Segmentation of biomedical images using active contour model with robust image feature and shape prior

In this article, a new level set model is proposed for the segmentation of biomedical images. The image energy of the proposed model is derived from a robust image gradient feature which gives the active contour a global representation of the geometric configuration, making it more robust in dealing with image noise, weak edges, and initial configurations. Statistical shape information is incorporated using nonparametric shape density distribution, which allows the shape model to handle relatively large shape variations. The segmentation of various shapes from both synthetic and real images depict the robustness and efficiency of the proposed method. © 2013 The Authors. International Journal for Numerical Methods in Biomedical Engineering published by John Wiley & Sons, Ltd.

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

[2]  Daniel Cremers,et al.  Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation , 2006, International Journal of Computer Vision.

[3]  Karol Miller,et al.  Modelling brain deformations for computer‐integrated neurosurgery , 2010 .

[4]  Nikos Paragios,et al.  Gradient vector flow fast geometric active contours , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[6]  Yingxi Liu,et al.  Computational fluid dynamics simulations of respiratory airflow in human nasal cavity and its characteristic dimension study , 2008 .

[7]  Datian Ye,et al.  Curvelet processing of MRI for local image enhancement , 2012, International journal for numerical methods in biomedical engineering.

[8]  Rachid Deriche,et al.  Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation , 2002, International Journal of Computer Vision.

[9]  Nikos Paragios,et al.  Shape Priors for Level Set Representations , 2002, ECCV.

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

[11]  David A. Steinman,et al.  Image-Based Computational Fluid Dynamics Modeling in Realistic Arterial Geometries , 2002, Annals of Biomedical Engineering.

[12]  Alan S. Willsky,et al.  Nonparametric shape priors for active contour-based image segmentation , 2005, 2005 13th European Signal Processing Conference.

[13]  Yunmei Chen,et al.  Using Prior Shapes in Geometric Active Contours in a Variational Framework , 2002, International Journal of Computer Vision.

[14]  Sharmila Majumdar,et al.  A Local Adaptive Threshold Strategy for High Resolution Peripheral Quantitative Computed Tomography of Trabecular Bone , 2007, Annals of Biomedical Engineering.

[15]  Guo-Wei Wei,et al.  Partial differential equation transform—Variational formulation and Fourier analysis , 2011, International journal for numerical methods in biomedical engineering.

[16]  Olaf Hellwich,et al.  A threestepped coordinated level set segmentation method for identifying atherosclerotic plaques on MR-images , 2009 .

[17]  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 .

[18]  C. Goodall.  Procrustes methods in the statistical analysis of shape , 1991 .

[19]  Xianghua Xie,et al.  Level set segmentation with robust image gradient energy and statistical shape prior , 2011, 2011 18th IEEE International Conference on Image Processing.

[20]  Lei Zhang,et al.  Active contours driven by local image fitting energy , 2010, Pattern Recognit..

[21]  Xianghua Xie,et al.  Geometric Potential Force for the Deformable Model , 2009, BMVC.

[22]  Katsushi Ikeuchi,et al.  Geodesic Active Contour , 2014, Computer Vision, A Reference Guide.

[23]  Bernhard G. Bodmann,et al.  Texture-based tissue characterization for high-resolution CT scans of coronary arteries , 2009 .

[24]  Ron Kimmel,et al.  Segmentation of thin structures in volumetric medical images , 2006, IEEE Transactions on Image Processing.

[25]  Tao Li,et al.  A robust parametric active contour based on fourier descriptors , 2011, 2011 18th IEEE International Conference on Image Processing.

[26]  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).

[27]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[28]  Ponnada A. Narayana,et al.  Volume and Shape in Feature Space on Adaptive FCM in MRI Segmentation , 2008, Annals of Biomedical Engineering.

[29]  Yue Wang,et al.  Adaptive B‐Snake model using shape and appearance information for object segmentation , 2011 .

[30]  J E Gil,et al.  Efficient biomarkers for the characterization of bone tissue , 2012, International journal for numerical methods in biomedical engineering.

[31]  Xianghua Xie,et al.  Modelling pipeline for subject‐specific arterial blood flow—A review , 2011 .

[32]  Yogesh Rathi,et al.  A Framework for Image Segmentation Using Shape Models and Kernel Space Shape Priors , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  Elisabetta Binaghi,et al.  Automatic MRI 2D brain segmentation using graph searching technique , 2013, International journal for numerical methods in biomedical engineering.

[34]  J. S. Sahambi,et al.  Modified active contour model and Random Walk approach for left ventricular cardiac MR image segmentation , 2011 .

[35]  Xianghua Xie,et al.  Geometrically Induced Force Interaction for Three-Dimensional Deformable Models , 2011, IEEE Transactions on Image Processing.

[36]  J. Peiro,et al.  On the segmentation of vascular geometries from medical images , 2010 .

[37]  Zhen Ma,et al.  Segmentation of female pelvic cavity in axial T2‐weighted MR images towards the 3D reconstruction , 2012, International journal for numerical methods in biomedical engineering.

[38]  Siqi Chen,et al.  Level set segmentation with both shape and intensity priors , 2009, 2009 IEEE 12th International Conference on Computer Vision.

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

[40]  James A. Sethian,et al.  Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid , 2012 .

[41]  John W. Fisher,et al.  Submitted to Ieee Transactions on Image Processing a Nonparametric Statistical Method for Image Segmentation Using Information Theory and Curve Evolution , 2022 .

[42]  W. Eric L. Grimson,et al.  Model-based curve evolution technique for image segmentation , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.