Interactive image segmentation framework based on control theory

Segmentation of anatomical structures in medical imagery is a key step in a variety of clinical applications. Designing a generic, automated method that works for various structures and imaging modalities is a daunting task. Instead of proposing a new specific segmentation algorithm, in this paper, we present a general design principle on how to integrate user interactions from the perspective of control theory. In this formulation, Lyapunov stability analysis is employed to design an interactive segmentation system. The effectiveness and robustness of the proposed method are demonstrated.

[1]  Jasjit S. Suri,et al.  Computer Vision, Pattern Recognition and Image Processing in Left Ventricle Segmentation: The Last 50 Years , 2000, Pattern Analysis & Applications.

[2]  Andrew Blake,et al.  "GrabCut" , 2004, ACM Trans. Graph..

[3]  Allen R. Tannenbaum,et al.  Localizing Region-Based Active Contours , 2008, IEEE Transactions on Image Processing.

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

[5]  Carlos Vázquez,et al.  Image segmentation as regularized clustering: a fully global curve evolution method , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[6]  Daniel Cremers,et al.  A probabilistic level set formulation for interactive organ segmentation , 2007, SPIE Medical Imaging.

[7]  Patricio A. Vela,et al.  Interactive Medical Image Segmentation using PDE Control of Active Contours , 2013, IEEE Transactions on Medical Imaging.

[8]  Yogesh Rathi,et al.  Image Segmentation Using Active Contours Driven by the Bhattacharyya Gradient Flow , 2007, IEEE Transactions on Image Processing.

[9]  Carlos Vázquez,et al.  Joint multiregion segmentation and parametric estimation of image motion by basis function representation and level set evolution , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Nahum Kiryati,et al.  Interactive level set segmentation for image-guided therapy , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

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

[12]  Laurent D. Cohen,et al.  Global Minimum for Active Contour Models: A Minimal Path Approach , 1997, International Journal of Computer Vision.

[13]  David Zhang,et al.  A survey of graph theoretical approaches to image segmentation , 2013, Pattern Recognit..

[14]  Martha Elizabeth Shenton,et al.  A 3D interactive multi-object segmentation tool using local robust statistics driven active contours , 2012, Medical Image Anal..