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231224s2012 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2012.2186306
|2 doi
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|a pubmed24n0717.xml
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|a eng
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| 100 |
1 |
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|a Chen, Xinjian
|e verfasserin
|4 aut
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| 245 |
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|a Medical image segmentation by combining graph cuts and oriented active appearance models
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|c 2012
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 18.07.2012
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|a Date Revised 21.10.2021
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a In this paper, we propose a novel method based on a strategic combination of the active appearance model (AAM), live wire (LW), and graph cuts (GCs) for abdominal 3-D organ segmentation. The proposed method consists of three main parts: model building, object recognition, and delineation. In the model building part, we construct the AAM and train the LW cost function and GC parameters. In the recognition part, a novel algorithm is proposed for improving the conventional AAM matching method, which effectively combines the AAM and LW methods, resulting in the oriented AAM (OAAM). A multiobject strategy is utilized to help in object initialization. We employ a pseudo-3-D initialization strategy and segment the organs slice by slice via a multiobject OAAM method. For the object delineation part, a 3-D shape-constrained GC method is proposed. The object shape generated from the initialization step is integrated into the GC cost computation, and an iterative GC-OAAM method is used for object delineation. The proposed method was tested in segmenting the liver, kidneys, and spleen on a clinical CT data set and also on the MICCAI 2007 Grand Challenge liver data set. The results show the following: 1) The overall segmentation accuracy of true positive volume fraction TPVF > 94.3% and false positive volume fraction can be achieved; 2) the initialization performance can be improved by combining the AAM and LW; 3) the multiobject strategy greatly facilitates initialization; 4) compared with the traditional 3-D AAM method, the pseudo-3-D OAAM method achieves comparable performance while running 12 times faster; and 5) the performance of the proposed method is comparable to state-of-the-art liver segmentation algorithm. The executable version of the 3-D shape-constrained GC method with a user interface can be downloaded from http://xinjianchen.wordpress.com/research/
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| 650 |
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|a Journal Article
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| 650 |
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|a Research Support, N.I.H., Intramural
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| 700 |
1 |
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|a Udupa, Jayaram K
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Bagci, Ulas
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhuge, Ying
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yao, Jianhua
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 21(2012), 4 vom: 15. Apr., Seite 2035-46
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|g day:15
|g month:04
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