Segmentation of 2D and 3D Images through Hierarchical Clustering based
on Region Modelling
X. Shen, M. Spann, P. F. M. Nacken
Pattern Recognition, 31(9), pp. 1295-1320, Sept. 1998
Abstract
This paper presents an unsupervised segmentation method applicable to both
2D and 3D images. The segmentation is achieved by a bottom-up hierarchical
analysis to progressively agglomerate pixels/voxels in the image into non-overlapped
homogeneous regions characterised by a linear signal model. A hierarchy
of adjacency graphs is used to describe agglomeration results from the
hierarchical analysis, and is constructed by successively performing a
clustering operation which produces an optimal classification by merging
each region with its nearest neighbours. The nearest neighbour of a region
is determined by a merge condition derived under the framework of a statistical
inference and a dissimilarity function based on the error produced by fitting
the region model to pixels/voxels in two adjacent regions. The top level
of hierarchy then describes the segmentation result.
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