TY - JOUR
T1 - Hybrid image segmentation using watersheds and fast region merging
AU - Haris, Kostas
AU - Efstratiadis, Serafim N.
AU - Maglaveras, Nicos
AU - Katsaggelos, Aggelos K.
N1 - Funding Information:
Manuscript received July 13, 1996; revised October 20, 1997. This work was supported in part by the I4C project of the Health Telematics programme of the CEC. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Jeffrey J. Rodriguez.
PY - 1998
Y1 - 1998
N2 - A hybrid multidimensional image segmentation algorithm is proposed, which combines edge and region-based techniques through the morphological algorithm of watersheds. An edge-preserving 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 (bottom-up) 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 so-called nearest neighbor graph, due to which the priority queue size and processing time are drastically reduced. The final segmentation provides, due to the RAG, one-pixel wide, closed, and accurately localized contours/surfaces. Experimental results obtained with two-dimensional/three-dimensional (2-D/3-D) magnetic resonance images are presented.
AB - A hybrid multidimensional image segmentation algorithm is proposed, which combines edge and region-based techniques through the morphological algorithm of watersheds. An edge-preserving 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 (bottom-up) 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 so-called nearest neighbor graph, due to which the priority queue size and processing time are drastically reduced. The final segmentation provides, due to the RAG, one-pixel wide, closed, and accurately localized contours/surfaces. Experimental results obtained with two-dimensional/three-dimensional (2-D/3-D) magnetic resonance images are presented.
KW - Image segmentation
KW - Nearest neighbor region merging
KW - Noise reduction
KW - Watershed transform
UR - http://www.scopus.com/inward/record.url?scp=0032297229&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0032297229&partnerID=8YFLogxK
U2 - 10.1109/83.730380
DO - 10.1109/83.730380
M3 - Article
C2 - 18276235
AN - SCOPUS:0032297229
VL - 7
SP - 1684
EP - 1699
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
SN - 1057-7149
IS - 12
ER -