This Article 
 Bibliographic References 
 Add to: 
Adaptive Local Thresholding by Verification-Based Multithreshold Probing with Application to Vessel Detection in Retinal Images
January 2003 (vol. 25 no. 1)
pp. 131-137

Abstract—In this paper, we propose a general framework of adaptive local thresholding based on a verification-based multithreshold probing scheme. Object hypotheses are generated by binarization using hypothetic thresholds and accepted/rejected by a verification procedure. The application-dependent verification procedure can be designed to fully utilize all relevant informations about the objects of interest. In this sense, our approach is regarded as knowledge-guided adaptive thresholding, in contrast to most algorithms known from the literature. We apply our general framework to detect vessels in retinal images. An experimental evaluation demonstrates superior performance over global thresholding and a vessel detection method recently reported in the literature. Due to its simplicity and general nature, our novel approach is expected to be applicable to a variety of other applications.

[1] J. Bernsen, “Dynamic Thresholding of Grey-Level Images,” Proc. Int'l Conf. Pattern Recognition, pp. 1251-1255, 1986.
[2] C.K. Chow and T. Kaneko, “Automatic Boundary Detection of the Left Ventricle from Cineangiograms,” Computing Biomedical Res., vol. 5, pp. 388-410, 1972.
[3] A. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating Blood Vessels in Retinal Images by Piece-Wise Threshold Probing of a Matched Filter Response,” IEEE Trans. Medical Imaging, vol. 19, no. 3, pp. 203-210, 2000.
[4] X. Jiang, “An Adaptive Contour Closure Algorithm and Its Experimental Evaluation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 11, pp. 1252-1265, Nov. 2000.
[5] F. Leymarie and M. D. Levine, “Fast Raster Scan Distance Propagation on the Discrete Rectangular Lattice,” CVGIP: Image Understanding, vol. 55, no. 1, pp. 84-94, 1992.
[6] S.-P. Liou, A.H. Chiu, and R.C. Jain, “A Parallel Technique for Signal-Level Perceptual Organization,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, no. 4, pp. 317-325, Apr. 1991.
[7] G. Malandain and S.F. Vidal, “Euclidean Skeletons,” Image and Vision Computing, vol. 16, pp. 317-327, 1998.
[8] Y. Nakagawa and A. Rosenfeld, “Some Experiments on Variable Thresholding,” Pattern Recognition, vol. 11, pp. 191-204, 1979.
[9] L. O'Gorman, “Binarization and Multithresholding of Document Images Using Connectivity,” CVGIP: Graphical Models and Image Processing, vol. 56, no. 6, pp. 494-506, 1994.
[10] A. Pikaz and A. Averbuch, “Digital Image Thresholding Based on Topological Stable-State,” Pattern Recognition, vol. 29, no. 5, pp. 829-843, 1996.
[11] B. Sankur and M. Sezgin, “Image Thresholding Techniques: A Survey Over Categories,” Pattern Recognition, under review.
[12] S.D. Yanowitz and A.M. Bruckstein, “A New Method for Image Segmentation,” Computer Vision, Graphics, and Image Processing, vol. 46, pp. 82-95, 1989.

Index Terms:
Adaptive local thresholding, threshold probing, hypotheses generation and verification, vessel segmentation, retinal imaging, medical imaging.
Xiaoyi Jiang, Daniel Mojon, "Adaptive Local Thresholding by Verification-Based Multithreshold Probing with Application to Vessel Detection in Retinal Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 1, pp. 131-137, Jan. 2003, doi:10.1109/TPAMI.2003.1159954
Usage of this product signifies your acceptance of the Terms of Use.