Used Eigenvalues and Eigenvectors to Compress the Image

This article is a detailed review and explanation of the field of linear algebra in which all mathematical concepts and structures concerned with linear algebra. The main objective of the paper is to highlight the significant applications of linear algebra in the field of medical engineering. Principal component analysis is one of the most important compression and feature extraction algorithms used in engineering (Santo, 2012). It mainly depends on the calculation and extraction of eigenvalues and eigenvectors which are then used to represent an input; whether it's the image or a simple matrix. In this article, the use of principal component analysis for medical image compression is an important and novel application of linear algebra (Rajendra, 1996).