Abstract
Environmental Microbiology (EM) is an important scientific field, which investigates the ecological usage of different microorganisms. Traditionally, researchers look for the information of microorganisms by checking references or consulting experts. However, these methods are time-consuming and not effective. To increase the effectiveness of EM information search, we propose a novel approach to aid the information searching work using Content-based Image Retrieval (CBIR). First, we use an microorganism image as input data. Second, image segmentation technique is applied to obtain the shape of the microorganism. Third, we extract shape feature from the segmented shape to represent the microorganism. Especially, we use a contour-based shape feature called Internal Structure Histogram (ISH) to describe the shape, which can use angles defined on the shape contour to build up a histogram and represent the structure of the microorganism. Finally, we use Euclidean distances between each ISHs to measure the similarity of different EM images in the retrieval task, and use Average Precision (AP) to evaluate the retrieval result. The experimental result shows the effectiveness and potential of our EM-CBIR system.
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Akakin, H., Gurcar, M.: Content-based microscopic image retrieval system for multi-image queries. Inform. Technol. Biomed. 16(4), 758–769 (2012)
Belongie, S., Malik, J., Puzicha, J.: Matching shapes. In: International Conference on Computer Vision, pp. 454–461 (2001)
Brill, E.: Character Recognition via Fourier Descriptors. Qualitative Pattern Recognition Through Image Shaping, Los Angeles (1968)
Caicedo, J., Gonzalez, F., Romero, E.: Content-based histopathology image retrieval using a kernel-based semantic annotation framework. Biomed. Inform. 156(44), 519–528 (2011)
Canny, J.: A computational approach to edge detection. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Pearson International Edition, New Jersey (2008)
Kazakova, N., Margala, M., Durdle, N.G.: Sobel dege detection processor for a real-time volume rendering system. In: Circuits and Systems. pp. 23–26 (2004)
Kishida, K.: Property of Average Precision And Its Generalization: An Examination of Evaluation Indicator for Information Retrieval Experiments (2005)
Li, C., Shirahama, K., Grzegorzek, M.: Application of content-based image analysis to environmental microorganism classification. Biocybern. Biomed. Eng. 35(2015), 10–21 (2015)
Maini, R., Sohal, J.S.: Performance evaluation of prewitt edge detector for noisy images. Graph. Vis. Image Process. 6(3), 39–46 (2006)
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 73(1), 1–23 (2004)
Neycenssac, F.: Contrast enhancement using the Laplacian-of-a-Gaussian filter. Graph. Models Image Process. 55(6), 447–463 (1993)
Otsu, N.: A threshold selection method from gray-level histograms. Syst. Man Cybern. 9(1), 62–66 (1979)
Pepper, I., Gerba, C., Gentry, T.: Environmental Microbiology, 3rd edn. Academic Press, San Diego (2014)
Roerdink, J.B.T.M., Meijster, A.: The watershed transform: definitions, algorithms and parallelization strategies. Fundam. Inform. 41(1–2), 187–228 (2000)
Rui, Y., She, A., Huang, T.: A Modified Fourier Descriptor for Shape Matching in MARS. Image Databases and Multimedia Search. World Scienti1c Publishing Co, Singapore (1997)
Sheikh, A., H, L., Mansor, S., Fauzi, M., Anuar, F.: A content based image retrieval system for marine life images. In: International Symposium on Consumer Electronics, pp. 29–33 (2011)
Shyu, C., Brodley, C., Kak, A., Kosaka, A., Aisen, A., Broderick, L.: Assert: a physician-in-the-loop content-based retrieval system for HRCT image databases. Comput. Vis. Image Understand 75(1–2), 111–132 (1999)
Simpson, M., Rahman, M., Phadnis, S., Apostolova, E., Demner-Fushman, D., Antani, S., Thoma, G.: Text- and content-based approaches to image modality classiffcation and retrieval for the ImageCLEF 2011 medical retrieval track. In: Information Technology in Biomedicine, pp. 758–769 (2012)
Zhang, D., Lu, G.: A comparative study of Fourier descriptors for shape representation and retrieval. In: Asian Conference on Computer Vision, pp. 646–651 (2002)
Zheng, L., Wetzel, A., Gilbertson, J., Becich, M.: Design and analysis of a content-based pathology image retrieval system. In: Information Technology in Biomedicine, pp. 249–255 (2003)
Acknowledgments
We thank Program of Study Abroad for Young Scholar (supported by Chengdu University of Information Technology (CUIT)), China Scholarship Council, Project (No. Y2013106) Supported by the Teaching Research Foundation of CUIT, Project (No. KYTZ201410) Supported by the Scientific Research Foundation of CUIT, and Project (No. 2015GZ0197, 2015GZ0304) Supported by Scientific Research Fund of Sichuan Provincial Science & Technology Department to support this research work. We also thank Prof. Dr. Beihai Zhou and M.Sc. Fangshu Ma from University of Science and Technology Beijing (USTB) for their great help. Also, we thank Cathrin Warnke from the University of Siegen for her proof reading.
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Zou, Y.L., Li, C., Boukhers, Z., Shirahama, K., Jiang, T., Grzegorzek, M. (2016). Environmental Microbiological Content-Based Image Retrieval System Using Internal Structure Histogram. In: Burduk, R., Jackowski, K., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds) Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. Advances in Intelligent Systems and Computing, vol 403. Springer, Cham. https://doi.org/10.1007/978-3-319-26227-7_51
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DOI: https://doi.org/10.1007/978-3-319-26227-7_51
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