Merging discrepancy measures for region-based segmentation results evaluation and comparison: Application to thresholded weld defect radiographic images

This paper presents a new method for evaluating and comparing image segmentation results. This method consists of an association of measures that have the purpose to compute, in the sense of segmentation evaluation, the difference between two regions, one extracted from an ideal segmentation map and the same region obtained with a segmentation algorithm. Those measures take into account many aspects of the miss-segmented pixels as connectivity, compactness, location and their influence on the segmentation result. The new measure which rates the overall segmentation results, is easier to use comparatively to several measures, especially for a machine or an inexperienced user.

[1]  Noel C. F. Codella,et al.  Image Segmentation Techniques , 1984 .

[2]  Aicha-Baya Goumeidane,et al.  Error measures for segmentation results: Evaluation on synthetic images , 2010, 2010 17th IEEE International Conference on Electronics, Circuits and Systems.

[3]  Hui Zhang,et al.  Image segmentation evaluation: A survey of unsupervised methods , 2008, Comput. Vis. Image Underst..

[4]  Linda G. Shapiro,et al.  Image Segmentation Techniques , 1984, Other Conferences.

[5]  Horst Bunke,et al.  Distance Measures for Image Segmentation Evaluation , 2006, EURASIP J. Adv. Signal Process..

[6]  Hélène Laurent,et al.  Unsupervised Performance Evaluation of Image Segmentation , 2006, EURASIP J. Adv. Signal Process..

[7]  Jan J. Gerbrands,et al.  Objective and quantitative segmentation evaluation and comparison , 1994, Signal Process..

[8]  P. Gong,et al.  Accuracy Assessment Measures for Object-based Image Segmentation Goodness , 2010 .

[9]  Shamik Sural,et al.  Evaluation of Segmentation Techniques Using Region Size and Boundary Information , 2009, PReMI.

[10]  Jaime S. Cardoso,et al.  Toward a generic evaluation of image segmentation , 2005, IEEE Transactions on Image Processing.

[11]  Y. J. Zhang,et al.  A survey on evaluation methods for image segmentation , 1996, Pattern Recognit..

[12]  Hélène Laurent,et al.  A comparative study of supervised evaluation criteria for image segmentation , 2004, 2004 12th European Signal Processing Conference.

[14]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[15]  Hugues Benoit-Cattin,et al.  New discrepancy measures for segmentation evaluation , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[16]  Martial Hebert,et al.  Toward Objective Evaluation of Image Segmentation Algorithms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.