A key problem for many medical image segmentation tasks is the combination of different-level knowledge. We propose a novel scheme of embedding detected regions into a superpixel based graphical model, by which we achieve a full leverage on various image cues for ultrasound lesion segmentation. Region features are mapped into a higher-dimensional space via a boosted model to become well controlled. Parameters for regions, superpixels and a new affinity term are learned simultaneously within the framework of structured learning. Experiments on a breast ultrasound image data set confirm the effectiveness of the proposed approach as well as our two novel modules. © 2013 Springer-Verlag.
CITATION STYLE
Hao, Z., Wang, Q., Wang, X., Kim, J. B., Hwang, Y., Cho, B. H., … Lee, W. K. (2013). Learning a structured graphical model with boosted top-down features for ultrasound image segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8149 LNCS, pp. 227–234). https://doi.org/10.1007/978-3-642-40811-3_29
Mendeley helps you to discover research relevant for your work.