DMCA
1 Unsupervised Segmentation of Predefined Shapes in Multivariate Images (2003)
BibTeX
@MISC{Noordam031unsupervised,
author = {J. C. Noordam and L. M. C. Buydens},
title = {1 Unsupervised Segmentation of Predefined Shapes in Multivariate Images},
year = {2003}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Fuzzy C-means (FCM) is an unsupervised clustering technique and is often used for the unsupervised segmentation of multivariate images. In traditional FCM the clustering is based on spectral information only and geometrical relationship between neighbouring pixels is not used in the clustering procedure. In this paper, the Spatially Guided FCM (SG-FCM) algorithm is presented which segments multivariate images by incorporating both spatial and spectral information. Spatial information is described by a geometrical shape description and can vary from a local neighbourhood to a more extended shape model such as the hough circle detection. A modified FCM objective function uses the spatial information as described by the shape model. This results in a segmented image in which the construction of the cluster prototypes is influenced by spatial information. The performance of Spatially Guided FCM is compared with both FCM and the sequence of FCM and a majority filter. The SG-FCM segmented image shows more homogeneous regions and less spurious pixels.
Keyphrases
unsupervised segmentation multivariate image predefined shape spatial information spectral information spatially guided fcm hough circle detection spurious pixel geometrical shape description extended shape model geometrical relationship clustering procedure image show traditional fcm segmented image majority filter shape model fuzzy c-means modified fcm objective function homogeneous region cluster prototype unsupervised clustering technique local neighbourhood
