Unsupervised segmentation of predefined shapes in multivariate images

Fuzzy C‐means (FCM) is an unsupervised clustering technique that is often used for the unsupervised segmentation of multivariate images. In traditional FCM the clustering is based on spectral information only and the geometrical relationship between neighbouring pixels is not used in the clustering...

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Bibliographic Details
Published in:Journal of chemometrics 2003-04, Vol.17 (4), p.216-224
Main Authors: Noordam, J. C., van den Broek, W. H. A. M., Buydens, L. M. C.
Format: Article
Language:English
Subjects:
Applied sciences
clustering-algorithm
Exact sciences and technology
fuzzy
fuzzy C-means clustering
Image processing
Information, signal and communications theory
magnetic-resonance images
multivariate imaging
segmentation
Signal processing
spectral-spatial clustering
Telecommunications and information theory
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Summary:Fuzzy C‐means (FCM) is an unsupervised clustering technique that is often used for the unsupervised segmentation of multivariate images. In traditional FCM the clustering is based on spectral information only and the 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 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 SG‐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. Copyright © 2003 John Wiley & Sons, Ltd.
ISSN:0886-9383
1099-128X
DOI:10.1002/cem.794