The use of on-line co-training to reduce the training set size in pattern recognition methods: Application to left ventricle seg

The use of statistical pattern recognition models to segment the left ventricle of the heart in ultrasound images has gained substantial attention over the last few years. The main obstacle for the wider exploration of this methodology lies in the need for large annotated training sets, which are used for the estimation of the statistical model parameters. In this paper, we present a new on-line co-training methodologythat reduces the need for large training sets for such parameter estimation. Our approach learns the initial parameters of two different models using a small manually annotated training set. Then, given each frame of a test sequence, the methodology not only produces the segmentation of the current frame, but it also uses the results of both classifiers to re-train each other incrementally. This on-line aspect of our approach has the advantages of producing segmentation results and re-training the classifiers on the fly as frames of a test sequence are presented, but ...

• Type: Journal
• Year: 2012
• Where: CVPR
• Authors: Gustavo Carneiro, Jacinto C. Nascimento
• Keywords: Computer Vision, Cvpr 2012, Left Ventricle, Segmentation System, Statistical Pattern Recognition