International Journal of Scientific and Research Publications, Volume 2, Issue 10, October 2012 1 ISSN 2250-3153 www.ijsrp.org A New Image Segmentation Algorithm for Grid Computing Pinaki Pratim Acharjya * , Dibyendu Ghoshal ** * Department of CSE, Bengal Institute of Technology and Management ** Department of ECE, National Institute of Technology Abstract- A new method for image segmentation using watershed transform algorithm is presented in this paper. It takes advantage of the fact that the proposed algorithm produced good results even if the same parameters are used for the standard segmentation algorithm. The proposed segmentation algorithm will be very effective for grid computing as it seems to possess specific tasks of image information and detection in order to obtain a detailed and accurate image analysis. Index Terms- Grid computing, Image segmentation, Watershed algorithm. I. INTRODUCTION mage segmentation [1-2] is an essential process for computer vision and for image analysis tasks. The general segmentation problem involves the partitioning a given image into a number of homogeneous segments, such that the union of any two neighboring segments yields a heterogeneous segment. Various methods are there for dealing with segmentation and feature extraction like, histogram based techniques, edge-based techniques, region based techniques, Markov random field based techniques, and so on. However, because of the variety and complexity of images, the segmentation is a challenging task. Image segmentation analysis computationally requires large amounts of processing power for sharing any algorithm and there solutions. One way of obtaining this processing power is to make use of grid computing [3-4]. With grid computing we have the ability to distribute jobs to many smaller server components. The main advantage of grid computing is instead of having one heavily burdened server the load can be distributed across many computers, where the distributed nature of grid computing is transparent to the user. When a user submits a job they don't have to think about which machine their job is going to get executed on. The "grid software" will perform the necessary calculations and decide where to send the job based on policies. Among the various image segmentation techniques, a well- known image segmentation technique is watershed transform using distance transform [5-6], which is based on mathematical morphology. In contrast to classical area based segmentation, the watershed transform is executed on the gradient image. A good number of works has already been carried out on watershed segmentation and these are available in the published or online literature [7-15]. In this paper we propose a new reliable algorithm for image segmentation using watershed using distance transform with image contrast enhancement concept which will found to be very useful for the grid computing environment. This paper is organized as the following. The section 2 is a brief description of Grid technology. The section 3 introduces the morphological watershed method in image segmentation. Section 4 describes the proposed algorithm. The implementation results and discussions are described in section 5 and finally conclusions are discussed in section 6. Experimental results presented in this paper are obtained by using MATLAB. II. THE GRID TECHNOLOGY Figure 1: A basic grid computing system, every computer can access the resources of every other computer belonging to the network. Sharing any algorithms and there solutions is a vast challenge. A grid is a network of computers. Grid technology is based upon so-called nodes that are linked together and share certain communication rules in using open standards. Grid computing framework allows a platform independent access to remote computing services. Web services allow end-users to fully interact with data, information requests as well as applications with a low level of user interaction. It is an Internet embedded network with variety of connected nodes which correspond to servers. Grids are the platform of communication standards, and users can freely concentrate on their desired tasks. Grid also provides network computing of the user’s tasks, which is distributed computing. Grid provides a verity of services like, computational services, data services, application services, information services, knowledge services, etc. I