondary use of a large database of ultrasound images taken in the course of medical practice.

Segmentation Methods in Ultrasound Images

2011 ◽  
pp. 377-390
Author(s):  
Farhang Sahba
Keyword(s):  
Image Segmentation ◽  
Automatic Segmentation ◽  
Transrectal Ultrasound ◽  
Ultrasound Image ◽  
The Body ◽  
Ultrasound Images ◽  
Sound Waves ◽  
Medical Test ◽  
Segmentation Methods ◽  
Segmentation Task

Ultrasound imaging now has widespread clinical use. It involves exposing a part of the body to highfrequency sound waves in order to generate images of the inside of the body. Because it is a real-time procedure, the ultrasound images show the movement of the body’s internal structure as well. It is usually a painless medical test and its procedures seem to be safe. Despite recent improvement in the quality of information from an ultrasound device, these images are still a challenging case for segmentation. Thus, there is much interest in understanding how to apply an image segmentation task to ultrasound data and any improvements in this regard are desirable. Many methods have been introduced in existing literature to facilitate more accurate automatic or semi-automatic segmentation of ultrasound images. This chapter is a basic review of the works on ultrasound image segmentation classified by application areas, including segmentation of prostate transrectal ultrasound (TRUS), breast ultrasound, and intravascular ultrasound (IVUS) images.

Segmentation Methods in Ultrasound Images

2009 ◽  
pp. 473-487
Author(s):  
Farhang Sahba
Keyword(s):  
Image Segmentation ◽  
Automatic Segmentation ◽  
Transrectal Ultrasound ◽  
Ultrasound Image ◽  
The Body ◽  
Ultrasound Images ◽  
Sound Waves ◽  
Medical Test ◽  
Segmentation Methods ◽  
Segmentation Task

Ultrasound imaging now has widespread clinical use. It involves exposing a part of the body to highfrequency sound waves in order to generate images of the inside of the body. Because it is a real-time procedure, the ultrasound images show the movement of the body’s internal structure as well. It is usually a painless medical test and its procedures seem to be safe. Despite recent improvement in the quality of information from an ultrasound device, these images are still a challenging case for segmentation. Thus, there is much interest in understanding how to apply an image segmentation task to ultrasound data and any improvements in this regard are desirable. Many methods have been introduced in existing literature to facilitate more accurate automatic or semi-automatic segmentation of ultrasound images. This chapter is a basic review of the works on ultrasound image segmentation classified by application areas, including segmentation of prostate transrectal ultrasound (TRUS), breast ultrasound, and intravascular ultrasound (IVUS) images.

Texture Analysis and Classification of Coronary Plaque in Intravascular Ultrasound

1997 ◽  
Vol 3 (S2) ◽  
pp. 265-266
Author(s):  
K. J. Dixon ◽  
D. G. Vince ◽  
R. M. Cothren ◽  
J. F. Cornhill
Keyword(s):  
Intravascular Ultrasound ◽  
Coronary Arteries ◽  
Imaging System ◽  
Plaque Rupture ◽  
Ultrasound Image ◽  
Diagnostic Technique ◽  
Coronary Plaque ◽  
Arterial Disease ◽  
Plaque Morphology ◽  
Ultrasound Images

Atherosclerosis is a degenerative arterial disease that leads to the gradual blockage of vessels due to plaque formation or acute ischaemic events such as plaque rupture. A thorough understanding of plaque morphology is necessary in the determination of factors underlying coronary artery disease. Intravascular ultrasound (IVUS) represents a diagnostic technique that provides tomographic visualization of coronary arteries. Ultrasound reflects sound at the interfaces between media of different acoustic refractive indices theoretically implying that various components within an ultrasound image should be distinguishable. The aim of this study is to classify plaque lesions using advanced digital image processing into the following categories: adventitia, media, fibrous, necrotic core, and calcified. Examination of plaque composition can yield valuable information necessary in determining the appropriate preventative and mechanical interventions.Diseased samples were obtained from excised human coronary arteries at autopsy. Intravascular ultrasound images were acquired using an HP SONOS clinical IVUS imaging system and 3.5 French 30 MHz catheters.

scholarly journals Ultrasound Image Denoising using Multiscale Ridgelet Transform with Hard and NeighCoeff Thresholding

2013 ◽  
pp. 150-155
Author(s):  
Preeti Goel ◽  
H. P. Sinha ◽  
Harpreet Singh
Keyword(s):  
Adaptive Filters ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
The Body ◽  
Speckle Reduction ◽  
Ultrasound Images ◽  
Sound Waves ◽  
Edge Preservation ◽  
Ridgelet Transform ◽  
Visual Enhancement

Ultrasound imaging utilizes sound waves reflected from different organs of the body to give local details and important diagnostic information on the human body. However, using ultrasound images for diagnosis is difficult because of the existence of speckle noise in the image. The speckle noise is due to interference between coherent waves which are backscattered by targeted surfaces and arrive out of phase at the sensor. This hampers the perception and the extraction of fine details from the image. Speckle reduction/filtering i.e. visual enhancement techniques are used for enhancing the visual quality of the images. The multscale ridgelet transform based denoising algorithm for Ultrasound images is proposed for effective edge preservation in comparison to filtering techniques using the Adaptive Filters.

scholarly journals SEGMENTATION OF KIDNEY STONE REGION IN ULTRA SOUND IMAGEBY USING REGION PARTITION AND MOUNTING SEGMENTATION ALGORITHM (RPM)

2021 ◽  
Vol 9 (1) ◽  
pp. 512-518
Author(s):  
S. Suganyadevi, Dr. M. Renukadevi
Keyword(s):  
Ultrasound Imaging ◽  
Performance Test ◽  
Abdominal Ultrasound ◽  
Disease Diagnosis ◽  
The Body ◽  
Ultra Sound ◽  
Ultrasound Images ◽  
Contour Segmentation ◽  
Pull Out ◽  
Kidney Region

One of the safest techniques for disease diagnosis which can be used in any part of the body is ultrasound imaging. The cost when compared with MRI, PET etc are higher than using ultra sound images is the one of the major reason. Further, it is an efficient technique for initial diagnosis and it is free from any radiation exposure. This paper concentrates on segmentation of kidney from abdominal ultrasound images. There are many common ailments affecting kidney. Hence conducting study on this segmented image becomes easy with an efficient segmentation technique. In this paper Various algorithms to pull out kidney regions from abdominal ultrasound images which are discussed by many researchers are also investigated. Due to the complicated internal organs of the abdominal region, extraction of only the kidney region is very challenging and is the major drawback of ultrasound imaging. a new technique where the collected abdominal ultrasound image  is cleaned, to remove unwanted noise produced due to various interferences has been processed by this paper, the kidney region is segmented after applying the filtering technique. the subjected to Region indicator contour segmentation method to extract the renal calculi which is the region of interest in this study is this extracted kidney image. with a  reasonable number of dataset and applied the statistical performance test to check for the accuracy , the method is experimented .

Performance Evaluation of Adaptive Imaging Based on Multiphase Apodization with Cross-correlation: A Pilot Study in Abdominal Ultrasound

2018 ◽  
Vol 40 (4) ◽  
pp. 195-214
Author(s):  
Junseob Shin ◽  
Yu Chen ◽  
Harshawn Malhi ◽  
Frank Chen ◽  
Jesse Yen
Keyword(s):  
Cross Correlation ◽  
Human Subjects ◽  
Vena Cava ◽  
Ultrasound Image ◽  
Abdominal Ultrasound ◽  
Image Contrast ◽  
Ultrasound Images ◽  
Adaptive Imaging ◽  
Weighting Matrix

Degradation of image contrast caused by phase aberration, off-axis clutter, and reverberation clutter remains one of the most important problems in abdominal ultrasound imaging. Multiphase apodization with cross-correlation (MPAX) is a novel beamforming technique that enhances ultrasound image contrast by adaptively suppressing unwanted acoustic clutter. MPAX employs multiple pairs of complementary sinusoidal phase apodizations to intentionally introduce grating lobes that can be used to derive a weighting matrix, which mostly preserves the on-axis signals from tissue but reduces acoustic clutter contributions when multiplied with the beamformed radio-frequency (RF) signals. In this paper, in vivo performance of the MPAX technique was evaluated in abdominal ultrasound using data sets obtained from 10 human subjects referred for abdominal ultrasound at the USC Keck School of Medicine. Improvement in image contrast was quantified, first, by the contrast-to-noise ratio (CNR) and, second, by the rating of two experienced radiologists. The MPAX technique was evaluated for longitudinal and transverse views of the abdominal aorta, the inferior vena cava, the gallbladder, and the portal vein. Our in vivo results and analyses demonstrate the feasibility of the MPAX technique in enhancing image contrast in abdominal ultrasound and show potential for creating high contrast ultrasound images with improved target detectability and diagnostic confidence.

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.

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