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Items: 1 to 20 of 103

1.

Accurate fully automatic femur segmentation in pelvic radiographs using regression voting.

Lindner C, Thiagarajah S, Wilkinson JM, Wallis GA, Cootes TF; arcOGEN Consortium.

Med Image Comput Comput Assist Interv. 2012;15(Pt 3):353-60.

PMID:
23286150
2.

Fully automatic segmentation of the proximal femur using random forest regression voting.

Lindner C, Thiagarajah S, Wilkinson JM; arcOGEN Consortium, Wallis GA, Cootes TF.

IEEE Trans Med Imaging. 2013 Aug;32(8):1462-72. doi: 10.1109/TMI.2013.2258030. Epub 2013 Apr 12.

PMID:
23591481
3.

Accurate bone segmentation in 2D radiographs using fully automatic shape model matching based on regression-voting.

Lindner C, Thiagarajah S, Wilkinson JM, Wallis GA, Cootes TF; arcOGEN Consortium.

Med Image Comput Comput Assist Interv. 2013;16(Pt 2):181-9.

PMID:
24579139
4.

Fully automatic X-ray image segmentation via joint estimation of image displacements.

Chen C, Xie W, Franke J, Grützner PA, Nolte LP, Zheng G.

Med Image Comput Comput Assist Interv. 2013;16(Pt 3):227-34.

PMID:
24505765
5.

Automatic X-ray landmark detection and shape segmentation via data-driven joint estimation of image displacements.

Chen C, Xie W, Franke J, Grutzner PA, Nolte LP, Zheng G.

Med Image Anal. 2014 Apr;18(3):487-99. doi: 10.1016/j.media.2014.01.002. Epub 2014 Feb 5.

PMID:
24561486
6.

Automated segmentation of the femur and pelvis from 3D CT data of diseased hip using hierarchical statistical shape model of joint structure.

Yokota F, Okada T, Takao M, Sugano N, Tada Y, Sato Y.

Med Image Comput Comput Assist Interv. 2009;12(Pt 2):811-8.

PMID:
20426186
7.

MASCG: Multi-Atlas Segmentation Constrained Graph method for accurate segmentation of hip CT images.

Chu C, Bai J, Wu X, Zheng G.

Med Image Anal. 2015 Dec;26(1):173-84. doi: 10.1016/j.media.2015.08.011. Epub 2015 Sep 11.

PMID:
26426453
8.

Locally-constrained boundary regression for segmentation of prostate and rectum in the planning CT images.

Shao Y, Gao Y, Wang Q, Yang X, Shen D.

Med Image Anal. 2015 Dec;26(1):345-56. doi: 10.1016/j.media.2015.06.007. Epub 2015 Oct 2.

9.

Development of a fully automatic shape model matching (FASMM) system to derive statistical shape models from radiographs: application to the accurate capture and global representation of proximal femur shape.

Lindner C, Thiagarajah S, Wilkinson JM; arcOGEN Consortium, Wallis GA, Cootes TF.

Osteoarthritis Cartilage. 2013 Oct;21(10):1537-44. doi: 10.1016/j.joca.2013.08.008. Epub 2013 Aug 14.

10.

An articulated statistical shape model for accurate hip joint segmentation.

Kainmueller D, Lamecker H, Zachow S, Hege HC.

Conf Proc IEEE Eng Med Biol Soc. 2009;2009:6345-51. doi: 10.1109/IEMBS.2009.5333269.

PMID:
19964159
11.

Segmenting lung fields in serial chest radiographs using both population-based and patient-specific shape statistics.

Shi Y, Qi F, Xue Z, Chen L, Ito K, Matsuo H, Shen D.

IEEE Trans Med Imaging. 2008 Apr;27(4):481-94. doi: 10.1109/TMI.2007.908130.

PMID:
18390345
12.

Statistical model-based segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs.

Xie W, Franke J, Chen C, Grützner PA, Schumann S, Nolte LP, Zheng G.

Int J Comput Assist Radiol Surg. 2014 Mar;9(2):165-76. doi: 10.1007/s11548-013-0932-5. Epub 2013 Jul 31.

PMID:
23900851
13.

2D/3D deformable registration using a hybrid atlas.

Tang TS, Ellis RE.

Med Image Comput Comput Assist Interv. 2005;8(Pt 2):223-30.

PMID:
16685963
14.

An edge-region force guided active shape approach for automatic lung field detection in chest radiographs.

Xu T, Mandal M, Long R, Cheng I, Basu A.

Comput Med Imaging Graph. 2012 Sep;36(6):452-63. doi: 10.1016/j.compmedimag.2012.04.005. Epub 2012 May 18.

PMID:
22608158
15.

Segmenting lung fields in serial chest radiographs using both population and patient-specific shape statistics.

Shi Y, Qi F, Xue Z, Ito K, Matsuo H, Shen D.

Med Image Comput Comput Assist Interv. 2006;9(Pt 1):83-91.

PMID:
17354877
16.

Knowledge-based femur detection in conventional radiographs of the pelvis.

Pilgram R, Walch C, Blauth M, Jaschke W, Schubert R, Kuhn V.

Comput Biol Med. 2008 May;38(5):535-44. doi: 10.1016/j.compbiomed.2008.01.010. Epub 2008 Mar 20.

PMID:
18358463
17.

A robust technique for 2D-3D registration.

Gong RH, Abolmaesumi P, Stewart J.

Conf Proc IEEE Eng Med Biol Soc. 2006;1:1433-6.

PMID:
17945644
18.

Hybrid segmentation of mass in mammograms using template matching and dynamic programming.

Song E, Xu S, Xu X, Zeng J, Lan Y, Zhang S, Hung CC.

Acad Radiol. 2010 Nov;17(11):1414-24. doi: 10.1016/j.acra.2010.07.008.

PMID:
20817575
19.

Fast and robust clinical triple-region image segmentation using one level set function.

Li S, Fevens T, Krzyzak A, Jin C, Li S.

Med Image Comput Comput Assist Interv. 2006;9(Pt 2):766-73.

PMID:
17354842
20.

Segmentation of lumbar vertebrae using part-based graphs and active appearance models.

Roberts MG, Cootes TF, Pacheco E, Oh T, Adams JE.

Med Image Comput Comput Assist Interv. 2009;12(Pt 2):1017-24.

PMID:
20426211

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