Cluster analysis for automatic image segmentation in dynamic scintigraphies

Abstract

An original and entirely automatic algorithm is proposed to select regions of interest (ROIs) on dynamic scintigrams. This algorithm is based on factor analysis and on cluster analysis. It consists of first extracting the orthogonal factor images of the series using factor analysis of correspondence. These factor images are then automatically segmented in ROIs using a hierarchical ascendant classification procedure. The distance used for the classification is the 'minimum added intra-class variance' distance. This algorithm has been implemented on a fast computer dedicated to nuclear medicine (Nodecrest Micas V system). The time of calculation on 1000 pixels from 40 images is less than 5 min when three factor images are used. This algorithm is validated using a numerical phantom and is illustrated using renal (99Tcm DTPA) and cardiac (equilibrium gated angiography) dynamic scintigraphies. The results show that the algorithm is able to recognize the bladder, the renal cavities and the renal parenchyma on the renal series, and the ventricules and the atria on the cardiac series.