A level-set approach to joint image segmentation and registration with application to CT lung imaging

Highlights • A simple novel joint image registration and segmentation method is presented.• The new algorithm is based on a level-set formulation.• The algorithm merges Chan–Vese segmentation with active dense displacement estimation.• Numerical implementation is evaluated on a publicly available lung CT data set.• Improvement of registration and segmentation properties compared with existing methods is shown.

[1]  Joachim Weickert,et al.  A Review of Nonlinear Diffusion Filtering , 1997, Scale-Space.

[2]  Heinz Handels,et al.  Pulmonary lobe segmentation with level sets , 2012, Medical Imaging.

[3]  N. Paragios A level set approach for shape-driven segmentation and tracking of the left ventricle , 2003, IEEE Transactions on Medical Imaging.

[4]  Martin Rumpf,et al.  Multiscale Joint Segmentation and Registration of Image Morphology , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Gregory G. Slabaugh,et al.  Estimation of Vector Fields in Unconstrained and Inequality Constrained Variational Problems for Segmentation and Registration , 2008, Journal of Mathematical Imaging and Vision.

[6]  Nikos Paragios,et al.  Non-rigid registration using distance functions , 2003, Comput. Vis. Image Underst..

[7]  A. Yezzi,et al.  A variational framework for joint segmentation and registration , 2001, Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2001).

[8]  Valerie Duay,et al.  Active deformation fields: Dense deformation field estimation for atlas-based segmentation using the active contour framework , 2011, Medical Image Anal..

[9]  L. Ambrosio,et al.  Gradient Flows: In Metric Spaces and in the Space of Probability Measures , 2005 .

[10]  R. Castillo,et al.  A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets , 2009, Physics in medicine and biology.

[11]  Stanley Osher,et al.  REVIEW ARTICLE: Level Set Methods and Their Applications in Image Science , 2003 .

[12]  Jan Rühaak,et al.  Highly accurate fast lung CT registration , 2013, Medical Imaging.

[13]  Laurent Risser,et al.  Complex Lung Motion Estimation via Adaptive Bilateral Filtering of the Deformation Field , 2013, MICCAI.

[14]  J. Sethian,et al.  FRONTS PROPAGATING WITH CURVATURE DEPENDENT SPEED: ALGORITHMS BASED ON HAMILTON-JACOB1 FORMULATIONS , 2003 .

[15]  D. Juric,et al.  A front-tracking method for the computations of multiphase flow , 2001 .

[16]  David Gavaghan,et al.  Review of automatic pulmonary lobe segmentation methods from CT , 2015, Comput. Medical Imaging Graph..

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

[18]  Stanley Osher,et al.  Implicit and Nonparametric Shape Reconstruction from Unorganized Data Using a Variational Level Set Method , 2000, Comput. Vis. Image Underst..

[19]  Paul J Keall,et al.  Tumor and normal tissue motion in the thorax during respiration: Analysis of volumetric and positional variations using 4D CT. , 2007, International journal of radiation oncology, biology, physics.

[20]  L. Xing,et al.  Overview of image-guided radiation therapy. , 2006, Medical dosimetry : official journal of the American Association of Medical Dosimetrists.

[21]  Stephen T. C. Wong,et al.  oint registration and segmentation of serial lung CT images for image-guided ung cancer diagnosis and therapy , 2009 .

[22]  Y. Chen,et al.  Image registration via level-set motion: Applications to atlas-based segmentation , 2003, Medical Image Anal..

[23]  Tony F. Chan,et al.  A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model , 2002, International Journal of Computer Vision.

[24]  D. Mumford,et al.  Optimal approximations by piecewise smooth functions and associated variational problems , 1989 .

[25]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[26]  Thomas Guerrero,et al.  Quantification of regional ventilation from treatment planning CT. , 2005, International journal of radiation oncology, biology, physics.

[27]  Benoit M. Dawant,et al.  Morphometric analysis of white matter lesions in MR images: method and validation , 1994, IEEE Trans. Medical Imaging.

[28]  Luminita A. Vese,et al.  A combined segmentation and registration framework with a nonlinear elasticity smoother , 2009, Comput. Vis. Image Underst..

[29]  Laurent Risser,et al.  An implicit sliding-motion preserving regularisation via bilateral filtering for deformable image registration , 2014, Medical Image Anal..

[30]  Mattias P. Heinrich,et al.  Advances and challenges in deformable image registration: From image fusion to complex motion modelling , 2016, Medical Image Anal..

[31]  Rachid Deriche,et al.  A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape , 2007, International Journal of Computer Vision.

[32]  W. Eric L. Grimson,et al.  A shape-based approach to the segmentation of medical imagery using level sets , 2003, IEEE Transactions on Medical Imaging.

[33]  Tony F. Chan,et al.  Active Contours without Edges for Vector-Valued Images , 2000, J. Vis. Commun. Image Represent..

[34]  R. Castillo,et al.  Four-dimensional deformable image registration using trajectory modeling , 2010, Physics in medicine and biology.

[35]  Mark Sussman,et al.  An Efficient, Interface-Preserving Level Set Redistancing Algorithm and Its Application to Interfacial Incompressible Fluid Flow , 1999, SIAM J. Sci. Comput..