|
|
|
|
| LEADER |
01000caa a22002652 4500 |
| 001 |
NLM160011159 |
| 003 |
DE-627 |
| 005 |
20240312231926.0 |
| 007 |
tu |
| 008 |
231223s2006 xx ||||| 00| ||eng c |
| 028 |
5 |
2 |
|a pubmed24n1324.xml
|
| 035 |
|
|
|a (DE-627)NLM160011159
|
| 035 |
|
|
|a (NLM)16402624
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Li, Kang
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Optimal surface segmentation in volumetric images--a graph-theoretic approach
|
| 264 |
|
1 |
|c 2006
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ohne Hilfsmittel zu benutzen
|b n
|2 rdamedia
|
| 338 |
|
|
|a Band
|b nc
|2 rdacarrier
|
| 500 |
|
|
|a Date Completed 01.02.2006
|
| 500 |
|
|
|a Date Revised 12.03.2024
|
| 500 |
|
|
|a published: Print
|
| 500 |
|
|
|a Citation Status MEDLINE
|
| 520 |
|
|
|a Efficient segmentation of globally optimal surfaces representing object boundaries in volumetric data sets is important and challenging in many medical image analysis applications. We have developed an optimal surface detection method capable of simultaneously detecting multiple interacting surfaces, in which the optimality is controlled by the cost functions designed for individual surfaces and by several geometric constraints defining the surface smoothness and interrelations. The method solves the surface segmentation problem by transforming it into computing a minimum s-t cut in a derived arc-weighted directed graph. The proposed algorithm has a low-order polynomial time complexity and is computationally efficient. It has been extensively validated on more than 300 computer-synthetic volumetric images, 72 CT-scanned data sets of different-sized plexiglas tubes, and tens of medical images spanning various imaging modalities. In all cases, the approach yielded highly accurate results. Our approach can be readily extended to higher-dimensional image segmentation
|
| 650 |
|
4 |
|a Evaluation Study
|
| 650 |
|
4 |
|a Journal Article
|
| 650 |
|
4 |
|a Research Support, N.I.H., Extramural
|
| 650 |
|
4 |
|a Research Support, Non-U.S. Gov't
|
| 650 |
|
4 |
|a Research Support, U.S. Gov't, Non-P.H.S.
|
| 700 |
1 |
|
|a Wu, Xiaodong
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Chen, Danny Z
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Sonka, Milan
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 28(2006), 1 vom: 18. Jan., Seite 119-34
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
|
| 773 |
1 |
8 |
|g volume:28
|g year:2006
|g number:1
|g day:18
|g month:01
|g pages:119-34
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 28
|j 2006
|e 1
|b 18
|c 01
|h 119-34
|