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Hybrid Image Segmentation Using Watersheds and Fast Region Merging
 IEEE transactions on Image Processing
, 1998
"... Abstract—A hybrid multidimensional image segmentation algorithm is proposed, which combines edge and regionbased techniques through the morphological algorithm of watersheds. An edgepreserving statistical noise reduction approach is used as a preprocessing stage in order to compute an accurate est ..."
Abstract

Cited by 132 (1 self)
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Abstract—A hybrid multidimensional image segmentation algorithm is proposed, which combines edge and regionbased techniques through the morphological algorithm of watersheds. An edgepreserving statistical noise reduction approach is used as a preprocessing stage in order to compute an accurate estimate of the image gradient. Then, an initial partitioning of the image into primitive regions is produced by applying the watershed transform on the image gradient magnitude. This initial segmentation is the input to a computationally efficient hierarchical (bottomup) region merging process that produces the final segmentation. The latter 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 RAG edges in a priority queue. We propose a significantly faster algorithm, which additionally maintains the socalled nearest neighbor graph, due to which the priority queue size and processing time are drastically reduced. The final segmentation provides, due to the RAG, onepixel wide, closed, and accurately localized contours/surfaces. Experimental results obtained with twodimensional/threedimensional (2D/3D) magnetic resonance images are presented. Index Terms — Image segmentation, nearest neighbor region merging, noise reduction, watershed transform. I.
Hybrid Image Segmentation Using Watersheds
, 1996
"... A hybrid image segmentation algorithm is proposed which combines edge and regionbased techniques through the morphological algorithm of watersheds. The algorithm consists of the following steps: a) Edgepreserving statistical noise reduction, b) Gradient Approximation, c) Detection of watersheds o ..."
Abstract

Cited by 2 (1 self)
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A hybrid image segmentation algorithm is proposed which combines edge and regionbased techniques through the morphological algorithm of watersheds. The algorithm consists of the following steps: a) Edgepreserving statistical noise reduction, b) Gradient Approximation, c) Detection of watersheds on gradient magnitude image, and d) 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 signi cantly 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 nal segmentation is an image partition which, through the RAG, provides information that can be used by knowledgebased high level processes, i.e. recognition. In addition, this region based representation provides onepixel wide, closed, and accurately localized contours/surfaces. Due to the small number of free parameters, the algorithm can be quite eectively used in interactive image processing. Experimental results obtained with 2D MR images are presented.