International Conference on Intelligent Computational Systems (ICICS'2012) Jan. 7-8, 2012 Dubai Morphology Approach in Image Processing Satish Pawar and V. K. Banga Mathematical Morphology, which contains the used operators. The methodology is split into the following four steps: • Erosion • Dilation • Opening • Closing Abstract—Morphological operators transform the original image into another image through the interaction with the other image of certain shape and size which is known as the structure element. Morphology provides a systematic approach to analyze the geometric characteristics of signals or images, and has been applied widely too many applications such as edge detection, objection segmentation, noise suppression and so on. Morphology aims to extend the binary morphological operators to grey-level images. In order to define the basic morphological operations such as fuzzy erosion, dilation, opening and closing, a general method based upon fuzzy implication and inclusion grade operators is introduced. The fuzzy morphological operations extend the ordinary morphological operations by using fuzzy sets where for fuzzy sets, the union operation is replaced by a maximum operation, and the intersection operation is replaced by a minimum operation. A. EROSION Erosion is one of the basic operators in the area of mathematical morphology, the other being dilation. It is typically applied to binary images, but there are versions that work on grayscale images. The basic effect of the operator on a binary image is to erode away the boundaries of regions of foreground pixels .Thus areas of foreground pixels shrink in size, and holes within those areas become larger. Keywords—Binary Morphological, Fuzzy sets, Grayscale morphology, Image processing, Mathematical morphology. I. INTRODUCTION T HIS document is a template for Word (doc) versions. If you are reading a paper version of this document, so you can use it to prepare your manuscript. Mathematical Morphology is a method for quantitative analysis of spatial structures that aims at analyzing shapes and forms of an object. Mathematical morphology is based on set theory. The shapes of objects in a binary image are represented by object membership sets. Objects are connected areas of pixels with value 1, the background pixels have value 0. Binary mathematical morphology is based on two basic operations, defined in terms of a structuring element, a small window that scans the image and alters the pixels in function of its window content: a dilation of set A with structuring element B enlarges the objects, an erosion shrinks objects. Fig. 1(a) Simple image Fig. 1 (b) Image after erosion process In this process we increase the black pixel in the image making, it look thinner. Every object pixel that is touching an background pixel is changed into background pixel. II. METHODOLOGY For the extraction of the features of interest were applied routines of mathematical morphology on images. The software MATLAB was used as platform for the toolbox of B. DILATION Dilation is one of the operators in the area of mathematical morphology, the other being erosion. It is typically applied to binary images, but there are versions that work on grayscale images. The basic effect of the operator on a binary image is to gradually enlarge the boundaries of regions of foreground pixels . Thus areas of foreground pixels grow in size while holes within those regions become smaller. Satish Pawar is pursuing M. Tech (Electronics and Communication Engineering) in Department of Electronics & Communication Engineering from Amritsar College of Engineering and Technology, Amritsar, Punjab, India ([email protected]). Dr. Vijay Kumar Banga is working as Professor and Head of the Department of Electronics & Communication Engineering, Amritsar College of Engineering and Technology, Amritsar, Punjab, India ( [email protected]). 148 International Conference on Intelligent Computational Systems (ICICS'2012) Jan. 7-8, 2012 Dubai Fig. 2 (a) Simple image Fig. 2 (b)Image after Erosion Fig. 2 (c) Image after dilation process In dialation we increase the white pixel in the image making, it look broader. Every background pixel that is touching an object pixel is changed into an object pixel. Fig. 2 (d) Image after Dilation In this process we firstly do Erosion and then Dilation. This method is used to remove the extra white pixels from the images. C. OPENING In morphology, opening is the dilation of the erosion of a set A by a structuring element B: D. CLOSING In mathematical morphology, the closing of a set (binary image) A by a structuring element B is the erosion of the dilation of that set, and denote erosion and dilation, respectively. where Together with closing, the opening serves in computer vision and image processing as a basic workhorse of morphological noise removal. Opening removes small objects from the foreground (usually taken as the dark pixels) of an image, placing them in the background, while closing removes small holes in the foreground, changing small islands of background into foreground. These techniques can also be used to find specific shapes in an image. Opening can be used to find things into which a specific structuring element can fit. and denote the dilation and erosion, where respectively. In image processing, closing is, together with opening, the basic workhorse of morphological noise removal. Opening removes small objects, while closing removes small holes. Fig. 4 (a) Simple image Fig. 3 (a)Simple image Fig. 4 (b) Image after dilation In this process we firstly do Dilation and then Erosion. This method is used to remove the extra black pixels from the 149 International Conference on Intelligent Computational Systems (ICICS'2012) Jan. 7-8, 2012 Dubai images. REFERENCES [1] [2] [3] [4] [5] Fig. 4 (c) Image after erosion E. Application and implementation The application developed allows the user to perform four main operations to an image: dilation, erosion, opening and closing. Listed below are a few of the functionalities of the program: • Visual inspection of image processing allows the user to see how the structure image affects the original image. • Variable playback speeds allows the user to control the speed at which the structure image is processed through the image so a user can see how it affects the final image. • User defined structure image lets the user control what the 3x3 structure image looks like and allows users the ability to see how different structure images affect different images. • User defined images lets the user define an image up to 16x16. By clicking on the different cells, a user can setup up an image to their specifications before processing. • Rewind functionality enables a user to revert back to the original image if multiple passes were made during image processing (such as during opening and closing). III. CONCLUSION We show some digital images to illustrate the effect of dilation-erosion operators in images. It is known that binary mathematical morphology dilation expands the image and erosion shrinks it. Erosion yields a smaller image than the original and dilation in opposite. This idea can be extended to grey level imagery. A point to notice here, is the fact that erosion and dilation, basic operations in binary imagery, can be extended to grey level. If the image and structuring element are binary (0 and 1), binary operators, erosion-dilation hold the same effect that the fuzzy erosion-dilation operation results in overlapping effects of expands/shrink images. Fuzzy logic and fuzzy set theory provide many solutions to the mathematical morphology algorithms. They have extended way of processing the gray scale images by the help of the fuzzy morphological operators. 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