ISSN: 2277-9655 [Kumar* et al., 6(2): February, 2017] Impact Factor: 4.116 IC™ Value: 3.00 CODEN: IJESS7 http: // www.ijesrt.com© International Journal of Engineering Sciences & Research Technology [104] IJESRT INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY SEGMENTATION USING MASKING METHODS IN COLOUR IMAGES: AN APPROACH E.Boopathi Kumar & Dr. V.Thiagarasu Research Scholar, Department of Computer Science, Gobi Arts & Science College, Tamilnadu, India. Associate Professor, Department of Computer Science, Gobi Arts & Science College, Tamilnadu, India. DOI: 10.5281/zenodo.268693 ABSTRACT Image segmentation is one of the popular methods in the field of Image processing. It is the process of grouping an image into units that are consistent with respect to one or more characteristics. Segmentation in gray images has lots of methods and it has several algorithms to represent it. But images giving more information in scenes i.e., colour images have few numbers of methods to segment. So, this paper represent colour image segmentation methods in the literature and getting to prepare novel segmentation method with combined form of masking, thresholding and noise removal methods. Otsu method is one of the best and classical Thresholding method used in colour image segmentation. It uses various combinations of masks to scan over the image to detect the correct boundary. Otsu method divide the segmentation tasks in two or more modules and make the process easily. In the same way this paper discusses about fuzzy membership functions mask to scan the image with few combinations and include noise removal method to produce the output image in well defined manner. KEYWORDS: Segmentation, Masking Methods, Color Segmentation, Fuzzy Membership Functions, Noise removal, Thresholding, Otsu method. INTRODUCTION Image segmentation is the first step in image analysis and pattern recognition. It is a critical and essential component of image analysis and pattern recognition system and it is one of the most difficult tasks in image processing to determine the quality of the final result of analysis. Image segmentation is a process of dividing an image into different regions such that each region is, but the union of any two adjacent regions is not, homogeneous. The problem of image segmentation has been known and addressed for the last 30 years. The general description of an image in everyone mind is the list of objects in an image and their positions. But when we deeply examine an image it depicts shadows of an object, differences in the color brightness of an object. Image segmentation is the process of partitioning an image into regions that are in some sense homogeneous, but different from neighboring regions. Segmentation is the first key step in object recognition, scene understanding and image understanding. Segmentation of an image can be done on the basis of some characteristics such as color, objects that are present in the entire image. The level to which the separation is carried depends on the problem being solved. The result of image segmentation is a group of different segments that mutually cover the entire image. Image segmentation algorithms are based on one of the two basic properties of the intensity value i.e. discontinuity and similarity. There are different approaches for different type of images. The first approach represents Histogram thresholding, second approach is Edge based and the last one is region based approach. In histogram thresholding different gray or color ranges are represented to made regions of an image. In the second approach, different edge detection operators are used and also the edges are joined if the regions are not connected. In the third approach images are partitioned into regions which are similar according