Learning a Structured Graphical Model with Boosted Top-Down Features for Ultrasound Image Segmentation

Zhihui Hao¹, Qiang Wang¹, Xiaotao Wang¹, Jung Bae Kim², Youngkyoo Hwang², Baek Hwan Cho³, Ping Guo¹, and Won Ki Lee¹

¹Medical Imaging Group, China Lab, China

²Medical System Lab, USA

³Data Analytics Group, Samsung Advanced Institute of Technology, Korea

Abstract. 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.

LNCS 8149, p. 227 ff.

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