Segmentacion

International Journal of Computer Science & Communication Vol. 1, No. 2, July-December 2010, pp. 171-174

Image Segmentation by Improved Watershed Transformation in Programming Environment MATLAB
Manisha Bhagwat1, R.K.Krishna2 & V.E.Pise3
Department of Computer Technology, Nagpur University, Chandrapur, India Department of Electronics, Nagpur University, Chandrapur, India Email:1manishapise@rediffmail.com, 2rkrishna40@rediffmail.com, 3vivek_pise@rediffmail.com
1 2,3

ABSTRACT Image segmentation is the foundation of object recognition and computer vision. Watershed transform is usually adopted for image segmentation in the area of image processing and image analysis because it always generates closed contours for each region in the original image. The concept of watershed transformis based on a processing simulating the immersion of a landscape in a lake that is dams have to be built to prevent the merging of different catchment basins. But the watershed transformation leads to over segmentation. In this paper we will discuss the image segmentation by improved watershed transformation in MATLAB programming environment. Keywords: Morphological Reconstruction, Recognition,Watershed Transformation

1. INTRODUCTION

Image segmentation is an important and, perhaps, the most difficult task in image processing. Segmentation refers to the grouping of image elements that exhibit “similar” characteristics, i.e. subdividing an image into its constituent regions or objects. All subsequent interpretation tasks, such as object recognition and classification, rely heavily onthe quality of the segmentation process. There are many applications whether on synthesis of the objects or computer graphic images require precise segmentation. In general, image noise should be eliminated through image preprocessing. And there is some specifically-given work (such as region extraction and image marking) to do after the main operation of image segmentation for the sake ofgetting better visual effect [1]. Watershed transform has long been admitted as a useful tool in image segmentation. The watershed lines can effectively divide individual catchment basins in a gradient image and generate closed contours for each region in the original image. The methodologies of image segmentation based upon watershed transform have been developed and improved during the past decade.Numerous researches have been conducted for obtaining watershed lines. For example, Beucher [1] proposed an effective watershed algorithm by using markers. On the other hand, Vincent et al. [10] proposed a famous watershed algorithm called “immersion algorithm”, which provides an effective and efficient implementation for watershed transform. Beucher [1] categorized watershed algorithms into twogroups. The algorithms in the first group like

immersion algorithm [2] simulate the flooding process. The immersion algorithm is one of the most famous watershed segmentation algorithms. It offers an efficient way to extract watershed lines by simulating the immersion process on gradient images. The second group of watershed algorithms aims at direct detection of watershed lines. Over-segmentationis a significant problem for most watershed algorithms, which were addressed in numerous literatures [1-5]. Conventionally, watershed transform is mostly designed for the purpose of image segmentation. In this paper we will discuss the image segmentation by improved watershed transformation which is gives better result than traditional watershed transformation in MATLAB programming environmentand reduces over segmentation...
2. WATERSHED TRANSFORMATION

Watershed transform, which is originally proposed by Digabel and Lantuejoul [6][7], has been widely adopted in image segmentation. The methodologies of watershedbased image segmentation have been steady developed and improved during the past decade. Generally speaking, watershed transform can be classified as a region-based image...