CAMIS: clustering algorithm for medical image sequences using a mutual nearest neighbor criterion
Habib Benali, Irène Buvat, Frédérique Frouin, J. Bazin, R. Di Paola · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1994
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.