CAMIS: clustering algorithm for medical image sequences using a mutual nearest neighbor criterion

Habib Benali; Irene Buvat; Frederique Frouin; Jean Pierre Bazin; Robert Di Paola

doi:10.1117/12.179264

8 July 1994 CAMIS: clustering algorithm for medical image sequences using a mutual nearest neighbor criterion

Habib Benali, Irene Buvat, Frederique Frouin, Jean Pierre Bazin, Robert Di Paola

Proceedings Volume 2299, Mathematical Methods in Medical Imaging III; (1994) https://doi.org/10.1117/12.179264
Event: SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, 1994, San Diego, CA, United States

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.

Citation Download Citation

Habib Benali, Irene Buvat, Frederique Frouin, Jean Pierre Bazin, and Robert Di Paola "CAMIS: clustering algorithm for medical image sequences using a mutual nearest neighbor criterion", Proc. SPIE 2299, Mathematical Methods in Medical Imaging III, (8 July 1994); https://doi.org/10.1117/12.179264

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