Abstract
Fuzzy mathematical morphology is an extension of binary morphology to gray-scale images using techniques from fuzzy logic. Fuzzy mathematical morphology can be applied to process image data having characteristics of vagueness and imprecision. In this chapter, the main concepts from fuzzy mathematical morphology are briefly introduced and the results of applying fuzzy morphological operators to construct morphological gradient are reported in low-contrast biological images.
As our circle of knowledge expands, so does the circumference of darkness surrounding it.
Albert Einstein
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
Notes
- 1.
Two pixels are connected in a subset S of an image A if there exists a path between them made up of pixels belonging to S. The largest set of pixels connected to the pixel \(p \in S\) is known as connected component of S.
References
Beucher, S.: Segmentation d’images et morphologie mathmatique. Doctoral dissertation, Ecole Nationale Suprieure des Mines de Paris (1990)
Bloch, I., Maytre, H.: Fuzzy mathematical morphologies: a comparative study. Pattern Recognit. 28, 1341–1387 (1995)
Caponetti, L., Castellano, G., Corsini, V., Sforza, G.: Multiresolution texture analysis for human oocyte cytoplasm description. In: Proceedings of the MeMeA09, pp. 150–155 (2009)
Caponetti, L., Castellano, G., Basile, M.T., Corsini, V.: Fuzzy mathematical morphology for biological image segmentation. Appl. Intell. 40(1), 1–11 (2014)
De Baets, B.: Fuzzy morphology: A logical approach. In: Uncertainty Analysis in Engineering and Science: Fuzzy Logic, Statistics, and Neural Network Approach, pp. 53–68. Kluwer Academic Publishers, Norwel (1997)
De Baets, B., Kerre, E.E., Gupta, M.: The fundamentals of fuzzy mathematical morphologies part i: basics concepts. Int. J. Gen. Syst. 23, 155–171 (1995)
Deng, T., Heijmans, H.: Grey-scale morphology based on fuzzy logic. J. Math. Imaging Vis. 16(2), 155–171 (2002)
di Gesú, V., Maccarone, M.C., Tripiciano, M.: Mathematical morphology based on fuzzy operators. Fuzzy Logic 477–486 (1993)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall Inc, Upper Saddle River, NJ, USA (2006)
Hamamah, S.: Oocyte and embryo quality: is their morphology a good criterion?. Journal de gynecologie, obstetrique et biologie de la reproduction. 34(7 Pt 2), 5S38–5S41 (2005)
Huang, L.-K., Wang, M.-J.J.: Image thresholding by minimizing the measure of fuzziness. Pattern Recognit. 28(4), 41–51 (1995)
Landini G.: ImageJ plugin Morphology. Available at: http://sites.imagej.net/Landini/
Serra, J.: Image Analysis and Mathematical Morphology. Academic Press Inc., Cambridge (1983)
Wilding, M., Di Matteo, L., DAndretti, S., Montanaro, N., Capobianco, C., Dale, B.: An oocyte score for use in assisted reproduction. Journal of Ass. Repr. Gen. 24(8), 350–358 (2007)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2017 The Author(s)
About this chapter
Cite this chapter
Caponetti, L., Castellano, G. (2017). Morphological Analysis. In: Fuzzy Logic for Image Processing. SpringerBriefs in Electrical and Computer Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-44130-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-44130-6_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-44128-3
Online ISBN: 978-3-319-44130-6
eBook Packages: EngineeringEngineering (R0)