Abstract
This paper describes a novel image retrieval method for parasite detection based on the analysis of digital images captured by the camera from a microscope. In our approach we use several image processing methods to find known parasite shapes. At first, we use an edge detection method with edge representation by vectors. The next step consists in clustering edges fragments by their normal vectors and positions. Then grouped edges fragments are used to perform elliptical or circular shapes fitting as they resemble most parasite forms. This approach is invariant from rotation of parasites eggs or the analyzed sample. It is also invariant to scale of digital images and it is robust to overlapping shapes of parasites eggs thanks to the ability to reconstructing elliptical or other symmetric shapes that represent the eggs of parasites. With this solution we can also reconstruct incomplete shape of parasite egg which can be visible only in some part of the retrieved image.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Akgül, C.B., Rubin, D.L., Napel, S., Beaulieu, C.F., Greenspan, H., Acar, B.: Content-based image retrieval in radiology: current status and future directions. J. Digit. Imaging 2, 208–222 (2011)
Antonie, M.L., Zaiane, O.R., Coman, A.: Application of Data Mining Techniques for Medical Image Classification. In: Proceedings of the Second International Workshop on Multimedia Data Mining (2001)
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)
Chuctaya, H., Portugal, C., Beltran, C., Gutierrez, J., Lopez, C., Tupac, Y.: M-CBIR: A medical content-based image retrieval system using metric data-structures. JCC (2011)
Gardner, S.L.: The parasite collection search page in the Manter Laboratory of Prasitology, http://manter.unl.edu/hwml
Gudivada, V.N., Raghavan, V.V.: Content based image retrieval systems, vol. 28(9), pp. 18–22. IEEE Computer Society (1995)
Gueld, M.O., Keysers, D., Deselaers, T., Leisten, M., Schubert, H., Ney, H., Lehmann, T.M.: Comparison of global features for categorization of medical images. Medical Imaging (2004)
Hsua, W., Antani, S., Long, L.R., Neve, L., Thoma, G.R.: SPIRS: a Web-based image retrieval system for large biomedical databases. Int. J. Med. Inform. 78, 13–24 (2009)
Mallik, J., Samal, A.K., Gardner, S.L.: A Content Based Pattern Analysis System for a Biological Specimen Collection. In: 7th IEEE International Conference on Data Mining (2007)
Meduri, R., Samal, A., Gardner, S.L.: Worm-Web Search: A Content-Based Image Retrieval (CBIR) System for the Parasite Image Collection in the Harold W. Manter Laboratory of Parasitology, University of Nebraska State Mueum (2008)
Muller, H., Michoux, N., Bandon, D., Geissbuhler, A.: A Review of Content - Based Image Retrieval Systems in Medical Applications - Clinical Benefits and Future Directions. Int. J. Med. Inform. (2009)
Tek, F.B., Dempster, A.G., Kale, I.: Malaria Parasite Detection in Peripheral Blood Images. In: British Machine Vision Conference (2006)
Urschler, M., Mayer, H., Bolter, R., Leberl, F.: The LiveWire Approach for the Segmentation of Let Ventricle Electron-Beam CT Images. OCG 160, 319–326 (2002)
Wanjale, K., Borawake, T., Chaudhari, S.: Content Based Image Retrieval for Medical Images Techniques and Storage Methods-Review Paper. IJCA Journal 1(19) (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Najgebauer, P., Nowak, T., Romanowski, J., Rygał, J., Korytkowski, M., Scherer, R. (2012). Novel Method for Parasite Detection in Microscopic Samples. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7267. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29347-4_64
Download citation
DOI: https://doi.org/10.1007/978-3-642-29347-4_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29346-7
Online ISBN: 978-3-642-29347-4
eBook Packages: Computer ScienceComputer Science (R0)