Abstract
The problem of light microscope images enhancement by filtering for recognition pathologic liver tissues in fish is considered in the paper. The problem follows from the necessity of monitoring the sea water pollutions caused by mercury compounds and their influence on living organisms. It is proposed to use image filtering based on morphological spectra to enhance visibility of liver lesions in the images in order to extract morphologic-statistical parameters useful in automatic tissues classification into normal and pathologic classes. It is shown that selected components of the 4th range morphologic spectra (MS4) are the most suitable to discriminate normal and pathologic liver tissues. The selected spectral components are characterized by their estimated mean values, standard deviations and kurtoses. The so-obtained morphologic-statistical parameters have been used to construct the learning sets for two types of image classifiers: based on the nearest mean and k nearest neighbors rules. It is shown that preliminary image filtering by morphological spectra-based filters improves spatial distribution of the recognized normal and pathologic objects in the parameter space.
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
References
Barst, B.D.: Hepatotoxicity of mercury to fish. B.S. Thesis, University of North Texas, August 2010
Barst, B.D., Gevertz, A.K., Chumchal, M.M., et al.: Laser ablation ICP-MSCo-Localization of mercury and immune response in fish. Environ. Sci. Technol. 45, 8982–8988 (2011)
Przytulska, M., Kulikowski, J.L., Wierzbicka, D.: Biomedical images enhancement based on the properties of morphological spectra. Biocybern. Biomed. Eng. 35, 206–215 (2014)
Maitre, H.: Image Processing. Wiley, New York (2008)
González, R., Woods, R.: Digital Image Processing. Pearson/Prentice Hall, Upper Saddle River (2008)
Kulikowski, J., Przytulska, M., Wierzbicka, D.: Description of biomedical textures by statistical properties of their morphological spectra. Biocybern. Biomed. Eng. 30(3), 19–34 (2009)
Fix, E., Hodges, J.L.: Discriminatory analysis: nonparametric discrimination small sample performance, project 21–49-004, report number 11, USAF School of Aviation Medicine, Randolph Field, Texas, pp. 280–322 (1952)
Dasarathy, B.V.: NN Pattern Classification Techniques, pp. 40–56. IEEE Computer Society Press, Washington (1991)
Devijver, P.A., Kittler, J.: Pattern Recognition: A Statistical Approach. Prentice Hall, London (1982)
Acknowledgments
We would like to express our gratitude to Benjamin Daniel Barst, for providing images of liver tissues in fish for our experiments and to Diana Wierzbicka for her help in image filtering.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Przytulska, M., Kulikowski, J., Jóźwik, A. (2016). Morphologic-Statistical Approach to Detection of Lesions in Liver Tissue in Fish. 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_43
Download citation
DOI: https://doi.org/10.1007/978-3-319-26227-7_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26225-3
Online ISBN: 978-3-319-26227-7
eBook Packages: EngineeringEngineering (R0)