基于特征向量的医学视频场景压缩

Hiba Hameed Alshuwaili, A. Ibrahim, Salwa A. Al-agha
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引用次数: 0

摘要

随着技术的发展,数字数据的使用变得容易,因此医疗机构开始转向分析图像和视频内容。医学视频的录制是为了收集有关使用技术的实验信息和由一系列医生与视频记录组成的综合数据,这导致用户与计算机交互。使用编码方法,在计算机的帮助下,对视频进行分析,以获得包含有关患者病情的重要信息的医疗诊断,以及在医疗环境中进行手术以进行临床试验和诊断。这导致了软件开发,减少了医疗工作量实时质量测量和利用现有治疗方法进行患者医疗诊断医疗视频已经成为领先和最常见的领域之一,除了用于诊断心脏和肺部疾病,大脑和消化系统疾病的医学图像之外,这种诊断是通过使用超声和磁共振成像进行的。所有这些都有助于通过新的研究方法和临床实验提供医疗保健。近年来,视频和图像的压缩技术得到了广泛的应用,通过使用这些有效的压缩技术,几乎或不造成损坏,文件大小减小,视觉质量不受影响,压缩的主要目标是使数据以更好的形式保存或传输,各种压缩方式,如无损和有损。本研究的目的是为了压缩视频电影以降低数据的不相关性和冗余性,本研究中使用了主成分分析(PCA),医学视频压缩的主成分分析基于自矩阵分析,使所有读者都能理解PCA方法。本文提出了一种适用于医学视频压缩的主成分分析(PCA)技术,利用特征值和特征向量技术对医学视频帧进行压缩。实验结果表明,该技术处理医学视频帧的效率高、速度快。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Compressing the Scenes of Medical Video by Using Eigenvector
With the technological development, it has become easy to use digital data, and thus medical institutions have turned to analyzing images and video content. Medical video is recorded in order to collect experimental information about the use of technologies and integrated data that consists of a series of doctors with video recording, and this leads to user interaction with computers. Using the coding methodology, the video is analyzed for a medical diagnosis that contains important information about the patient’s condition with the help of computers, as well as performing surgeries in a medical environment to conduct clinical trials and diagnostics. This leads to software development, reduction in medical effort real-time quality measurement and utilization of available treatments for patient medical diagnosis Medical video has become one of the leading and most common fields, in addition to medical images for diagnosing heart and lung diseases, brain and digestive diseases, and this diagnosis is made by using ultrasound and magnetic resonance imaging in addition to a CT scan. All this helped to provide health care through new methods of research and experiments clinical. Lately, Compression technologies developed video and images are widely used, and by using these effective compression techniques, little or no damage is achieved, the file size is reduced and the visual quality is not affected, major target of compression is to enable data to get saved or transmit in the better form types of compression in various manners like the lossless and the lossy. The aim of this study is to compress video film to lower the irrelevance and the redundancy of data, principle component analysis (PCA) is used in this research, the main component analysis of medical video compression based on self-matrix analysis that all readers will be able to understand the PCA method. This paper presents a suitable technique for medical video compression using Principle Component Analysis PCA, compressed the frames of medical video with eigenvalue and eigenvector techniques. Experimental results shown the efficient and quick technique for medical video frames.
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