Interactive segmentation using curve evolution and relevance feedback

We propose in this paper an interactive segmentation algorithm based on curve evolution techniques. The task of automated segmentation has proven to be highly complex and application dependent. User's knowledge can be used to alleviate the problem. In this paper, we propose the use of a recently developed curve evolution technique B. Sumengen et al., (2003), augmented with a relevance feedback phase through user interaction. After the initial automatic segmentation is computed, the user presents his positive/negative feedback via a simple user interface. Segmentation parameters are then adapted locally to reflect user's requirements. Experimental results show the usefulness of the proposed approach in interactive segmentation tasks.

[1]  Lawrence O. Hall,et al.  Automatic tumor segmentation using knowledge-based techniques , 1998, IEEE Transactions on Medical Imaging.

[2]  B. S. Manjunath,et al.  Image segmentation using curve evolution and flow fields , 2002, Proceedings. International Conference on Image Processing.

[3]  William A. Barrett,et al.  Intelligent scissors for image composition , 1995, SIGGRAPH.

[4]  Marcel Worring,et al.  Watersnakes: Energy-Driven Watershed Segmentation , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Marie-Pierre Jolly,et al.  Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[6]  Anthony J. Yezzi,et al.  A statistical approach to snakes for bimodal and trimodal imagery , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[7]  B. S. Manjunath,et al.  EdgeFlow: a technique for boundary detection and image segmentation , 2000, IEEE Trans. Image Process..

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

[9]  Narendra Ahuja,et al.  Selecting Objects With Freehand Sketches , 2001, ICCV.

[10]  B. S. Manjunath,et al.  Image segmentation using multi-region stability and edge strength , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[11]  Guillermo Sapiro,et al.  Knowledge-based segmentation of SAR data with learned priors , 2000, IEEE Trans. Image Process..

[12]  Laurent D. Cohen,et al.  Real-Time Interactive Path Extraction with on-the-Fly Adaptation of the External Forces , 2002, ECCV.

[13]  Sung-Bae Cho,et al.  Geometric Structure Analysis of Document Images: A Knowledge-Based Approach , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Petia Radeva,et al.  Flexible shapes for segmentation and tracking of cardiovascular data , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[15]  Ramakant Nevatia,et al.  Segmentation and tracking of multiple humans in complex situations , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[16]  Marie-Pierre Jolly,et al.  Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images , 2001, ICCV.

[17]  William A. Barrett,et al.  Toboggan-based intelligent scissors with a four-parameter edge model , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[18]  Anke Neumann,et al.  Graphical Gaussian Shape Models and Their Application to Image Segmentation , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Robert M. Haralick,et al.  A knowledge-based boundary delineation system for contrast ventriculograms , 2001, IEEE Transactions on Information Technology in Biomedicine.

[20]  Beatriz Marcotegui,et al.  A toolbox for interactive segmentation based on nested partitions , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).