Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33415-3_62 | MICCAI |
Keywords | Field | DocType |
novel algorithm,complicated case,bottom-up segmentation,image over-segmentation,separate detection,multiple detection hypothesis,combining crf,lesion segmentation,breast sonograms,multi-hypothesis detection,accurate lesion segmentation,breast ultrasound image,lesion detection,comprehensive decision | Breast ultrasound,Pairwise comparison,Computer vision,Scale-space segmentation,Lesion,Pattern recognition,Segmentation,Computer science,Artificial intelligence,Concatenation,Classifier (linguistics),Lesion segmentation | Conference |
Volume | Issue | ISSN |
15 | Pt 1 | 0302-9743 |
Citations | PageRank | References |
3 | 0.39 | 15 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhihui Hao | 1 | 39 | 4.30 |
| Qiang Wang | 2 | 16 | 4.39 |
| Yeong Kyeong Seong | 3 | 22 | 6.38 |
| Jong-Ha Lee | 4 | 62 | 6.51 |
| Haibing Ren | 5 | 69 | 8.32 |
| Jiyeun Kim | 6 | 23 | 3.66 |