Heart Cavity Segmentation in Ultrasound Images Based on Supervised Neural Networks
MIRAGE '09 Proceedings of the 4th International Conference on Computer Vision/Computer Graphics CollaborationTechniques
Level set segmentation of the fetal heart
FIMH'05 Proceedings of the Third international conference on Functional Imaging and Modeling of the Heart
| Hi-index | 0.00 |
This paper presents a new approach for echocardiographic image sequence segmentation and a practical application of the results. The proposed method uses the self-organizing maps 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 correlating its pixels and clusters found in the map. The image sequence segmented was used to measure fetal heart structures. To refine the measurements we used a border detection technique based on the least-means squares error. The segmentation procedure was validated successfully by physicians.