Detecting and measuring fine roots in minirhizotron images using matched filtering and local entropy thresholding

Machine Vision and Applications

Volumn 17, Issue 4, 2006, Pages 265-278

  Zeng, Guang ^a   Birchfield, Stanley T ^a   Wells, Christina E ^b  

^a AMRL   (United States)

^b CLEMSON UNIVERSITY   (United States)

Author keywords

AdaBoost; Freeman algorithm; Matched filtering; Minirhizotron images; Root detection; Thresholding

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; COMPUTATIONAL METHODS; COMPUTER VISION; ENTROPY;

ADABOOST; FREEMAN ALGORITHM; MATCHED FILTERING; MINIRHIZOTRON IMAGES; ROOT DETECTION; THRESHOLDING;

IMAGE PROCESSING;

EID: 33747180796     PISSN: 09328092     EISSN: 14321769     Source Type: Journal    
DOI: 10.1007/s00138-006-0024-4     Document Type: Article

References (36)

1. Birchfield, S., Wells, CE.: Rootfly: Software for Minirhizotron Image Analysis, http://www.ces.clemson.edu/~stb/rootfly/
2. Andren, O., Elmquist, H., Hansson, A. C.: Recording, processing and analysis of grass root images from a rhizotron. Plant and Soil 185(2), 259-264 (1996)
3. Baldwin, J. P., Tinker, P. B., Marriott, F. H. C.: The measurement and distribution of onion roots in the field and the laboratory. J. Appl. Ecol. 8, 543-554 (1971)
4. Barsia, A., Heipkeb, C.: Artificial neural networks for the detection of road junctions in aerial images. In: Proceedings of the international society for photogrammetry and remote sensing (ISPRS) Workshop on Photogrammetric Image Analysis, vol. XXXIV, Sept. 2003.
5. Barzohar, M., Cooper, D. B.: Automatic finding of main roads in aerial images by using geometric-stochastic models and estimation. IEEE Trans. Pattern Anal. Mach. Intell. 18(7), 707-721 (1996)
6. Boehm, W.: Methods of Studying Root Systems. Springer Berlin Heidelberg New York (1979)
7. Caldwell, M. M., Virginia, R. A.: Root systems. In: Pearcy, R., Ehleringer, J., Mooney, H., Rundel, P. (eds.) Plant Physiological Ecology. Chapman and Hall, New York (1989)
8. Chanwimaluang, T., Fan, G.: An efficient blood vessel detection algorithm for retinal images using local entropy thresholding. In: Proceedings of the IEEE International Symposium on Circuits and Systems, vol. 5, pp. 21-24 (2003)
9. Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M., Goldbaum, M.: Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans. Med. Imag. 8(3), 263-269 (1989)
10. Chen, D., Li, B., Liang, Z., Wan, M., Kaufman, A., Wax, M.: A tree-branch searching, multiresolution approach to skeletonization for virtual endoscopy. In: Proceedings of the International Society for Optical Engineering, vol. 3979, pp. 726-734 (2000)
11. Cormen, T. H., Leiserson, C. E., Rivest, R. L.: Introduction to Algorithms. McGraw-Hill, New York (1990)
12. Cover, T. M., Thomas, J. A.: Elements of Information Theory. Wiley, New York (1991)
13. Dorst, L., Smeulders, A. W. M.: Length estimators for digitized contours. Comput. Vis. Graph. Imag. Process. 40, 311-333 (1987)
14. Duda, R., Hart, P.: Pattern Classification and Scene Analysis. Wiley New York (1973)
15. Erz, G., Posch, S.: A region based seed detection for root detection in minirhizotron images. In: Proceeding of 25th DAGM Symposium, vol. 2781, pp. 482-489 (2003)
16. Freeman, H.: Boundary encoding and processing. In: Picture Processing and Psychopictorics, pp. 241-266. Academic, New York (1970)
17. Freund, Y., Schapire, R. E.: A short introduction to boosting. J. Japanese Soc. Artif. Intell. 14(5), 771-780 (1999)
18. Glasbey, C. A., Morgan, G. W.: Image Analysis for the Biological Sciences. Wiley, Chichester (1995)
19. Han, J., Guo, L.: An algorithm for automatic detection of runways in aerial images. Mach. Graph. Visi. Int. J. 10(4), 503-518 (2001)
20. Hendrick, R. L., Pregitzer, K. S.: Spatial variation in tree root distribution and growth associated with minirhizotrons. Plant Soil 143(2), 283-288 (1992)
21. Joslin, J. D., Henderson, G. S.: Organic matter and nutrients associated with fine root turnover in a white oak stand. Forest Sci., 33, 330-346 (1987)
22. Kimura, K., Kikuchi, S., Yamasaki, S.: Accurate root length measurement by image analysis. Plant Soil 216(1), 117-127 (1999)
23. Lebowitz, R. J.: Digital image analysis measurement of root length and diameter. Env. Exp. Bot. 28, 267-273 (1988)
24. Nater, E. A., Nater, K. D., Baker, J. M.: Application of artificial neural system algorithms to image analysis of roots in soil. Geoderma 53(3), 237-253 (1992)
25. Norby, R. J., Jackson, R. B.: Root dynamics and global change: seeking an ecosystem perspective. New Phytol. 147, 3-12 (2000)
26. Otsu, N.: A threshold selection method from gray level histograms. IEEE Trans. Syst. Man, Cybernet. 9(1), 62-66 (1979)
27. Pal, N. R., Pal, S. K.: Entropie thresholding. Signal Process. 16, 97-108 (1989)
28. Petrou, M.: Optimal convolution filters and an algorithm for the detection of wide linear features. IEE Proceedings I, Vis. Signal Image Process 140(5), 331-339 (1993)
29. Swets, J.: Measuring the accuracy of diagnostic systems. Science 240, 1285-1293 (1988)
30. Taiz, L., Zeiger, E., Plant Physiology, 3rd ed. Sinnauer, Sunderland (2002)
31. Upchurch, D. R., Ritchie, J. T.: Root observations using a video recording system in mini-rhizotrons. Agron. J. 75(6), 1009-1015 (1983)
32. Vamerali, T., Ganis, A., Bona, S., Mosca, G.: An approach to minirhizotron root image analysis. Plant Soil 217(1), 183-193 (1999)
33. Vogt, K. A., Grier, C. C., Vogt, D. J.: Production, turnover and nutritional dynamics of above- and below ground detritus of world forests. Adv. Ecol. Res. 15, 303-307 (1986)
34. Voorhees, W. B., Carlson, V. A., Hallauert, E. A.: Root length measurement with a computer-controlled digital scanning micro-densitometer. Agron. J. 72, 847-851 (1980)
35. Wells, C. E., Eissenstat, D. M.: Marked differences in survivorship among apple roots of different diameters. Ecology 82, 882-892 (2001)
36. Zeng, G., Wells, C. E., Birchfield, S. T.: Automatic discrimination of fine roots in minirhizotron images. Plant Soil 2006 (in review)