A methodology for the validation of image segmentation methods

A validation methodology for image segmentation methods is explored that is based on quality constrained cost analysis. In this methodology a segmentation method is evaluated by the cost reduction it provides relative to the cost of a full-interactive (manual) segmentation. This cost is constrained by a quality threshold, so that less-than-perfect segmentations are allowed. In this way, segmentation methods which are entirely different in nature can be compared objectively. The validation methodology is presented in its most general form, along with an example of its application to the comparison of segmentation methods.<<ETX>>

[1]  Victoria Interrante,et al.  Multiscale, Geometric Image Descriptions for Interactive Object Definition , 1989, DAGM-Symposium.

[2]  Stephen M. Pizer,et al.  A Multiresolution Hierarchical Approach to Image Segmentation Based on Intensity Extrema , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  C. Chen,et al.  Medical Image Segmentation By A Constraint Satisfaction Neural Network , 1990, 1990 IEEE Nuclear Science Symposium Conference Record.

[4]  Theodosios Pavlidis,et al.  Picture Segmentation by a Tree Traversal Algorithm , 1976, JACM.

[5]  Stephen M. Pizer,et al.  Toward Interactive Object Definition in 3D Scalar Images , 1990 .

[6]  Marc Levoy,et al.  A hybrid ray tracer for rendering polygon and volume data , 1990, IEEE Computer Graphics and Applications.

[7]  S P Raya,et al.  Low-level segmentation of 3-D magnetic resonance brain images-a rule-based system. , 1990, IEEE transactions on medical imaging.

[8]  Guido Gerig,et al.  Medical Imaging and Computer Vision: An Integrated Approach for Diagnosis and Planning , 1989, DAGM-Symposium.

[9]  Gunilla Borgefors,et al.  Distance transformations in digital images , 1986, Comput. Vis. Graph. Image Process..

[10]  Azriel Rosenfeld,et al.  Image Smoothing and Segmentation by Multiresolution Pixel Linking: Further Experiments and Extensions , 1982, IEEE Transactions on Systems, Man, and Cybernetics.

[11]  Glynn P. Robinson,et al.  Scale and segmentation of grey-level images using maximum gradient paths , 1991, Image Vis. Comput..

[12]  Wesley E. Snyder,et al.  Segmentation of magnetic resonance images using mean field annealing , 1992, Image Vis. Comput..

[13]  Alexander Toet,et al.  PYRAMID SEGMENTATION OF MEDICAL 3D IMAGES. , 1984 .

[14]  D. Kennedy,et al.  Anatomic segmentation and volumetric calculations in nuclear magnetic resonance imaging. , 1989, IEEE transactions on medical imaging.