title: Combining CRF and multi-hypothesis detection for accurate lesion segmentation in breast sonograms.
abstract: The implementation of lesion segmentation for breast ultrasound image relies on several diagnostic rules on intensity, texture, etc. In this paper, we propose a novel algorithm to achieve a comprehensive decision upon these rules by incorporating image over-segmentation and lesion detection in a pairwise CRF model, rather than a term-by-term translation. Multiple detection hypotheses are used to propagate object-level cues to segments and a unified classifier is trained based on the concatenated features. The experimental results show that our algorithm can avoid the drawbacks of separate detection or bottom-up segmentation, and can deal with very complicated cases.
authors.affiliation: Samsung Advanced Institute of Technology, Samsung Electronics.
authors.forename: Zhihui
authors.initials: Z
authors.lastname: Hao
authors.forename: Qiang
authors.initials: Q
authors.lastname: Wang
authors.forename: Yeong Kyeong
authors.initials: YK
authors.lastname: Seong
authors.forename: Jong-Ha
authors.initials: JH
authors.lastname: Lee
authors.forename: Haibing
authors.initials: H
authors.lastname: Ren
authors.forename: Ji-yeun
authors.initials: JY
authors.lastname: Kim
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001940:Breast / Q000473:pathology*; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging*; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D016217:Ultrasonography, Mammary / Q000379:methods*