title: Learning a structured graphical model with boosted top-down features for ultrasound image segmentation.
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.
authors: Medical Imaging Group, China Lab.
authors: Zhihui
authors: Z
authors: Hao
authors: Medical Imaging Group, China Lab.
authors: Qiang
authors: Q
authors: Wang
authors: Medical Imaging Group, China Lab.
authors: Xiaotao
authors: X
authors: Wang
authors: Medical System Lab.
authors: Jung Bae
authors: JB
authors: Kim
authors: Medical System Lab.
authors: Youngkyoo
authors: Y
authors: Hwang
authors: Data Analytics Group, Samsung Advanced Institute of Technology.
authors: Baek Hwan
authors: BH
authors: Cho
authors: Medical Imaging Group, China Lab.
authors: Ping
authors: P
authors: Guo
authors: Medical Imaging Group, China Lab.
authors: Won Ki
authors: WK
authors: Lee
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016217:Ultrasonography, Mammary / Q000379:methods*