Physics-based deformable organisms for medical image analysis

Previously, "Deformable organisms" were introduced as a novel paradigm for medical image analysis that uses artificial life modelling concepts. Deformable organisms were designed to complement the classical bottom-up deformable models methodologies (geometrical and physical layers), with top-down intelligent deformation control mechanisms (behavioral and cognitive layers). However, a true physical layer was absent and in order to complete medical image segmentation tasks, deformable organisms relied on pure geometry-based shape deformations guided by sensory data, prior structural knowledge, and expert-generated schedules of behaviors. In this paper we introduce the use of physics-based shape deformations within the deformable organisms framework yielding additional robustness by allowing intuitive real-time user guidance and interaction when necessary. We present the results of applying our physics-based deformable organisms, with an underlying dynamic spring-mass mesh model, to segmenting and labelling the corpus callosum in 2D midsagittal magnetic resonance images.

[1]  R. Poli.  Genetic programming for image analysis , 1996 .

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

[3]  Demetri Terzopoulos,et al.  Artificial Fishes: Autonomous Locomotion, Perception, Behavior, and Learning in a Simulated Physical World , 1994, Artificial Life.

[4]  Richard Lepage,et al.  Knowledge-Based Image Understanding Systems: A Survey , 1997, Comput. Vis. Image Underst..

[5]  Mannes Poel,et al.  A Reinforcement Learning Agent for Minutiae Extraction from Fingerprints , 2001 .

[6]  Yuan Yan Tang,et al.  Adaptive Image Segmentation With Distributed Behavior-Based Agents , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Ghassan Hamarneh,et al.  Physics-based shape deformations for medical image analysis , 2003, IS&T/SPIE Electronic Imaging.

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

[9]  Jack Sklansky,et al.  Finding circles by an array of accumulators , 1975, Commun. ACM.

[10]  M. LeMay,et al.  Abnormalities of the left temporal lobe and thought disorder in schizophrenia. A quantitative magnetic resonance imaging study. , 1992, The New England journal of medicine.

[11]  Robert Allen,et al.  Handbook of Medical Imaging—Processing and Analysis , 2001 .

[12]  Ghassan Hamarneh,et al.  Medial Profiles for Modeling Deformation and Statistical Analysis of Shape and their Use in Medical Image Segmentation , 2004, Int. J. Shape Model..

[13]  Isaac N. Bankman,et al.  Handbook of medical imaging , 2000 .

[14]  Terry S. Yoo,et al.  Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis , 2004 .

[15]  Olivier D. Faugeras,et al.  Statistical shape influence in geodesic active contours , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

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

[17]  Ghassan Hamarneh,et al.  Deformable Organisms for Automatic Medical Image Analysis , 2001, MICCAI.

[18]  Bruce A. Draper,et al.  Knowledge-directed vision: control, learning, and integration , 1996, Proc. IEEE.

[19]  James S. Duncan,et al.  Medical Image Analysis , 1999, IEEE Pulse.

[20]  Ron Kikinis,et al.  Adaptive Template Moderated Spatially Varying Statistical Classification , 1998, MICCAI.

[21]  Vincent Rodin,et al.  oRis: multiagents approach for image processing , 1998, Optics & Photonics.

[22]  Thomas M. Strat,et al.  Context-Based Vision: Recognizing Objects Using Information from Both 2D and 3D Imagery , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Demetri Terzopoulos,et al.  Deformable models in medical image analysis: a survey , 1996, Medical Image Anal..

[24]  Demetri Terzopoulos,et al.  Artificial life for computer graphics , 1999, CACM.

[25]  S. Osher,et al.  Geometric Level Set Methods in Imaging, Vision, and Graphics , 2011, Springer New York.

[26]  Ghassan Hamarneh,et al.  Statistically constrained snake deformations , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[27]  Johan Montagnat,et al.  A review of deformable surfaces: topology, geometry and deformation , 2001, Image Vis. Comput..

[28]  Milan Sonka,et al.  "Handbook of Medical Imaging, Volume 2. Medical Image Processing and Analysis " , 2000 .

[29]  Bruce A. Draper,et al.  Learning Blackboard-Based Scheduling Algorithms for Computer Vision , 1992, Int. J. Pattern Recognit. Artif. Intell..

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

[31]  Martin C. Martin,et al.  Genetic programming for real world robot vision , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[32]  Catherine Garbay,et al.  A Society of Goal-Oriented Agents for the Analysis of Living Cells , 1997, AIME.

[33]  Demetri Terzopoulos,et al.  On Matching Deformable Models to Images , 1987, Topical Meeting on Machine Vision.

[34]  J. Sethian.  Level set methods : evolving interfaces in geometry, fluid mechanics, computer vision, and materials science , 1996 .

[35]  Christopher J. Taylor,et al.  A cooperative framework for segmentation of MRI brain scans , 2000, Artif. Intell. Medicine.

[36]  Ghassan Hamarneh,et al.  Medial Profiles for Modeling and Statistical Analysis of Shape , 2003 .

[37]  Richard A. Robb,et al.  Biomedical Imaging, Visualization, and Analysis , 1999 .

[38]  Ghassan Hamarneh,et al.  Controlled Shape Deformations via Medial Profiles , 2001 .

[39]  Timothy F. Cootes,et al.  Active Appearance Models , 1998, ECCV.