A novel model of image segmentation based on watershed method is proposed in this paper. To prevent the oversegmentation of traditional watershed, our proposed algorithm has five stages. Firstly, the morphological reconstruction is applied to smooth the flat area and preserve the edge of the image. Secondly, multiscale morphological gradient is used to avoid the thickening and merging of the edges. Thirdly, for contrast enhancement, the top/bottom hat transformation is used. Fourthly, the morphological gradient of an image is modified by imposing regional minima at the location of both the internal and the external markers. Finally, a weighted function is used to combine the top/bottom hat transformation algorithm and the markers algorithm to get the new algorithm. The experimental results show the superiority of the new algorithm in terms of suppression over-segmentation. 1. Introduction A segmentation divides an image into its constituent regions or objects, and the segmentation must be stopped when the objects of interest in an application have been isolated [1]. Image segmentation is based on three principal concepts: edge detection, thresholding, and region growing. The most common one is thresholding. Thresholding has a high speed of operation and ease of implementation. However its performance is relatively limited since image pixels with the same gray level value will invariably be segmented into the same class [2]. Segmentation by morphological watersheds [3–10] embodies many of the concepts of the other three approaches, which produces more stable segmentation results, as well as providing simple framework. A simple watershed transformation causes oversegmentation [11]. In order to prevent this oversegmentation, the watershed method passed through several stages of evolution. The original watershed method was developed by Lantuejoul [12] and was widely described together with its applications by Beucher and Meyer [13]. The authors in [3] used FIFO queues to extend the original evolution with gray scale images [11]. Shafarenko et al. [14] applied FIFO to color images. In this paper we enhance the contrast of the gradient image by top/bottom hat transformation, modify the result of the enhancement by imposing regional minima at the locations of both the internal and the external markers, combine the top/bottom hat transformation algorithm and the markers algorithm by using suitable weight function, and subject the combination to the watershed algorithm. The new algorithm has a capability to prevent oversegmentation of the simple watershed
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