Learning a Structured Graphical Model with Boosted Top-Down Features for Ultrasound Image Segmentation
| Autor: | Qiang Wang, Baek Hwan Cho, Youngkyoo Hwang, Xiaotao Wang, Zhihui Hao, Won Ki Lee, Jung-Bae Kim, Ping Guo |
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| Rok vydání: | 2013 |
| Předmět: |
medicine.diagnostic_test
Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Scale-space segmentation Pattern recognition Image segmentation Top-down and bottom-up design medicine Embedding Leverage (statistics) Computer vision Graphical model Artificial intelligence Structured prediction business Breast ultrasound |
| Zdroj: | Advanced Information Systems Engineering ISBN: 9783642387081 MICCAI (1) |
| DOI: | 10.1007/978-3-642-40811-3_29 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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