Translator Disclaimer
Paper
8 July 1994 CAMIS: clustering algorithm for medical image sequences using a mutual nearest neighbor criterion
Author Affiliations +
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.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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
PROCEEDINGS
12 PAGES


SHARE
Advertisement
Advertisement
RELATED CONTENT


Back to Top