Plateau Problem in the Watershed Transform

The watershed transform is one of best known and widely used methods for image segmentation in mathematical morphology. Since the definition, deriving from geology and nature observation is quite intuitive and straightforward to im- plement; many fast and powerful algorithms for watershed transform have already been presented. However, there still occur problems when one wishes to achieve a precise solution on blurred or noised image. The same range of problems is faced when a plateau occurs in the image. In this paper several methods for plateau re- duction are discussed and some novel ideas proposed. All algorithms are performed on a set of both natural and synthetic images.

[1]  Jacques A. de Guise,et al.  A new set of fast algorithms for mathematical morphology : I. Idempotent geodesic transforms , 1992, CVGIP Image Underst..

[2]  Ron Kikinis,et al.  Improved watershed transform for medical image segmentation using prior information , 2004, IEEE Transactions on Medical Imaging.

[3]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[4]  Ilya Levner,et al.  Classification-Driven Watershed Segmentation , 2007, IEEE Transactions on Image Processing.

[5]  Mariusz Nieniewski,et al.  Extraction of diffuse objects from images by means of watershed and region merging: example of solar images , 2004, IEEE Trans. Syst. Man Cybern. Part B.

[6]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Fernand Meyer,et al.  Topographic distance and watershed lines , 1994, Signal Process..

[8]  Fernand Meyer,et al.  The Viscous Watershed Transform , 2005, Journal of Mathematical Imaging and Vision.

[9]  Mariusz Nieniewski Morphological method for extraction of microcalcifications in mammograms for breast cancer diagnosis , 1999 .

[10]  Jacques A. de Guise,et al.  A new set of fast algorithms for mathematical morphology : II. Identification of topographic features on grayscale images , 1992, CVGIP Image Underst..

[11]  Qiang Ji,et al.  Improved watershed segmentation using water diffusion and local shape priors , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[12]  L. Joshua Leon,et al.  Watershed-Based Segmentation and Region Merging , 2000, Comput. Vis. Image Underst..

[13]  Serge Beucher,et al.  Use of watersheds in contour detection , 1979 .

[14]  Jos B. T. M. Roerdink,et al.  The Watershed Transform: Definitions, Algorithms and Parallelization Strategies , 2000, Fundam. Informaticae.