International Journal of Engineering Research and Advanced Technology (IJERAT) E-ISSN : 2454-6135 DOI: 10.31695/IJERAT.2020.3643 Volume.6, Issue 8 August -2020 www.ijerat.com Page 76 Licensed Under Creative Commons Attribution CC BY Preprocessing and Feature Extraction for Psoriasis Images Based on Discrete Wavelet Transform Raniah Ali Mustafa 1 Haitham Salman Chyad 2 & Rafid Aedan Haleot 3 1-3 Mustansiriyah University, College of Education, Department of Computer Science Iraq ______________________________________________________________________________________ ABSTRACT This paper, proposes the pre-processing methods use a small neighborhood of a pixel in the input image to get a new brightness value in the output image. Such pre-processing operations are also called filtration. in this paper, we start with the image using a medical case for psoriasis image after change it to gray state implemented under the transform domain (i.e frequency), using wavelet transform then use three filters sharpening, Sobel, and Laplace filter. after make proposed by computing PSNR for each state to show the effect of it. Then extract features through an apply a set of measures (Energy, Entropy, Standard deviation, Variance, Mean) of low low sub-image. The proposed system was implemented on the medical case for psoriasis image dataset, some of them were obtained from the hospitals and the other was obtained from the dataset (Light Field Image of Dataset skin Lesions), available on the Internet and the proposed system implemented in programing language Visual Basic 6.0. Key Words: Psoriasis Images, Wavelet Transform, Haar Basis Filter, Sharpening, Soble, Laplacian. ______________________________________________________________________________________________ 1. INTRODUCTION Wavelet transforms have become one of the most important and powerful tool in image processing, data compression, and signal processing. and any operation that is aimed at modifying the visual impression of image? traditional image sharpening methods usually introduce over shooting along the edges, and do not perform well for sharpening an image resulting from high magnification zooming. the fundamental idea of image sharpening is to add to the input signal a high-pass filtered version of the signal itself. Than a the sobel operator performs a 2-D spatial gradient measurement on an image and so emphasizes regions of high spatial frequency that correspond to edges. typically, it is used to find the opproximate absolute gradient magnitude at each point in an input gray scale image. finally, the laplacian is a 2-D isotropic measure of the 2nd spatial derivative of in image. the laplacian of an image highlights regions of rapid intensity change and is therefore often used for edge detection (see zero crossing edge detectors). the laplacian is often applied to an image that has first been smoothed with something approximating a Gaussian smoothing filter in order to reduce its sensitivity to noice, and hence the two variants will be described together here. the operator normally takes a single gray level image as input and produces another gray level image as output. In this paper an algorithm is invested to preprocessing image to detect or recognize psoriasis place using three types of filters. the rest of this paper start with an overview about psoriasis, DWT section accommodate with brief definition basic filters used section three state related works with our paper [1]. 2. PSORIASIS IMAGES Psoriasis is a chronic skin disease characterized by red patches on the skin, often accompanied by silvery-white scales of dead skin cells. It is not contagious. Psoriasis is believed to be an autoimmune disease, meaning the body’s defense system attack s healthy cells in the body. This creates skin cells that mature and die in less than a week, which is extremely rapid considering normal skill cells mature and replace dead ones usually in a month’s time [ 2].