Echocardiographic image sequence segmentation using self-organizing maps

This paper presents a new approach for echocardiographic image sequence segmentation. The proposed method uses the self-organizing map to approximate the probability density function of the image patterns. The map is post-processed, by the k-means clustering algorithm, in order to detect groups of neurons whose weights are similar. Each segmented image of the sequence is generated by correlation its pixels and cluster found in the map. The best number of clusters is dependent on the application To validate the segmentation procedure, we used a segmented sequence to measure successfully the variation of the interventricular septum width.