Continuous force field analysis for generalized gradient vector flow field

We propose a modification of the generalized gradient vector flow field techniques based on a continuous force field analysis. At every iteration the generalized gradient vector flow method obtains a new, improved vector field. However, the numerical procedure always employs the original image to calculate the gradients used in the source term. The basic idea developed in this paper is to use the resulting vector field to obtain an improved edge map and use it to calculate a new gradient based source term. The improved edge map is evaluated by new continuous force field analysis techniques inspired by a preceding discrete version. The approach leads to a better convergence and better segmentation accuracy as compared to several conventional gradient vector flow type methods.

[1]  Xun Wang,et al.  A comparative study of deformable contour methods on medical image segmentation , 2008, Image Vis. Comput..

[2]  Baba C. Vemuri,et al.  Shape Modeling with Front Propagation: A Level Set Approach , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Berkman Sahiner,et al.  An adaptive density-weighted contrast enhancement filter for mammographic breast mass detection , 1996, IEEE Trans. Medical Imaging.

[4]  Alexander A. Sawchuk,et al.  Adaptive Restoration Of Images With Speckle , 1983, Optics & Photonics.

[5]  Xun Wang,et al.  Deformable Contour Method: A Constrained Optimization Approach , 2004, International Journal of Computer Vision.

[6]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Chandra Kambhamettu,et al.  Extraction and tracking of the tongue surface from ultrasound image sequences , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[8]  Jerry L. Prince,et al.  Generalized gradient vector flow external forces for active contours , 1998, Signal Process..

[9]  Mark S. Nixon,et al.  Biased motion-adaptive temporal filtering for speckle reduction in echocardiography , 1996, IEEE Trans. Medical Imaging.

[10]  Fritz Albregtsen,et al.  Segmentation of ultrasound images of liver tumors applying snake algorithms and GVF , 2005 .

[11]  Tony Lindeberg,et al.  Scale-Space Theory in Computer Vision , 1993, Lecture Notes in Computer Science.

[12]  Pascal Laugier,et al.  Automatic detection of the boundary of the calcaneus from ultrasound parametric images using an active contour model; clinical assessment , 1998, IEEE Transactions on Medical Imaging.

[13]  Aaron Fenster,et al.  Three-dimensional ultrasound imaging system for prostate cancer diagnosis and treatment , 1998, IEEE Trans. Instrum. Meas..

[14]  Woo Kyung Moon,et al.  Segmentation of breast tumor in three-dimensional ultrasound images using three-dimensional discrete active contour model. , 2003, Ultrasound in medicine & biology.

[15]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[16]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[17]  Jerry L. Prince,et al.  Gradient vector flow: a new external force for snakes , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[18]  Anthony J. Yezzi,et al.  A geometric snake model for segmentation of medical imagery , 1997, IEEE Transactions on Medical Imaging.

[19]  Ramesh C. Jain,et al.  Vector field analysis for oriented patterns , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[20]  Chunming Li,et al.  Segmentation of external force field for automatic initialization and splitting of snakes , 2005, Pattern Recognit..

[21]  Kaleem Siddiqi,et al.  Area and length minimizing flows for shape segmentation , 1998, IEEE Trans. Image Process..

[22]  Jerry L. Prince,et al.  Snakes, shapes, and gradient vector flow , 1998, IEEE Trans. Image Process..

[23]  Gilson A. Giraldi,et al.  Dual-T-Snakes model for medical imaging segmentation , 2003, Pattern Recognit. Lett..

[24]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[25]  Ronald Chung,et al.  Using 2D active contour models for 3D reconstruction from serial sections , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[26]  M. Strintzis,et al.  Maximum likelihood motion estimation in ultrasound image sequences , 1997, IEEE Signal Processing Letters.

[27]  Alexander A. Sawchuk,et al.  Adaptive restoration of images with speckle , 1987, IEEE Trans. Acoust. Speech Signal Process..

[28]  Frédéric Galland,et al.  Minimum description length synthetic aperture radar image segmentation , 2003, IEEE Trans. Image Process..

[29]  Azriel Rosenfeld,et al.  Picture Processing and Psychopictorics , 1970 .

[30]  Faouzi Ghorbel,et al.  Fourier-based geometric shape prior for snakes , 2008, Pattern Recognit. Lett..

[31]  Irwin Sobel,et al.  An Isotropic 3×3 image gradient operator , 1990 .

[32]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[33]  Luis Weruaga,et al.  Convergence analysis of active contours , 2008, Image Vis. Comput..

[34]  Laurent D. Cohen,et al.  Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[35]  Laurent D. Cohen,et al.  On active contour models and balloons , 1991, CVGIP Image Underst..

[36]  Herbert Freeman,et al.  Machine Vision for Three-Dimensional Scenes , 1990 .

[37]  Alan L. Yuille,et al.  Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[38]  Jun Li,et al.  Nonlinear phase portrait modeling of fingerprint orientation , 2004, ICARCV 2004 8th Control, Automation, Robotics and Vision Conference, 2004..

[39]  Josiane Zerubia,et al.  Higher Order Active Contours , 2006, International Journal of Computer Vision.

[40]  Chongzhao Han,et al.  Force field analysis snake: an improved parametric active contour model , 2005, Pattern Recognit. Lett..

[41]  Johan Montagnat,et al.  New Algorithms for Controlling Active Contours Shape and Topology , 2000, ECCV.

[42]  Hui-Fuang Ng,et al.  Automatic thresholding for defect detection , 2004, Third International Conference on Image and Graphics (ICIG'04).

[43]  James S. Duncan,et al.  Deformable boundary finding in medical images by integrating gradient and region information , 1996, IEEE Trans. Medical Imaging.

[44]  John R. Kender,et al.  Sectored Snakes: Evaluating Learned-Energy Segmentations , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[45]  李幼升,et al.  Ph , 1989 .

[46]  Christophe Chesnaud,et al.  Statistical Region Snake-Based Segmentation Adapted to Different Physical Noise Models , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[47]  Demetri Terzopoulos,et al.  T-snakes: Topology adaptive snakes , 2000, Medical Image Anal..

[48]  Rémi Ronfard,et al.  Region-based strategies for active contour models , 1994, International Journal of Computer Vision.

[49]  Bjørn Olstad,et al.  Encoding of a priori Information in Active Contour Models , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[50]  Luis Álvarez-León,et al.  Computerized ultrasound characterization of breast tumors , 2005 .

[51]  Ruey-Feng Chang,et al.  3-D breast ultrasound segmentation using active contour model. , 2003, Ultrasound in medicine & biology.

[52]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[53]  Min Wei,et al.  A fast snake model based on non-linear diffusion for medical image segmentation. , 2004, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[54]  Johan Montagnat,et al.  Shape and Topology Constraints on Parametric Active Contours , 2001, Comput. Vis. Image Underst..

[55]  Jinshan Tang A multi-direction GVF snake for the segmentation of skin cancer images , 2009, Pattern Recognit..

[56]  Jun Li,et al.  Constrained nonlinear models of fingerprint orientations with prediction , 2006, Pattern Recognit..

[57]  Jerry L Prince,et al.  Image Segmentation Using Deformable Models , 2000 .