An edge-based active contour model using an inflation/deflation force with a damping coefficient

A much easier initialisation of the contour than in the basic model.Automatically removing self-crossings and loops of the active contour.Automatically dampening the inflation/deflation force of the moving nodes.Automatically adding new nodes and removing superfluous ones. This publication presents an edge-based active contour model using the inflation/deflation force, allowing active contour nodes to be moved to find object boundaries in a digital image. The methods proposed in this study make it possible to keep a high value of the inflation/deflation force for each node until the node approaches the boundary of the analysed shape. After the boundary searched for is reached, the value of the inflation/deflation force for these nodes is automatically damped. The solutions used in this paper are of major practical significance if the analysed images contain weak boundaries and/or strong noise at the same time, and on top of that there are strictures of the shape which should be approximated. Experiments were carried out for artificial images as well as USG and MRI medical images, and have confirmed the suitability of the solutions used.

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

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

[3]  Y. V. Venkatesh,et al.  Self-organizing neural networks based on spatial isomorphism for active contour modeling , 2000, Pattern Recognit..

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

[5]  Mohamed Medhat Gaber,et al.  A Survey of SOM-Based Active Contour Models for Image Segmentation , 2014, WSOM.

[6]  Junaed Sattar.  Snakes , Shapes and Gradient Vector Flow , 2022 .

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

[8]  Allen R. Tannenbaum,et al.  Self-Crossing Detection and Location for Parametric Active Contours , 2012, IEEE Transactions on Image Processing.

[9]  Bing Li,et al.  Active Contour External Force Using Vector Field Convolution for Image Segmentation , 2007, IEEE Transactions on Image Processing.

[10]  Alistair B. Forbes,et al.  Spline Approximation Using Knot Density Functions , 2007 .

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

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

[13]  Rachid Deriche,et al.  Geodesic active regions and level set methods for motion estimation and tracking , 2005, Comput. Vis. Image Underst..

[14]  Shigang Liu,et al.  A local region-based Chan-Vese model for image segmentation , 2012, Pattern Recognit..

[15]  Guopu Zhu,et al.  Boundary-based image segmentation using binary level set method , 2007 .

[16]  Luminita A. Vese,et al.  Self-Repelling Snakes for Topology-Preserving Segmentation Models , 2008, IEEE Transactions on Image Processing.

[17]  M. Kimmel,et al.  Conflict of interest statement. None declared. , 2010 .

[18]  John G. Csernansky,et al.  Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults , 2007, Journal of Cognitive Neuroscience.

[19]  Arun Ross,et al.  Iris Segmentation Using Geodesic Active Contours , 2009, IEEE Transactions on Information Forensics and Security.

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

[21]  CiecholewskiMarcin.  An edge-based active contour model using an inflation/deflation force with a damping coefficient , 2016 .

[22]  Muhammed Fatih Talu,et al.  ORACM: Online region-based active contour model , 2013, Expert Syst. Appl..

[23]  Mohamed Medhat Gaber,et al.  On the Relationship between Variational Level Set-Based and SOM-Based Active Contours , 2015, Comput. Intell. Neurosci..

[24]  Koen L. Vincken,et al.  Probabilistic segmentation of white matter lesions in MR imaging , 2004, NeuroImage.

[25]  Reza Safabakhsh,et al.  A TASOM-based algorithm for active contour modeling , 2003, Pattern Recognit. Lett..

[26]  Chunming Li,et al.  Level set evolution without re-initialization: a new variational formulation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[27]  Xinge You,et al.  A local region based approach to lip tracking , 2012, Pattern Recognit..

[28]  Luis Álvarez,et al.  Morphological snakes , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[29]  Myoung-Hee Kim,et al.  A non-self-intersecting adaptive deformable surface for complex boundary extraction from volumetric images , 2001, Comput. Graph..

[30]  Anthony J. Yezzi,et al.  A Fully Global Approach to Image Segmentation via Coupled Curve Evolution Equations , 2002, J. Vis. Commun. Image Represent..

[31]  David A. Clausi,et al.  Multi-scale tensor vector field active contour , 2012, 2012 19th IEEE International Conference on Image Processing.

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

[33]  Andrzej Materka,et al.  Two-phase active contour method for semiautomatic segmentation of the heart and blood vessels from MRI images for 3D visualization. , 2002, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.