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27 February 1996 Hybrid image segmentation using watersheds
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Proceedings Volume 2727, Visual Communications and Image Processing '96; (1996)
Event: Visual Communications and Image Processing '96, 1996, Orlando, FL, United States
A hybrid image segmentation algorithm is proposed which combines edge- and region-based techniques through the morphological algorithm of watersheds. The algorithm consists of the following steps: (1) edge-preserving statistical noise reduction, (2) gradient approximation, (3) detection of watersheds on gradient magnitude image, and (4) hierarchical region merging (HRM) in order to get semantically meaningful segmentations. The HRM process uses the region adjacency graph (RAG) representation of the image regions. At each step, the most similar pair of regions is determined (minimum cost RAG edge), the regions are merged and the RAG is updated. Traditionally, the above is implemented by storing all the RAG edges in a priority queue (heap). We propose a significantly faster algorithm which maintains an additional graph, the most similar neighbor graph, through which the priority queue size and processing time are drastically reduced. The final segmentation is an image partition which, through the RAG, provides information that can be used by knowledge-based high level processes, i.e. recognition. In addition, this region based representation provides one-pixel wide, closed, and accurately localized contours/surfaces. Due to the small number of free parameters, the algorithm can be quite effectively used in interactive image processing. Experimental results obtained with 2D MR images are presented.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kostas Haris, Serafim N. Efstratiadis, Nicos Maglaveras, and Costas Pappas "Hybrid image segmentation using watersheds", Proc. SPIE 2727, Visual Communications and Image Processing '96, (27 February 1996);

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