Monitoring and Analysis of Early Heart Structure of Fetus in Gynecology and Obstetrics Based on Ultrasound Image

2021 ◽  
Vol 11 (3) ◽  
pp. 973-980
Author(s):  
Jingbin Yan ◽  
Birong Liang ◽  
Ying Lv ◽  
Yanbin Li
Keyword(s):  
Fetal Heart ◽  
Heart Function ◽  
Signal To Noise Ratio ◽  
Ultrasound Image ◽  
Detection Algorithm ◽  
Diagnostic Methods ◽  
Image Detection ◽  
Ultrasonic Image ◽  
Structure And Motion ◽  
Gynecology And Obstetrics

As the core organ of cardiovascular system, fetal heart plays a very important role. With the development of pregnancy, the early fetal heart rate tends to mature, and the corresponding cardiac function tends to mature and stable. But the fetal heart is very fragile during pregnancy. Various diseases during pregnancy directly lead to fetal heart growth restriction, and even lead to fetal heart function damage. Ultrasound image diagnosis is one of the most important diagnostic methods in medical imaging. It is of great significance to detect the early heart structure of the fetus in gynecology and obstetrics. It can detect the early fetal heart in real time and noninvasively. However, the traditional ultrasonic image detection has many disadvantages in the process of application, such as many noise points, low performance of processing algorithm, which to some extent affects the detection performance of ultrasonic image detection in the detection of fetal heart structure in gynecology and obstetrics. Based on the above problems, this paper proposes an adaptive detection algorithm of superimposed moving image based on ultrasonic image detection, which can accurately extract and analyze fetal heart region when the signal-to-noise ratio of ultrasonic image sequence is low. The average anisotropy algorithm is also proposed innovatively in this paper. In order to predict the structure of fetal heart more accurately, the active heart model combining fetal heart structure and motion information is considered in the actual analysis process. Experiments show that the accuracy error of the algorithm is less than 11 pixels.

scholarly journals Automatic Detection Technology of Sonar Image Target Based on the Three-Dimensional Imaging

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Wanzeng Kong ◽  
Jinshuai Yu ◽  
Ying Cheng ◽  
Weihua Cong ◽  
Huanhuan Xue
Keyword(s):  
Data Storage ◽  
3D Imaging ◽  
Signal To Noise Ratio ◽  
Three Dimensional ◽  
Automatic Detection ◽  
Classification Model ◽  
Image Detection ◽  
Practical Applications ◽  
Sonar Image ◽  
Detection Technology

With 3D imaging of the multisonar beam and serious interference of image noise, detecting objects based only on manual operation is inefficient and also not conducive to data storage and maintenance. In this paper, a set of sonar image automatic detection technologies based on 3D imaging is developed to satisfy the actual requirements in sonar image detection. Firstly, preprocessing was conducted to alleviate the noise and then the approximate position of object was obtained by calculating the signal-to-noise ratio of each target. Secondly, the separation of water bodies and strata is realized by maximum variance between clusters (OTSU) since there exist obvious differences between these two areas. Thus image segmentation can be easily implemented on both. Finally, the feature extraction is carried out, and the multidimensional Bayesian classification model is established to do classification. Experimental results show that the sonar-image-detection technology can effectively detect the target and meet the requirements of practical applications.

Head Instability Detection for Testing Process in Perpendicular Magnetic Recording System

2013 ◽  
Vol 770 ◽  
pp. 319-322 ◽  
Author(s):  
Piya Kovintavewat ◽  
Santi Koonkarnkhai ◽  
Aimamorn Suvichakorn
Keyword(s):  
Magnetic Recording ◽  
Signal To Noise Ratio ◽  
Disk Drive ◽  
Detection Algorithm ◽  
Recording System ◽  
High Signal ◽  
Signal To Noise ◽  
Perpendicular Magnetic Recording ◽  
Read Head ◽  
Pass Through

During hard disk drive (HDD) testing process, the magneto-resistive read (MR) head is analyzed and checked if the head is defective or not. Baseline popping (BLP) is one of the crucial problems caused by head instability, whose effect can distort the readback signal to the extent of causing possible sector read failure. Without BLP detection algorithm, the defective read head might pass through HDD assembling process, thus producing an unreliable HDD. This situation must be prevented so as to retain customer satisfaction. This paper proposes a simple (but efficient) BLP detection algorithm for perpendicular magnetic recording systems. Results show that the proposed algorithm outperforms the conventional one in terms of both the percentage of detection and the percentage of false alarm, when operating at high signal-to-noise ratio.

Localized Acoustic-Event Measurement Probe: Connector Confirmation Utilizing Acoustic Signatures

2020 ◽  
Author(s):  
Brian Skoglind ◽  
Travis Roberts ◽  
Sourabh Karmakar ◽  
Cameron Turner ◽  
Laine Mears
Keyword(s):  
Background Noise ◽  
Signal To Noise Ratio ◽  
Detection Algorithm ◽  
Consumer Products ◽  
Noise Mitigation ◽  
Acoustic Event ◽  
Electrical Connection ◽  
Acoustic Signatures ◽  
Electrical Connector ◽  
Electrical Connections

Abstract Electrical connections in consumer products are typically made manually rather than through automated assembly systems due to the high variety of connector types and connector positions, and the soft flexible nature of their structures. Manual connections are prone to failure through missed or improper connections in the assembly process and can lead to unexpected downtime and expensive rework. Past approaches for registering connection success such as vision verification or Augmented Reality have shown limited ability to verify correct connection state. However, the feasibility of an acoustic-based verification system for electrical connector confirmation has not been extensively researched. One of the major problems preventing acoustic based verification in a manufacturing or assembly environment is the typically low signal to noise ratio (SNR) between the sound of an electrical connection and the diverse soundscape of the plant. In this study, a physical means of background noise mitigation and signature amplification are investigated in order to increase the SNR between the electrical connection and the plant soundscape in order to improve detection. The concept is that an increase in the SNR will lead to an improvement in the accuracy and robustness of an acoustic event detection and classification system. Digital filtering has been used in the past to deal with low SNRs, however, it runs the risk of filtering out potential important features for classification. A sensor platform is designed to filter out and reduce background noise from the plant without effecting the raw acoustic signal of the electrical connection, and an automated detection algorithm is presented. The solution is over 75% effective at detecting and classifying connections.

Fiducial visibility on planar images during motion-synchronized tomotherapy treatments

2022 ◽  
Author(s):  
William Ferris ◽  
Larry Albert DeWerd ◽  
Wesley S Culberson
Keyword(s):  
Management System ◽  
Signal To Noise Ratio ◽  
Detection Algorithm ◽  
Position Error ◽  
Signal To Noise ◽  
Motion Management ◽  
Future Studies ◽  
Planar Images ◽  
Noise Ratio

Abstract Objective: Synchrony® is a motion management system on the Radixact® that uses planar kV radiographs to locate the target during treatment. The purpose of this work is to quantify the visibility of fiducials on these radiographs. Approach: A custom acrylic slab was machined to hold 8 gold fiducials of various lengths, diameters, and orientations with respect to imaging axis. The slab was placed on the couch at the imaging isocenter and planar radiographs were acquired perpendicular to the custom slab with varying thicknesses of acrylic on each side. Fiducial signal to noise ratio (SNR) and detected fiducial position error in millimeters were quantified. Main Results: The minimum output protocol (100 kVp, 0.8 mAs) was sufficient to detect all fiducials on both Radixact configurations when the thickness of the phantom was 20 cm. However, no fiducials for any protocol were detected when the phantom was 50 cm thick. The algorithm accurately detected fiducials on the image when the SNR was larger than 4. The MV beam was observed to cause RFI artifacts on the kV images and to decrease SNR by an average of 10%. Significance: This work provides the first data on fiducial visibility on kV radiographs from Radixact Synchrony treatments. The Synchrony fiducial detection algorithm was determined to be very accurate when sufficient SNR is achieved. However, a higher output protocol may need to be added for use with larger patients. This work provided groundwork for investigating visibility of fiducial-free solid targets in future studies and provided a direct comparison of fiducial visibility on the two Radixact configurations, which will allow for intercomparison of results between configurations.

Robust PPG Peak Detection Using Dilated Convolutional Neural Networks

2021 ◽  
Author(s):  
Kianoosh Kazemi ◽  
Juho Laitala ◽  
Iman Azimi ◽  
Pasi Liljeberg ◽  
Amir M. Rahmani
Keyword(s):  
Neural Networks ◽  
Heart Rate ◽  
Convolutional Neural Networks ◽  
Signal To Noise Ratio ◽  
Motion Artifact ◽  
Detection Algorithm ◽  
Peak Detection ◽  
Detection Methods ◽  
Method Performance ◽  
Data Generator

<div>Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate and heart rate variability. In the past decades, many methods have been proposed to provide reliable peak detection. These peak detection methods include rule-based algorithms, adaptive thresholds, and signal processing techniques. However, they are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for noise and motion artifact corrupted PPG signals. Our algorithm is based on Convolutional Neural Networks (CNN) with dilated convolutions. Using dilated convolutions provides a large receptive field, making our CNN model robust at time series processing. In this study, we use a dataset collected from wearable devices in health monitoring under free-living conditions. In addition, a data generator is developed for producing noisy PPG data used for training the network. The method performance is compared against other state-of-the-art methods and tested in SNRs ranging from 0 to 45 dB. Our method obtains better accuracy in all the SNRs, compared with the existing adaptive threshold and transform-based methods. The proposed method shows an overall precision, recall, and F1-score 80%, 80%, and 80% in all the SNR ranges. However, these figures for the other methods are below 78%, 77%, and 77%, respectively. The proposed method proves to be accurate for detecting PPG peaks even in the presence of noise.</div>

Theoretical Methods of Images Processing in Optoelectronic Systems

Biometrics ◽  
2017 ◽  
pp. 361-381
Author(s):  
Tatyana Strelkova ◽  
Vladimir Kartashov ◽  
Alexander P. Lytyuga ◽  
Alexander I. Strelkov
Keyword(s):  
Mathematical Model ◽  
Signal To Noise Ratio ◽  
Detection Algorithm ◽  
Optical Signal ◽  
Optoelectronic System ◽  
Output Plane ◽  
Mean Values ◽  
Optical Signals ◽  
Images Processing ◽  
Detection Characteristics

The chapter covers development of mathematical model of signals in output plane of optoelectronic system with registration of optical signals from objects. Analytical forms for mean values and dispersion of signal and interference components of photo receiver response are given. The mathematical model can be used as a base with detection algorithm development for optical signal from objects. An algorithm of signals' detection in output plane of optoelectronic system for the control is offered. The algorithm is synthesized taking into account corpuscular and statistical properties of optical signals. Analytical expressions for mean values and signal and noise components dispersion are cited. These expressions can be used for estimating efficiency of the offered algorithm by the criterion of detection probabilistic characteristics and criterion of signal/noise relation value. The possibility of signal detection characteristics improvement with low signal-to-noise ratio is shown.

An Efficient Block Mode Detection Algorithm for Scalable Video Coding using Probability Model

2015 ◽  
Vol 4 (2) ◽  
pp. 42-55 ◽  
Author(s):  
L. Balaji ◽  
K.K. Thyagharajan ◽  
A. Dhanalakshmi
Keyword(s):  
Signal To Noise Ratio ◽  
Probability Model ◽  
Scalable Video Coding ◽  
Rate Distortion ◽  
Detection Algorithm ◽  
Base Layer ◽  
Scalable Video ◽  
Coding Efficiency ◽  
Macro Block ◽  
Video Model

H.264 / AVC expansion is H.264 / SVC which is applicable in environments that demand video streaming. This paper delivers an algorithm to shorten computational complexity and extend coding efficiency by determining the mode speedily. In this writing, the authors talk a fast mode resolution algorithm with less complexity unlikely the traditional joint scalable video model (JSVM). Their algorithm end mode hunt by a probability model defined. This model is address for both intra-mode and inter-mode predictions of base layer and enhancement layers in a macro block (MB). The estimated rate distortion cost (RDC) for modes among layers is custom to determine the best mode of each MB. The experimental results show that the authors' algorithm realizes 26.9% of encoding time when compared with the JSVM reference software with smallest reduction in peak signal to noise ratio (PSNR).

scholarly journals A Blind Signal Samples Detection Algorithm for Accurate Primary User Traffic Estimation

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4136
Author(s):  
Jakub Nikonowicz ◽  
Aamir Mahmood ◽  
Mikael Gidlund
Keyword(s):  
Signal To Noise Ratio ◽  
Energy Detection ◽  
Low Complexity ◽  
Primary User ◽  
Time Frame ◽  
Detection Algorithm ◽  
Experimental Simulation ◽  
Spectrum Access ◽  
Blind Signal ◽  
Occupancy Estimation

The energy detection process for enabling opportunistic spectrum access in dynamic primary user (PU) scenarios, where PU changes state from active to inactive at random time instances, requires the estimation of several parameters ranging from noise variance and signal-to-noise ratio (SNR) to instantaneous and average PU activity. A prerequisite to parameter estimation is an accurate extraction of the signal and noise samples in a received signal time frame. In this paper, we propose a low-complexity and accurate signal samples detection algorithm as compared to well-known methods, which is also blind to the PU activity distribution. The proposed algorithm is analyzed in a semi-experimental simulation setup for its accuracy and time complexity in recognizing signal and noise samples, and its use in channel occupancy estimation, under varying occupancy and SNR of the PU signal. The results confirm its suitability for acquiring the necessary information on the dynamic behavior of PU, which is otherwise assumed to be known in the literature.

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