ICIAP 2015: Image Analysis and Processing — ICIAP 2015 pp 635-646 | Cite as
Automatic Image Analysis and Classification for Urinary Bacteria Infection Screening
Abstract
In this paper, we present an automatic system for the screening of urinary tract infections. It is estimated that about 150 million infections of this kind occur world wide yearly, giving rise to roughly five billion health–care expenditures. Currently, Petri plates seeded with infected samples are analyzed by human experts, an error prone and lengthy process. Nevertheless, based on image processing techniques and machine learning tools, the recognition of the bacterium type and the colony count can be automatically carried out. The proposed system captures a digital image of the plate and, after a preprocessing stage to isolate the colonies from the culture ground, accurately identifies the infection type and severity. Moreover, it contributes to the standardization of the analysis process, also avoiding the continuous transition between sterile and external environments, which is typical in the classical laboratory procedure.
Keywords
Advanced image processing Support vector machines Urinoculture screeningPreview
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References
- 1.Ogawa, H., Nasu, S., Takeshige, M., Funabashi, H., Saito, M., Matsuoka, H.: Noise-free accurate count of microbial colonies by time-lapse shadow image analysis. Journal of Microbiological Methods 91(3), 420–428 (2012)CrossRefGoogle Scholar
- 2.Clarke, M.L., Burton, R.L., Hill, A.N., Litorja, M., Nahm, M.H., Hwang, J.: Low-cost, high-throughput, automated counting of bacterial colonies. Cytometry Part A 77(8), 790–797 (2010)CrossRefGoogle Scholar
- 3.Brugger, S.D., Baumberger, C., Jost, M., Jenni, W., Brugger, U., Mühlemann, K.: Automated counting of bacterial colony forming units on agar plates. PLoS ONE 7(3), e33695 (2012)CrossRefGoogle Scholar
- 4.Ferrari, A., Signoroni, A.: Multistage classification for bacterial colonies recognition on solid agar images. In: Proceeding of IEEE IST 2014, pp. 101–106 (2014)Google Scholar
- 5.Ilsever, M., Unsalan, C.: Two-Dimensional Change Detection Methods: Remote Sensing Applications, pp. 7–21. SpringerBriefs in Computer, Science (2012)Google Scholar
- 6.Maragos, P., Pessoa, L.: Morphological filtering for image enhancement and detection. In: Handbook of Image and Video Processing. Academic Press (2005)Google Scholar
- 7.Mount, D., Netanyahu, N.: Efficient Randomized Algorithms for Robust Estimation of Circular Arcs and Aligned Ellipses. Computational Geometry 19(1), 1–33 (2001)CrossRefMathSciNetMATHGoogle Scholar
- 8.Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On Clustering Validation Techniques. Journal of Intelligent Information Systems 17, 107–145 (2001)CrossRefMATHGoogle Scholar
- 9.Hunter, R.S.: Photoelectric color difference meter. In: Proceedings of the Winter Meeting of the Optical Society of America, vol. 38(7), p. 661. JOSA (1948)Google Scholar
- 10.Saxe, D., Foulds, R.: Toward robust skin identification in video images. In: 2nd Int. Face and Gesture Recognition Conf. (1996)Google Scholar
- 11.Smolka, B., Szczepanski, M., Plataniotis, K.N., Venetsanopoulos, A.N.: Fast modified vector median filter. In: Skarbek, W. (ed.) CAIP 2001. LNCS, vol. 2124, p. 570. Springer, Heidelberg (2001) CrossRefGoogle Scholar
- 12.Heidenreich, N., Schindler, A., Sperlich, S.: Bandwidth selection for kernel density estimation: a review of fully automatic selectors. Adv. Stat. Anal. 97, 403–433 (2013)CrossRefMathSciNetGoogle Scholar
- 13.Comaniciu, D., Meer, P.: Mean-shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Machine Intell. 24, 603–619 (2002)CrossRefGoogle Scholar
- 14.Gonzalez, R., Woods, R.: Digital Image Processing, pp. 414–428. Addison Wesley (1992)Google Scholar
- 15.Smith, A.R.: Color gamut transform pairs. In: SIGGRAPH 1978 - Proceedings of the 5th Annual Conference on Computer Graphics and Interactive Techniques, vol. 12(3), pp. 12–19 (1978)Google Scholar
- 16.Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. on System, Man and Cybernetics 9, 62–66 (1979)CrossRefGoogle Scholar
- 17.Rother, C., Kolmogorov, V., Blake, A.: GrabCut: Interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23, 309–314 (2004)CrossRefGoogle Scholar