Abstract

Abstract
This paper tackles with one of the multivariate methods in digital image processing, namely (Principal Component Analysis) (PCA) to extract useful information and Texture Enhancement by reducing the dimensions of images. A Multivariate Microscope Image of cell nucleus of wheat and satellite image of Erbil was used. It was concluded that, (PCA) can show information on(components and nucleus cell ... etc.)and information on (changes developments in land cover, buildings and forests of the city), the first component interpreted (97.67%)(99.73),respectively of the total variance, giving clearer image of characteristics and information with a (2.33%) (0.23%) loss (noise).The loading values of PC'S assisted in clarifying the distribution of image elements (pixels) so that elements with similar characteristics appear homogeneous cluster, that enables to identify and determine regions of interest (ROI), this supports specialists to increase the understanding and interpretation of the image.
Key Word: Principal Component Analysis (PCA), Image Processing, Texture Enhancement, Region of Interest in Image (ROI).