Echocardiographic image sequence segmentation using self-organizing maps

Presents a new approach for echocardiographic image sequence segmentation. The proposed method uses a 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 of its pixels and clusters 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 successfully measure the variation of the interventricular septum width.