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Proceedings Paper

CAMIS: clustering algorithm for medical image sequences using a mutual nearest neighbor criterion
Author(s): Habib Benali; Irene Buvat; Frederique Frouin; Jean Pierre Bazin; Robert Di Paola
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Paper Abstract

We present a new clustering algorithm for medical images sequences (CAMIS). It combines criteria of spatial contiguity, signal evolution similarity, and the rule of mutual nearest neighbors. The statistical properties of the signal in the images (CT, MRI, nuclear medicine) is taken into account when choosing the dissimilarity index and is explicitly expressed for scintigraphic images. The partition, into an unknown number of classes, was updated by merging and pruning clusters. The efficiency of CAMIS as the first step of factor analysis of medical image sequences has been tested using simulated scintigraphic images.

Paper Details

Date Published: 8 July 1994
PDF: 12 pages
Proc. SPIE 2299, Mathematical Methods in Medical Imaging III, (8 July 1994); doi: 10.1117/12.179264
Show Author Affiliations
Habib Benali, INSERM Institut Gustave Roussy (France)
Irene Buvat, INSERM Institut Gustave Roussy (France)
Frederique Frouin, INSERM Institut Gustave Roussy (France)
Jean Pierre Bazin, INSERM Institut Gustave Roussy (France)
Robert Di Paola, INSERM Institut Gustave Roussy (France)

Published in SPIE Proceedings Vol. 2299:
Mathematical Methods in Medical Imaging III
Fred L. Bookstein; James S. Duncan; Nicholas Lange; David C. Wilson, Editor(s)

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