Atlas-based prostate segmentation using an hybrid registration, International Journal of Computer Assisted Radiology and Surgery, vol.12, issue.6, pp.485-92, 2008. ,
DOI : 10.1007/s11548-008-0247-0
URL : https://hal.archives-ouvertes.fr/hal-00289854
Combining a deformable model and a probabilistic framework for an automatic 3D segmentation of prostate on MRI, International Journal of Computer Assisted Radiology and Surgery, vol.1, issue.2, pp.181-189, 2009. ,
DOI : 10.1007/s11548-008-0281-y
URL : https://hal.archives-ouvertes.fr/hal-00702584
Supervised and unsupervised methods for prostate cancer segmentation with multispectral MRI, Medical Physics, vol.12, issue.1, pp.1873-83, 2010. ,
DOI : 10.1118/1.3359459
Computer-assisted diagnosis of prostate cancer using DCE-MRI data: design, implementation and preliminary results, International Journal of Computer Assisted Radiology and Surgery, vol.5, issue.2, pp.1-10, 2009. ,
DOI : 10.1007/s11548-008-0261-2
Prostate Cancer Segmentation With Simultaneous Estimation of Markov Random Field Parameters and Class, IEEE Transactions on Medical Imaging, vol.28, issue.6, pp.906-921, 2009. ,
DOI : 10.1109/TMI.2009.2012888
Imaging of the prostate, 2002. ,
Prostate Boundary Detection From Ultrasonographic Images, Journal of Ultrasound in Medicine, vol.22, issue.6, pp.605-628, 2003. ,
DOI : 10.7863/jum.2003.22.6.605
Deformable segmentation of 3-D ultrasound prostate images using statistical texture matching method, IEEE Transactions on Medical Imaging, vol.25, issue.3, pp.256-72, 2006. ,
DOI : 10.1109/TMI.2005.862744
Automatic initialization of an active shape model of the prostate, Medical Image Analysis, vol.12, issue.4, pp.469-83, 2008. ,
DOI : 10.1016/j.media.2008.02.001
Computer technology in detection and staging of prostate carcinoma: A review, Medical Image Analysis, vol.10, issue.2, pp.178-99, 2006. ,
DOI : 10.1016/j.media.2005.06.003
Ultrasound image segmentation: a survey, IEEE Transactions on Medical Imaging, vol.25, issue.8, pp.987-1010, 2006. ,
DOI : 10.1109/TMI.2006.877092
URL : https://hal.archives-ouvertes.fr/hal-00338658
Current Methods in Medical Image Segmentation, Annual Review of Biomedical Engineering, vol.2, issue.1, pp.315-322, 2000. ,
DOI : 10.1146/annurev.bioeng.2.1.315
Computerised prostate boundary estimation of ultrasound images using radial bas-relief method, Medical & Biological Engineering & Computing, vol.29, issue.5, pp.445-54, 1997. ,
DOI : 10.1007/BF02525522
Outlining the prostate boundary using the harmonics method, Medical & Biological Engineering & Computing, vol.14, issue.11, pp.768-71, 1998. ,
DOI : 10.1007/BF02518882
Edge-guided boundary delineation in prostate ultrasound images, IEEE Transactions on Medical Imaging, vol.19, issue.12, pp.1211-1220, 2000. ,
DOI : 10.1109/42.897813
Semi-automatic Segmentation of the Prostate, Pattern Recognition and Image Analysis Proceedings of First Iberian Conference, pp.1108-1124, 2003. ,
DOI : 10.1007/978-3-540-44871-6_128
Edge detection and ridge detection with automatic scale selection, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.465-70, 1996. ,
DOI : 10.1109/CVPR.1996.517113
Semi-Automatic Prostate Segmentation of MR Images Based on Flow Orientation, 2006 IEEE International Symposium on Signal Processing and Information Technology, pp.203-210, 2006. ,
DOI : 10.1109/ISSPIT.2006.270797
Semi automatic MRI prostate segmentation based on wavelet multiscale products, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.3020-3023, 2008. ,
DOI : 10.1109/IEMBS.2008.4649839
An introduction to kalman filter, 2011. ,
Probabilistic data association methods for tracking complex visual objects, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.6, pp.560-76, 2001. ,
DOI : 10.1109/34.927458
Segmentation of prostate contours from ultrasound images, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, pp.517-537, 2004. ,
DOI : 10.1109/ICASSP.2004.1326595
Markov Random Field Models in Computer Vision, Spinger-Verlag, 1995. ,
Segmentation of prostate boundaries using regional contrast enhancement, IEEE International Conference on Image Processing 2005, pp.1266-1275, 2005. ,
DOI : 10.1109/ICIP.2005.1530293
Handbook of Medical Image Processing and Analysis, 2008. ,
Snakes: Active contour models, International Journal of Computer Vision, vol.5, issue.6035, pp.321-352, 1988. ,
DOI : 10.1007/BF00133570
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.124.5318
Finite-element methods for active contour models and balloons for 2-D and 3-D images, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.15, issue.11, pp.1131-1178, 1993. ,
DOI : 10.1109/34.244675
Biomedical Image Analysis: Tracking, Synthesis Lectures on Image, Video, and Multimedia Processing, vol.2, issue.1, 2005. ,
DOI : 10.2200/S00002ED1V01Y200508IVM002
Snakes, Shapes, and Gradient Vector Flow, IEEE Transaction on Image Processing, vol.7, pp.359-69, 1998. ,
Outlining of the prostate using snakes with shape restrictions based on the wavelet transform (Doctoral Thesis: Dissertation), Pattern Recognition, vol.32, issue.10, pp.32-1767, 1999. ,
DOI : 10.1016/S0031-3203(98)00177-0
A discrete dynamic contour model, IEEE Transactions on Medical Imaging, vol.14, issue.1, pp.12-24, 1995. ,
DOI : 10.1109/42.370398
Prostate segmentation in 3D US images using the cardinal-spline-based discrete dynamic contour, Medical Imaging 2003: Visualization, Image-Guided Procedures, and Display, pp.69-76, 2003. ,
DOI : 10.1117/12.480370
Segmentation of prostate ultrasound images using an improved snakes model, Proceedings 7th International Conference on Signal Processing, 2004. Proceedings. ICSP '04. 2004., pp.2568-71, 2004. ,
DOI : 10.1109/ICOSP.2004.1442306
An Energy-Based Segmentation of Prostate from Ultrasound Images Using Dot-Pattern Select Cells, IEEE International Conference on Acoustics, Speech and Signal Processing, pp.297-300, 2007. ,
A three-dimensional deformable model for segmentation of human prostate from ultrasound images, Medical Physics, vol.3, issue.10, pp.2147-53, 2001. ,
DOI : 10.1118/1.1388221
Numerical estimation of the curvature of surfaces, Numerical Estimation of the Curvature of the Surfaces, pp.33-40, 1986. ,
DOI : 10.1016/S0010-4485(86)80008-2
Optimal Edge Detection Using Expansion Matching and Restoration, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.16, pp.1169-82, 1994. ,
Use of active shape models for locating structures in medical images, Image and Vision Computing, vol.12, issue.6, pp.355-66, 1994. ,
DOI : 10.1016/0262-8856(94)90060-4
A framework for automatic landmark identification using a new method of nonrigid correspondence, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.3, pp.241-51, 2000. ,
DOI : 10.1109/34.841756
Segmentation of prostate boundaries from ultrasound images using statistical shape model, IEEE Transactions on Medical Imaging, vol.22, issue.4, pp.539-51, 2003. ,
DOI : 10.1109/TMI.2003.809057
Segmentation of abdominal ultrasound images of the prostate using a priori information and an adapted noise filter, Computerized Medical Imaging and Graphics, vol.29, issue.1, pp.43-51, 2005. ,
DOI : 10.1016/j.compmedimag.2004.07.007
Prostate boundary segmentation from ultrasound images using 2D active shape models: Optimisation and extension to 3D, Computer Methods and Programs in Biomedicine, vol.84, issue.2-3, pp.99-113, 2006. ,
DOI : 10.1016/j.cmpb.2006.07.001
A hybrid ASM approach for sparse volumetric data segmentation, Pattern Recognition and Image Analysis, vol.17, issue.2, pp.252-260, 2007. ,
DOI : 10.1134/S1054661807020125
Geometric-model-based segmentation of the prostate and surrounding structures for image-guided radiotherapy, Visual Communications and Image Processing 2004, pp.168-76, 2004. ,
DOI : 10.1117/12.526016
Segmenting CT prostate images using population and patient-specific statistics for radiotherapy, Biomedical Imaging: From Nano to Macro, ISBI, pp.282-287 ,
DOI : 10.1118/1.3464799
Object recognition from local scale-invariant features, Proceedings of the Seventh IEEE International Conference on Computer Vision, pp.1150-1157, 1999. ,
DOI : 10.1109/ICCV.1999.790410
Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations, Journal of Computational Physics, vol.79, issue.1, pp.12-49, 1988. ,
DOI : 10.1016/0021-9991(88)90002-2
An Elliptical Level Set Method for Automatic TRUS Prostate Image Segmentation, 2006 IEEE International Symposium on Signal Processing and Information Technology, pp.191-197, 2006. ,
DOI : 10.1109/ISSPIT.2006.270795
A Medical Texture Local Binary Pattern For TRUS Prostate Segmentation, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.5605-5613, 2007. ,
DOI : 10.1109/IEMBS.2007.4353617
Prostate Surface Segmentation from 3D Ultrasound Images, Proceedings IEEE International Symposium on Biomedical Imaging, pp.613-619, 2002. ,
Slice-Based Prostate Segmentation in 3D US Images Using Continuity Constraint, Proceedings of 27th Annual International Conference of the Engineering in Medicine and Biology Society, pp.662-667, 2006. ,
Prostate Segmentation in 2D Ultrasound Images Using Image Warping and Ellipse Fitting, Medical Image Computing and Computer-Assisted Intervention -MICCAI, pp.17-24, 2006. ,
DOI : 10.1007/11866763_3
Prostate segmentation in echographic images: A variational approach using deformable super-ellipse and rayleigh distribution, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.129-161, 2008. ,
DOI : 10.1109/ISBI.2008.4540949
Semi-automatic segmentation for prostate interventions, Semi-Automatic Segmentation for Prostate Interventions, pp.226-263, 2011. ,
DOI : 10.1016/j.media.2010.10.002
A review of atlas-based segmentation for magnetic resonance brain images, Computer Methods and Programs in Biomedicine, vol.104, issue.3, pp.158-77, 2011. ,
DOI : 10.1016/j.cmpb.2011.07.015
Diffeomorphic Registration Using B-Splines, Medical Image Computing and Computer-Assisted Intervention MICCAI, pp.702-711, 2006. ,
DOI : 10.1007/11866763_86
Fast Automatic Multi-atlas Segmentation of the Prostate from 3D MR Images, Prostate Cancer Imaging, pp.10-21, 2011. ,
DOI : 10.2307/1932409
Label Fusion in Atlas-Based Segmentation Using a Selective and Iterative Method for Performance Level Estimation (SIMPLE), IEEE Transactions on Medical Imaging, vol.29, issue.12, pp.2000-2008, 2010. ,
DOI : 10.1109/TMI.2010.2057442
Atlas Based Segmentation and Mapping of Organs at Risk from Planning CT for the Development of Voxel-Wise Predictive Models of Toxicity in Prostate Radiotherapy, Prostate Cancer Imaging, pp.42-51, 2010. ,
DOI : 10.1007/978-3-642-15989-3_6
URL : https://hal.archives-ouvertes.fr/hal-00910242
Evaluation of multi-atlas-based segmentation of CT scans in prostate cancer radiotherapy, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.1966-1975, 2011. ,
DOI : 10.1109/ISBI.2011.5872795
URL : https://hal.archives-ouvertes.fr/hal-00908596
Graph Cuts in Vision and Graphics: Theories and Applications, Handbook of Mathematical Models in Computer Vision, 2006. ,
DOI : 10.1007/0-387-28831-7_5
Prostate Segmentation from 2D Ultrasound Images Using Graph Cuts and Domain Knowledge, Canadian Conference on Computer and Robot Vision, pp.359-62, 2008. ,
An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.9, pp.1124-1161, 2004. ,
DOI : 10.1109/TPAMI.2004.60
Active contours without edges, IEEE Transactions on Image Processing, vol.10, issue.2, pp.266-77, 2001. ,
DOI : 10.1109/83.902291
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.2.1828
Sing, 3D Prostate Surface Detection from Ultrasound Images Based on Level Set Method, Medical Image Computing and Computer-Assisted Intervention MICCAI, pp.389-96, 2002. ,
Automatic Segmentation of the Prostate from Ultrasound Data Using Feature-Based Self Organizing Map, Proceedinggs of Scandinavian Conference in Image Analysis, pp.1259-65, 2005. ,
DOI : 10.1007/11499145_127
Feature Based Classification of, Proceedings of International Joint Conference on Neural Networks, pp.278-81, 2007. ,
Fractal Functions and Wavelet Expansions Based on Several Scaling Functions, Journal of Approximation Theory, vol.78, issue.3, pp.373-401, 1994. ,
DOI : 10.1006/jath.1994.1085
URL : http://doi.org/10.1006/jath.1994.1085
Prostate Tissue Characterization Using TRUS Image Spectral Features, pp.589-601, 2006. ,
DOI : 10.1007/11867661_53
Filtering for texture classification: a comparative study, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.21, issue.4, pp.291-310, 1999. ,
DOI : 10.1109/34.761261
Automated texture-based segmentation of ultrasound images of the prostate, Computerized Medical Imaging and Graphics, vol.20, issue.3, pp.131-171, 1996. ,
DOI : 10.1016/0895-6111(96)00048-1
Mean shift: a robust approach toward feature space analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.5, pp.603-622, 2002. ,
DOI : 10.1109/34.1000236
Discrete Deformable Model Guided by Partial Active Shape Model for TRUS Image Segmentation, IEEE Transactions on Biomedical Engineering, vol.57, pp.1158-66, 2010. ,
Automated Segmentation of 3D US Prostate Images Using Statistical Texture-Based Matching Method, Medical Image Computing and Computer-Assisted Intervention -MICCAI, pp.688-96, 2003. ,
DOI : 10.1007/978-3-540-39899-8_84
An Efficient Method for Deformable Segmentation of 3D US Prostate Images, Second International Workshop on Medical Imaging and Augmented Reality, pp.103-115, 2004. ,
DOI : 10.1007/978-3-540-28626-4_13
Orthogonal moment operators for subpixel edge detection, Pattern Recognition, vol.26, issue.2, pp.295-306, 1993. ,
DOI : 10.1016/0031-3203(93)90038-X
Increasing Efficiency of SVM by Adaptively Penalizing Outliers, 5th International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, pp.539-51, 2005. ,
DOI : 10.1007/11585978_35
Semi-automated segmentation of the prostate gland boundary in ultrasound images using a machine learning approach, Medical Imaging 2008: Image Processing, pp.1-8, 2008. ,
DOI : 10.1117/12.770965
A 2D Active Appearance Model For Prostate Segmentation in Ultrasound Images, 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.3363-3369, 2005. ,
Active Appearance Models, Proceedings of European Conference on Computer Vision, pp.484-98, 1998. ,
Texture Guided Active Appearance Model Propagation for Prostate Segmentation, Prostate Cancer Imaging, pp.111-131, 2010. ,
DOI : 10.1007/978-3-642-15989-3_13
Statistical shape and texture model of quadrature phase information for prostate segmentation, International Journal of Computer Assisted Radiology and Surgery, vol.25, issue.1, pp.43-55, 2012. ,
DOI : 10.1007/s11548-011-0616-y
URL : https://hal.archives-ouvertes.fr/hal-00612739
Multiple Mean Models of Statistical Shape and Probability Priors for Automatic Prostate Segmentation ,
DOI : 10.1109/TMI.2005.862744
URL : https://hal.archives-ouvertes.fr/hal-00681463
Parametric Shape Modeling Using Deformable Superellipses for Prostate Segmentation, IEEE Transactions on Medical Imaging, vol.23, issue.3, pp.340-349, 2004. ,
DOI : 10.1109/TMI.2004.824237
Semiautomatic 3-D Prostate Segmentation from TRUS Images Using Spherical Harmonics, Semiautomatic 3D Prostate Segmentation from TRUS Images Using Spherical Harmonics, pp.1645-54, 2006. ,
DOI : 10.1109/TMI.2006.884630
Segmentation of Prostate from 3D Ultrasound Volumes Using Shape and Intensity Priors in Level Set Framework, Proceedings of the 28th, IEEE Engineering in Medicine and Biology Society, pp.2341-2345, 2006. ,
Image processing via level set curvature flow., Proceedings of the National Academy of Sciences, vol.92, issue.15, pp.7046-50, 1995. ,
DOI : 10.1073/pnas.92.15.7046
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC41468
Prostate Segmentation in HIFU Therapy, IEEE Transactions on Medical Imaging, vol.30, issue.3, pp.792-803, 2011. ,
DOI : 10.1109/TMI.2010.2095465
URL : https://hal.archives-ouvertes.fr/inserm-00580194
Differential Segmentation of the Prostate in MR Images Using Combined 3D Shape Modelling and Voxel Classification, 3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano, 2006., pp.410-413, 2006. ,
DOI : 10.1109/ISBI.2006.1624940
Automated segmentation of the prostate in 3D MR images using a probabilistic atlas and a spatially constrained deformable model, Medical Physics, vol.2353, issue.8, pp.1579-90, 2010. ,
DOI : 10.1118/1.3315367
URL : https://hal.archives-ouvertes.fr/hal-00456598
A shape-based approach to the segmentation of medical imagery using level sets, IEEE Transactions on Medical Imaging, vol.22, issue.2, pp.137-54, 2003. ,
DOI : 10.1109/TMI.2002.808355
Coupled Multi-shape Model and Mutual Information for Medical Image Segmentation, International Conference, Information Processing in Medical Imaging, pp.185-97, 2003. ,
A Threshold Selection Method from Gray-Level Histograms, IEEE Transactions on Systems, Man, and Cybernetics, vol.9, issue.1, pp.62-68, 1979. ,
DOI : 10.1109/TSMC.1979.4310076
Novel Stochastic Framework for Accurate Segmentation of Prostate in Dynamic Contrast Enhanced MRI, Prostate Cancer Imaging, pp.121-151, 2010. ,
DOI : 10.1007/978-3-642-15989-3_14
El-Baz, A new 3d automatic segmentation framework for accurate segmentation of prostate from dce-mri, International Symposium on Biomedical Imaging: From Nano to Macro, pp.1476-1485, 2011. ,
Boundary Delineation in Prostate Imaging Using Active Contour Segmentation Method with Interactively Defined Object Regions, Prostate Cancer Imaging, pp.131-173, 2010. ,
DOI : 10.1007/978-3-642-15989-3_15
URL : https://hal.archives-ouvertes.fr/hal-00527645
A Coupled Global Registration and Segmentation Framework With Application to Magnetic Resonance Prostate Imagery, IEEE Transactions on Medical Imaging, vol.29, issue.10, pp.17-81, 2010. ,
DOI : 10.1109/TMI.2010.2052065
Accurate Prostate Volume Estimation Using Multifeature Active Shape Models on T2-weighted MRI, Academic Radiology, vol.18, issue.6, pp.745-54, 2011. ,
DOI : 10.1016/j.acra.2011.01.016
A magnetic resonance spectroscopy driven initialization scheme for active shape model based prostate segmentation, Medical Image Analysis, vol.15, issue.2, pp.214-239, 2011. ,
DOI : 10.1016/j.media.2010.09.002
Facilitating 3D Spectroscopic Imaging through Automatic Prostate Localization in MR Images Using Random Walker Segmentation Initialized via Boosted Classifiers, Prostate Cancer Imaging, pp.47-56, 2011. ,
DOI : 10.1023/B:VISI.0000013087.49260.fb
Random Walks for Image Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.11, pp.1768-83, 2006. ,
DOI : 10.1109/TPAMI.2006.233
Model-based segmentation of medical imagery by matching distributions, IEEE Transactions on Medical Imaging, vol.24, issue.3, pp.281-92, 2005. ,
DOI : 10.1109/TMI.2004.841228
Constrained Surface Evolutions for Prostate and Bladder Segmentation in CT Images, First International Workshop, Computer Vision for Biomedical Image Applications, pp.251-60, 2005. ,
DOI : 10.1007/11569541_26
Automatic Segmentation of Intra-treatment CT Images for Adaptive Radiation Therapy of the Prostate, Medical Image Computing and Computer-Assisted Intervention- MICCAI, pp.442-50, 2005. ,
DOI : 10.1007/11566465_55
Automatic Segmentation of Bladder and Prostate Using Coupled 3D Deformable Models, Medical Image Computing and Computer-Assisted Intervention MICCAI, pp.252-60, 2007. ,
DOI : 10.1007/978-3-540-75757-3_31
URL : https://hal.archives-ouvertes.fr/inria-00616041
Optimal Graph Search Segmentation Using Arc-Weighted Graph for Simultaneous Surface Detection of Bladder and Prostate, Medical Image Computing and Computer-Assisted Intervention MICCAI, pp.827-862, 2009. ,
DOI : 10.1007/978-3-642-04271-3_100
Simultaneous detection of multiple elastic surfaces with application to tumor segmentation in CT images, Medical Imaging 2008: Image Processing ,
DOI : 10.1117/12.770516
Graph Search with Appearance and Shape Information for 3-D Prostate and Bladder Segmentation, Medical Image Computing and Computer-Assisted Intervention MICCAI, pp.172-80, 2010. ,
DOI : 10.1007/978-3-642-15711-0_22
3D Meshless Prostate Segmentation and Registration in Image Guided Radiotherapy, Medical Image Computing and Computer-Assisted Intervention MICCAI, pp.43-50, 2009. ,
DOI : 10.1007/978-3-642-04268-3_6
Level Set Segmentation with Both Shape and Intensity Priors, International Conference on Computer Vision, pp.763-70, 2009. ,
Segmenting CT prostate images using population and patient-specific statistics for radiotherapy, Medical Physics, vol.11, issue.8, 2010. ,
DOI : 10.1118/1.3464799
Learning Image Context for Segmentation of Prostate in CT-Guided Radiotherapy, Medical Image Computing and Computer-Assisted Intervention -MICCAI, pp.570-578, 2011. ,
DOI : 10.1023/B:VISI.0000013087.49260.fb
A Learning Based Hierarchical Framework for Automatic Prostate Localization in CT Images, Prostate Cancer Imaging, pp.1-9, 2011. ,
DOI : 10.1118/1.3464799
An integrated approach to segmentation and nonrigid registration for application in image-guided pelvic radiotherapy, Medical Image Analysis, vol.15, issue.5, pp.772-85, 2011. ,
DOI : 10.1016/j.media.2011.05.010
Segmenting the prostate and rectum in CT imagery using anatomical constraints, Medical Image Analysis, vol.15, issue.1, pp.1-11, 2011. ,
DOI : 10.1016/j.media.2010.06.004
Calculating the Hausdorff distance between curves, Information Processing Letters, vol.64, issue.1, pp.17-22, 1997. ,
DOI : 10.1016/S0020-0190(97)00140-3
Definition of the prostate in CT and MRI: a multi-observer study, International Journal of Radiation Oncology*Biology*Physics, vol.43, issue.1, pp.57-66, 1999. ,
DOI : 10.1016/S0360-3016(98)00351-4
Real-time MRI-TRUS fusion for guidance of targeted prostate biopsies, Computer Aided Surgery, vol.13, issue.5, pp.255-64, 2008. ,
DOI : 10.1007/978-3-540-75759-7_4
Empirical evaluation of bias field correction algorithms for computer-aided detection of prostate cancer on T2w MRI, Medical Imaging 2011: Computer-Aided Diagnosis, pp.79630-79642, 2011. ,
DOI : 10.1117/12.878813
A nonparametric method for automatic correction of intensity nonuniformity in MRI data, IEEE Transactions on Medical Imaging, vol.17, issue.1, pp.87-97, 1998. ,
DOI : 10.1109/42.668698
Parametric estimate of intensity inhomogeneities applied to MRI, IEEE Transactions on Medical Imaging, vol.19, issue.3, pp.153-65, 2000. ,
DOI : 10.1109/42.845174
Rapid and effective correction of RF inhomogeneity for high field magnetic resonance imaging, Human Brain Mapping, vol.11, issue.4, pp.204-215, 2000. ,
DOI : 10.1002/1097-0193(200008)10:4<204::AID-HBM60>3.0.CO;2-2
Automatic atlas-based segmentation of the prostate, www.wiki.namic .org/Wiki, 2009. ,
Segmenting the prostate and rectum in ct imagery using anatomical constraints, Medical Physics, pp.38-6351, 2011. ,
Fast, accurate, and robust automatic marker detection for motion correction based on oblique kV or MV projection image pairs, Medical Physics, vol.26, issue.8, pp.1554-1559, 2010. ,
DOI : 10.1118/1.3355871
3D Prostate Boundary Segmentation From Ultrasound Images Using 2D Active Shape Models, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, pp.2337-2377, 2006. ,
DOI : 10.1109/IEMBS.2006.260668
Prostate ultrasound image segmentation using level set-based region flow with shape guidance, Medical Imaging 2005: Image Processing, pp.1648-57, 2005. ,
DOI : 10.1117/12.594403
3D Yes No DM -Mesh No Volume VS 89% 10 data sets Shen, 2D No Gabor features DM -ASM Yes Contour MD 3.2(?1.28 mm) ± 0.87 pixels 8 images Area OE 3.98±0.97% Area error 1.66±1.68%, 2001. ,
2D No Median and morphological filering DM -ASM No Contour MD 3.77(?2.55 mm) ± 1.3 pixels 10 images Contour MaxD 6, ?4.18 mm) ± 1.8 pixels Area OV 93%±0.9%, 2004. ,
3D Yes Median filter DM -ASM No Contour MD 0.12±0.45 mm 36 data sets Contour MAD 1, 09±0.49 mm Contour MaxD 7.27±2.32 mm, 2006. ,
28 mm 5 data sets Contour MAD 1.19±0.14 mm Contour MaxD 7.01±1.04 mm Volume VD 7.2±3.4% Gong [90] 2004 2D Yes No DM -Curve Fitting No Contour MD 1.36±0.58 mm 125 images Contour HD 3.42±1.52 mm Badiel [56] 2006 2D No No DM -Curve Fitting No Area SN 97, 3D Yes No DM -Curve Fitting No Contour MD4±1% 17 images Area AC 93.5±1.9% Contour MAD 0.67±0.18 mm Contour MaxD 2.25±0.56 mm Mahdavi[58] 2011 3D Yes No DM -Curve Fitting No Volume VE 6.63±0.9% 21 data sets, 2002. ,