Tıbbi video kompresyonu için temel bileşen analizi

With the advancement of technology, 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 research methods and clinical trials. In recent times, compression techniques have developed both video and images on a large scale and using this effective compression techniques, little or no damage is achieved, the file size is reduced and the visual quality is not affected. The main goal of compression is to enable data to be better transferred or preserved for different types of compression, such as loss and non-loss of data. The aim of this study is to compress a video to reduce data redundancy and inconvenience)Principle Component Analysis (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 (Principle Component Analysis ) method .This paper presents suitable technique for medical video compression using( Principle Component Analysis) compressed the frames of medical video with eigenvalue and eigenvector techniques. Experimental results showed the efficient and quick technique for medical video frames.