基于改进图像注册的相位校正方法在超声波图像检测中的优化与应用分析

IF 3 4区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Nannan Lu, Hongyan Shu
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引用次数: 0

摘要

为了预防和检测各种疾病,包括脑部、胸部、消化系统、泌尿生殖系统和心血管系统的疾病,超声波技术对于评估生理数据和组织形态至关重要。它能够提供实时、高频扫描,是一种方便的非侵入性诊断工具。然而,病人的移动和人为失误造成的探头抖动等问题会产生大量干扰,导致检测结果不准确。图像配准技术可以帮助定位和消除不必要的干扰,同时保留关键数据。尽管 Matlab 中已经有了改进这些技术的研究,但目前还没有专门用于消除干扰的系统,而且这一过程仍然非常耗时,尤其是在处理大量超声波图像时。相位相关技术可将图像转换到频域,使噪声抑制变得更容易,是目前使用的最有效的算法之一,因为它能承受噪声的影响。然而,利用这种技术识别血管壁超声图像中位移的研究还很少。为了填补这些空白,这项研究提出了一种使用相位相关算法的图像配准系统。该系统提供旋转、缩放配准、图片平移、血管壁位移检测以及干扰去除功能。此外,该系统还包括批处理功能,以提高多张超声图像的配准效率。通过高效的干扰管理和简化的配准,该方法为提高超声波诊断的精确度和有效性提供了可行的途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimization and Application Analysis of Phase Correction Method Based on Improved Image Registration in Ultrasonic Image Detection

In order to prevent and detect a wide range of disorders, including those of the brain, thoracic, digestive, urogenital, and cardiovascular systems, ultrasound technology is essential for assessing physiological data and tissue morphology. Its capacity to deliver real-time, high-frequency scans makes it a handy and non-invasive diagnostic tool. However, issues like patient movements and probe jitter from human error can provide a large amount of interference, resulting in inaccurate test findings. Techniques for image registration can assist in locating and eliminating unwanted interference while maintaining crucial data. Even though there has been research on improving these techniques in Matlab, there are no specialized systems for interference removal, and the procedure is still time-consuming, particularly when working with huge quantities of ultrasound images. The phase correlation technique, which converts images into the frequency domain and makes noise suppression easier, is one of the most efficient algorithms now in use since it can tolerate noise with resilience. Nevertheless, little research has been done on using this technique to identify displacement in blood vessel wall ultrasound images. To address these gaps, this work presents an image registration system that uses the phase correlation algorithm. The system provides rotation, zoom registration, picture translation, and displacement detection of the vessel wall in addition to interference removal. Furthermore, batch processing is included to increase the effectiveness of registering multiple ultrasound pictures. Through efficient interference management and streamlined registration, this method offers a workable way to improve the precision and efficacy of ultrasonic diagnostics.

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来源期刊
International Journal of Imaging Systems and Technology
International Journal of Imaging Systems and Technology 工程技术-成像科学与照相技术
CiteScore
6.90
自引率
6.10%
发文量
138
审稿时长
3 months
期刊介绍: The International Journal of Imaging Systems and Technology (IMA) is a forum for the exchange of ideas and results relevant to imaging systems, including imaging physics and informatics. The journal covers all imaging modalities in humans and animals. IMA accepts technically sound and scientifically rigorous research in the interdisciplinary field of imaging, including relevant algorithmic research and hardware and software development, and their applications relevant to medical research. The journal provides a platform to publish original research in structural and functional imaging. The journal is also open to imaging studies of the human body and on animals that describe novel diagnostic imaging and analyses methods. Technical, theoretical, and clinical research in both normal and clinical populations is encouraged. Submissions describing methods, software, databases, replication studies as well as negative results are also considered. The scope of the journal includes, but is not limited to, the following in the context of biomedical research: Imaging and neuro-imaging modalities: structural MRI, functional MRI, PET, SPECT, CT, ultrasound, EEG, MEG, NIRS etc.; Neuromodulation and brain stimulation techniques such as TMS and tDCS; Software and hardware for imaging, especially related to human and animal health; Image segmentation in normal and clinical populations; Pattern analysis and classification using machine learning techniques; Computational modeling and analysis; Brain connectivity and connectomics; Systems-level characterization of brain function; Neural networks and neurorobotics; Computer vision, based on human/animal physiology; Brain-computer interface (BCI) technology; Big data, databasing and data mining.
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