Automatic infarct planimetry by means of swarm-based clustering

Abstract

Infarct planimetry is an important tool in cardiology research. At present this technique entails that infarct size is manually determined from scanned images of prepared heart sections. Existing attempts at automating infarct planimetry are limited in that they require user input in the form of starting points for region growing algorithms or template values for classification algorithms. In this paper a new automatic infarct planimetry (AIP) algorithm is presented. The algorithm entails colour contrast enhancement which is performed in the CIE LAB colour space. The distribution of the various tissue classes is thereafter modelled by means of a set of cluster centroids (multiple clusters are used to represent each tissue class in the RGB colour space). Finally, tissue pixels are classified by means of a nearest-neighbour rule. Two clustering algorithms are evaluated in this paper, namely the well-known k-means algorithm and particle swarm optimization (PSO) based clustering. The total AIP procedure is relatively robust for variations in background illumination as well as condensation patterns occurring on individual heart sections. The main advantage of this AIP algorithm is that only limited user input is required - the user merely has to specify which heart section is to be used for training. The classification decisions made by both variants of the AIP algorithm correlate well with those made by a human expert, with the PSO based clustering algorithm performing slightly better than k-means.