Multi-feature gradient vector flow snakes for adaptive segmentation of the ultrasound images of breast cancer

Journal of Visual Communication and Image Representation - Tập 24 Số 8 - Trang 1414-1430 - 2013
Annupan Rodtook1, Stanislav S. Makhanov2
1Department of Computer Science, Ramkhamhaeng University, Bangkok 10240, Thailand
2School of Information and Computer Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12000, Thailand#TAB#

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Kass, 1988, Snakes: active contour models, International Journal of Computer Vision, 1, 321, 10.1007/BF00133570

Lefebvre, 1998, Automatic detection of the boundary of the calcaneus from ultrasound parametric images using an active contour model; clinical assessment, IEEE Transactions on Medical Imaging, 17, 45, 10.1109/42.668693

Y.S. Akgul, C. Kambhamettu, M. Stone, Extraction and tracking of the tongue surface from ultrasound image sequences, in: International IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Santa Barbara, California, 1998, pp. 298–303.

R. Chung, C.K. Ho, Using 2D active contour models for 3D reconstruction from serial sections, in: Proceedings of the 13th International IEEE Conference on Pattern Recognition, Vienna, Austria, 1996, pp. 849–853.

Fenster, 1998, Three-dimensional ultrasound imaging system for prostate cancer diagnosis and treatment, IEEE Transactions on Instrumentation and Measurement, 47, 1439, 10.1109/19.746709

Strintzis, 1997, Maximum likelihood motion estimation in ultrasound image sequences, IEEE Signal Processing Letters, 4, 156, 10.1109/97.586034

Chen, 2003, 3-D breast ultrasound segmentation using active contour model, Ultrasound in Medicine and Biology, 29, 1017, 10.1016/S0301-5629(03)00059-0

Chang, 2003, Segmentation of breast tumor in three-dimensional ultrasound images using three-dimensional discrete active contour model, Ultrasound in Medicine and Biology, 29, 1571, 10.1016/S0301-5629(03)00992-X

Cvancarova, 2005, Segmentation of ultrasound images of liver tumors applying snake algorithms and GVF, Congress Series, 1281, 218, 10.1016/j.ics.2005.03.190

Aleman-Flores, 2005, Computerized ultrasound characterization of breast tumors, International Congress Series, 1281, 1063, 10.1016/j.ics.2005.03.157

Cohen, 1991, On active contour models and balloon, CVGIP: Image Understanding, 53, 211, 10.1016/1049-9660(91)90028-N

Cohen, 1993, Finite-element methods for active contour models and balloons for 2-D and 3-D images, IEEE Transactions on Pattern Analysis and Machine Intelligence, 15, 1131, 10.1109/34.244675

Pujol, 2005, Fundamentals of stop and go active models, Image Vision and Computing, 23, 681, 10.1016/j.imavis.2005.03.007

Tang, 2009, A multi-direction GVF snake for the segmentation of skin cancer images, Pattern Recognition, 42, 1172, 10.1016/j.patcog.2008.09.007

Shih, 2007, Locating object contours in complex background using improved snakes, Computer Vision and Image Understanding, 105, 93, 10.1016/j.cviu.2006.08.007

Dagher, 2008, Waterballoons: a hybrid watershed balloon snake segmentation, Image Vision and Computing, 26, 905, 10.1016/j.imavis.2007.10.010

Zhu, 2011, A snake based method for segmentation of intravascular ultrasound images and its in vivo validation, Ultrasonics, 51, 181, 10.1016/j.ultras.2010.08.001

Fenster, 2001, Sectored snakes: evaluating learned energy segmentations, Transactions in Pattern Analysis and Machine Intelligence, 23, 1028, 10.1109/34.955115

Charmi, 2008, Fourier-based geometric shape prior for snakes, Pattern Recognition Letters, 29, 897, 10.1016/j.patrec.2008.01.011

Chesnaud, 1999, Statistical region snake-based segmentation adapted to different physical noise models, IEEE Transactions on Pattern Analysis and Machine Intelligence, 21, 1145, 10.1109/34.809108

Ronfard, 1994, Region-based strategies for active contour models, International Journal of Computer Vision, 13, 229, 10.1007/BF01427153

Zhu, 1996, Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation, IEEE Transactions in Pattern Analysis and Machine Intelligence, 18, 884, 10.1109/34.537343

McInerney, 2000, T-snakes: topology adaptive snakes, Medical Image Analysis, 4, 73, 10.1016/S1361-8415(00)00008-6

Giraldi, 2003, Dual-T-snakes model for medical imaging segmentation, Pattern Recognition Letters, 24, 993, 10.1016/S0167-8655(02)00223-4

H. Delingnette, J. Montagnat, New algorithm for controlling active contours shape and topology, in: Sixth European Conference on Computer Vision (ECCV), Ireland, vol. 2, 2000, pp. 381–395.

Delingette, 2001, Shape and topology constraints on parametric active contours, Computer Vision and Image Understanding, 83, 140, 10.1006/cviu.2001.0920

Malladi, 1995, Shape modeling with front propagation, IEEE Transactions in Pattern Analysis and Machine Intelligence, 17, 158, 10.1109/34.368173

Osher, 1988, Fronts propagating with curvature dependent speed: algorithms based on Hamilton–Jacobi formulation, Journal of Computational Physics, 79, 12, 10.1016/0021-9991(88)90002-2

Caselles, 1997, Geodesic active contours, International Journal of Computer Vision, 22, 61, 10.1023/A:1007979827043

Siddiqi, 1998, Area and length minimizing flows for shape segmentation, IEEE Transactions in Image Processing, 7, 433, 10.1109/83.661193

Wang, 2004, Deformable contour method: a constrained optimization approach, International Journal of Computer Vision, 59, 87, 10.1023/B:VISI.0000020672.14006.ad

He, 2008, A comparative study of deformable contour methods on medical image segmentation, Image and Vision Computing, 26, 141, 10.1016/j.imavis.2007.07.010

Xu, 2000, Image segmentation using deformable models, vol. 2, 129

Li, 2005, Segmentation of external force field for automatic initialization and splitting of snakes, Pattern Recognition, 38, 1947, 10.1016/j.patcog.2004.12.015

Rochery, 2006, Higher order active contours, International Journal of Computer Vision, 69, 27, 10.1007/s11263-006-6851-y

Xu, 1998, Snakes, shapes, and gradient vector flow, IEEE Transaction on Image Processing, 7, 359, 10.1109/83.661186

Xu, 1997, Gradient vector flow: a new external force for snakes, Proceedings of the International IEEE Computer Society Conference on Computer Vision and, Pattern Recognition, 66

Xu, 1998, Generalized gradient vector flow external forces for active contours, Signal Processing, 71, 131, 10.1016/S0165-1684(98)00140-6

Wei, 2004, A fast snake model based on non-linear diffusion for medical image segmentation, Computerized Medical Imaging and Graphics, 28, 109, 10.1016/j.compmedimag.2003.12.002

Mille, 2009, Narrow band region-based active contours and surfaces for 2d and 3d segmentation, Computer Vision and Image Understanding, 113, 946, 10.1016/j.cviu.2009.05.002

Shang, 2008, Region competition based active contour for medical object extraction, Computerized Medical Imaging and Graphics, 32, 109, 10.1016/j.compmedimag.2007.10.004

Jumaat, 2010, Segmentation of masses from breast ultrasound images using parametric active contour algorithm, Procedia – Social and Behavioral Sciences, 8, 640, 10.1016/j.sbspro.2010.12.089

Chen, 2003, 3-D breast ultrasound segmentation using active contour model, Ultrasound in Medicine and Biology, 29, 1017, 10.1016/S0301-5629(03)00059-0

Chang, 2003, Segmentation of breast tumor in three-dimensional ultrasound images using three-dimensional discrete active contour model, Ultrasound in Medicine and Biology, 29, 1571, 10.1016/S0301-5629(03)00992-X

McInerney, 2008, Sketch-snakes: sketch-line initialized snakes for efficient interactive medical image segmentation, Computerized Medical Imaging and Graphics, 32, 331, 10.1016/j.compmedimag.2007.11.004

Hamarneh, 2000, Combining snakes and active shape models for segmenting the human left ventricle in echocardiographic images, Computers in Cardiology, 115

Chen, 2000, An early vision-based snake model for ultrasound image segmentation, Ultrasound in Medicine and Biology, 26, 273, 10.1016/S0301-5629(99)00140-4

Mignotte, 2001, A multiscale optimization approach for the dynamic contour-based boundary detection issue, Computerized Medical Imaging and Graphics, 25, 265, 10.1016/S0895-6111(00)00075-6

Rodtook, 2010, Continuous force field analysis for generalized gradient vector flow field, Pattern Recognition, 43, 3522, 10.1016/j.patcog.2010.04.003

Cheng, 2010, Automated breast cancer detection and classification using ultrasound images: a survey, Pattern Recognition, 43, 299, 10.1016/j.patcog.2009.05.012

Lee, 1980, Digital image enhancement and noise filtering by using local statistics, IEEE Transactions in Pattern Analysis and, Machine Intelligence, PAM1-2, 165, 10.1109/TPAMI.1980.4766994

Frost, 1982, A model for radar images and its application to adaptive digital filtering of multiplicative noise, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-4, 157, 10.1109/TPAMI.1982.4767223

Kuan, 1987, Adaptive restoration of images with speckle, IEEE on Transactions Acoustic, Speech and, Signal Processing, ASSP-35, 373, 10.1109/TASSP.1987.1165131

Gupta, 2007, A versatile technique for visual enhancement of medical ultrasound images, Digital Signal Processing, 17, 542, 10.1016/j.dsp.2006.12.001

Zhang, 2007, Boundary delineation in transrectal ultrasound image for prostate cancer, Computers in Biology and Medicine, 37, 1591, 10.1016/j.compbiomed.2007.02.008

Liu, 2010, Probability density difference-based active contour for ultrasound image segmentation, Pattern Recognition, 43, 2028, 10.1016/j.patcog.2010.01.002

Perona, 1990, Scale-space and edge detection using anisotropic diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, 12, 629, 10.1109/34.56205

Czerwinski, 1999, Detection of lines and boundaries in speckle images – application to medical ultrasound, IEEE Transactions on Medical Imaging, 18, 126, 10.1109/42.759114

Lopes, 1990, Adaptive speckle filters and scene heterogeneity, IEEE Transactions on Geoscience and Remote Sensing, 28, 992, 10.1109/36.62623

Huang, 2007, Level set contouring for breast tumor in sonography, Journal of Digital Imaging, 20, 238, 10.1007/s10278-006-1041-6

Levienaise-Obadia, 1999, Adaptive segmentation of ultrasound images, Image Vision and Computing, 17, 583, 10.1016/S0262-8856(98)00177-2

Yu, 2009, Object density-based image segmentation and its applications in biomedical image analysis, Computer Methods and Programs in Biomedicine, 96, 193, 10.1016/j.cmpb.2009.04.013

Vincent, 1993, Morphological grayscale reconstruction in image analysis Applications and efficient algorithms, IEEE Transactions on Image Processing, 2, 176, 10.1109/83.217222

Mignotte, 2001, A multiscale optimization approach for the dynamic contour-based boundary detection issue, Computerized Medical Imaging and Graphics, 25, 265, 10.1016/S0895-6111(00)00075-6

Ray, 2001, A fast and flexible multiresolution snake with a definite termination criterion, Pattern Recognition, 34, 1483, 10.1016/S0031-3203(00)00077-7

Chucherd, 2010, Phase portrait analysis for multiresolution generalized gradient vector flow, IEICE Transactions on Information and Systems, E93-D, 2822, 10.1587/transinf.E93.D.2822

Fritsch, 1980, Monotone piecewise cubic interpolation, SIAM Journal of Numerical Analysis, 17, 238, 10.1137/0717021

Castelmen, 1996

Chipman, 2006, Hybrid hierarchical clustering with applications to microarray data, Biostatistics, 2, 286

Naldi, 2002, Relaxation schemes for PDEs and applications to fourth order diffusion equations, Surveys on Mathematics for Industry, 10, 315

Hsu, 2008, Automatic segmentation of liver PET images, Computerized Medical Imaging and Graphics, 32, 601, 10.1016/j.compmedimag.2008.07.001

C.Y. Hsu, S.H. Chen, K.L. Wang, Active contour model with a novel image force field, in: Proceeding of the Conference CVGIP-2003, Taiwan, 2003, pp. 477–483.

Hsu, 2012, Automatic extraction of face contours in images and videos, Future Generation Computer Systems, 28, 322, 10.1016/j.future.2010.11.008

Ghita, 2010, A new GVF-based image enhancement formulation for use in the presence of mixed noise, Pattern Recognition, 43, 2646, 10.1016/j.patcog.2010.02.023

Li, 2007, Active contour external force using vector field convolution for image Segmentation, IEEE Transactions on Inage Processing, 16, 2096, 10.1109/TIP.2007.899601

Park, 2001, Active contour model with gradient directional information: Directional snake, IEEE Transactions in Circuits and Systems for Video Technology, 11, 252, 10.1109/76.905991

Cheng, 2006, Dynamic directional gradient vector flow for snakes, IEEE Transactions on Image Processing, 15, 1563, 10.1109/TIP.2006.871140

M.-P. Dubuisson, A.K. Jain, A modified Hausdorff distance for object matching proceedings, in: International Conference on Pattern Recognition, Israel, 1994, pp. 566–568.

Mumford, 1989, Optimal approximation by piecewise smooth functions and associated variational problems, Communications on Pure Applied Mathematics, 42, 577, 10.1002/cpa.3160420503

Vitti, 2012, The Mumford–Shah variational model for image segmentation: an overview of the theory, implementation and use, ISPRS Journal of Photogrammetry and Remote Sensing, 69, 50, 10.1016/j.isprsjprs.2012.02.005

Vese, 2002, A multiphase level set framework for image segmentation using the Mumford and Shah model, International Journal of Computer Vision, 50, 271, 10.1023/A:1020874308076

Yuan, 2012, Adaptive active contours without edges, Mathematical and Computer Modeling, 55, 1705, 10.1016/j.mcm.2011.11.014

L. Chan-Fei1, W. Yao-Nanm, L. Guo-Cai, A new splitting active contour framework based on Chan–Vese piecewise smooth model, Acta Automatica Sinica 34 (6) (2008) 660–664.

Diop, 2013, Bi-planar image segmentation based on variational geometrical active contours with shape priors, Medical Image Analysis, 17, 165, 10.1016/j.media.2012.09.006

Jain, 1996, Object matching using deformable templates, IEEE Transactions on Pattern Analysis and Machine Intelligence, 18, 267, 10.1109/34.485555

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