Method of the active contour for segmentation of bone systems on bitmap images

It is developed within a method of the active contours the approach, which is allowing to realize separation of a contour of a object of the image in case of its segmentation. This approach exceeds a parametric method on speed, but also does not concede to it on decision accuracy. The approach is offered within this operation will allow to realize allotment of a contour with high accuracy of the image and quicker than a parametric method of the active contours.

[1]  Michael Unser,et al.  Parametric B-spline snakes on distance maps—Application to segmentation of histology images , 2008, 2008 16th European Signal Processing Conference.

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

[3]  I. V. Kirillova,et al.  Patient-specific system for prognosis of surgical treatment outcomes of human cardiovascular system , 2015, Saratov Fall Meeting.

[4]  Hichem Frigui,et al.  Incorporating shape prior into active contours with a sparse linear combination of training shapes: Application to corpus callosum segmentation , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[5]  Hichem Frigui,et al.  3-D Active Contour Segmentation Based on Sparse Linear Combination of Training Shapes (SCoTS) , 2017, IEEE Transactions on Medical Imaging.

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

[7]  Aly A. Farag,et al.  A Novel Approach for Lung Nodules Segmentation in Chest CT Using Level Sets , 2013, IEEE Transactions on Image Processing.

[8]  Lubomir M. Hadjiiski,et al.  Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours. , 2006, Medical physics.

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

[10]  I. V. Kirillova,et al.  Morphology and biomechanics of human heart , 2016, SPIE BiOS.

[11]  智一 吉田,et al.  Efficient Graph-Based Image Segmentationを用いた圃場図自動作成手法の検討 , 2014 .

[12]  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).

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

[14]  I. V. Kirillova,et al.  Patient-specific modeling of human cardiovascular system elements , 2016, SPIE BiOS.

[15]  Sven J. Dickinson,et al.  TurboPixels: Fast Superpixels Using Geometric Flows , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  V. Caselles,et al.  A geometric model for active contours in image processing , 1993 .

[17]  Sergiu Nedevschi,et al.  Iterative Methods for Obtaining Energy-Minimizing Parametric Snakes with Applications to Medical Imaging , 2012, Comput. Math. Methods Medicine.

[18]  Pablo Márquez Neila.  Higher-order regularization and morphological techniques for image segmentation , 2015 .

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

[20]  Guido Gerig,et al.  User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability , 2006, NeuroImage.