Indonesian Journal of Electrical Engineering and Computer Science Vol. 24, No. 3, December 2021, pp. 1654~1662 ISSN: 2502-4752, DOI: 10.11591/ijeecs.v24.i3.pp1654-1662  1654 Journal homepage: http://ijeecs.iaescore.com Analysis the digital images by using morphology operators Hadeel Amjed Saeed, Sumaya Hamad, Azmi Tawfik Hussain Department of Computer Science, College of Computer Science and Information Technology, University of Anbar, Anbar, Iraq Article Info ABSTRACT Article history: Received Jun 24, 2021 Revised Oct 15, 2021 Accepted Oct 27, 2021 In this paper, we deal with morphology images that try to improve the use of images. On the one hand, the process is used to obtain the histogram of the image then converted it into a non-color image (gray scale). The next step is to perform the erosion, dilation, open and close operations on the images, how these methods have important effects, and how can be used on a variable number of images, and found the differences between them. These operations were applied on four different images, check images, four basic operations (dilation, erosion, open and close) for each image were performed. Then, retrieving process to the original state of the image (the colored copy) was applied. The results found that retrieving the original images is difficult, and there is the occurrence of some noises on the image when it was retrieved. Finally, conclusions of the work are presented. Keywords: Close Dilation Erosion Mathematical morphology Morphological operation Open This is an open access article under the CC BY-SA license. Corresponding Author: Sumaya Hamad Department of Computer Science College of Computer Science and Information Technology Anbar University, Anbar, Iraq Email: sumayah.hamad@uoanbar.edu.iq 1. INTRODUCTION The problem of enhancement digital images could be addressed using several approaches, one of which is mathematical morphology (MM). Such operators pick a new gray level amidset two patterns (primary) for each point of the analyzed image based on some proximity criterion [1], [2]. MM was initially designed to analyze structures geometrically. However, its strong theoretical foundation has permitted its use in a variety of disciplines, including graphs, and digital image processing to mention a few [2]-[4]. Despite the domain is old completely, using morphological operators has a regenerate benefit for different image processing tasks in new year's. This is mostly owing to their ability to solve a variety of difficult shape-related challenge [2]. Segmentation, as well as automated counting and inspection, are key areas of application. Morphology is a strong and essential collection of methods that can be mathematically handled precisely within the context of set theory. Although the advantages of mathematical rigour are provided by this set-theoretic structure, it is not easily accessible to those who are not mathematically educated, and the key ideas and applications of morphology can be grasped far more easily through a realistic and intuitive discussion [4]. Morphological operations can be applied to any image, but the most common use of morphology is for processing binary images, and the most common morphological operators are dilation and erosion. It is possible to demonstrate that many more complex morphological procedures can be reduced to dilations and erosions [1], [2], [5]. For a range of image processing applications like segmentation, filtering, and edge detection, mathematical morphology offers strong nonlinear operators. It is presented and demonstrated in several applications how to apply these non-linear operators in an end-to-end deep learning framework. In different