Characterization of the Algal Colonization of Mortar Surfaces Using Image Analysis
DOI:
https://doi.org/10.5566/ias.3165Keywords:
Avrami’s model, component-trees, image analysis, image segmentation, random forestAbstract
This research work aims to characterize the algal colonization of mortar surfaces using image analysis. The resistance of mortars to the biofouling is studied by means of an accelerated lab-scale test. A suspension of green algae Klebsormidium flaccidum, was performed to periodically sprinkle the mortar surfaces and digital images were acquired thanks to a scanner. The colonization rate of the surface follows a sigmoid type curve as a function of time that can be modeled thanks to Avrami’s model. It is described by a two-process mechanism: attachment and growth of algal stains. To identify the parameters of the algal colonization model, the images were segmented thanks to a random forest algorithm to obtain pixel information on the presence of algae. These processes were then represented with a morphological min-tree and information such as the number of new germs or their growth have been exploited to feed the Avrami’s model. The results show a good agreement between the modeling and the experimental data.
References
Berger C, G´eraud T, Levillain R, Widynski N, Baillard A, Bertin E (2007). Effective component tree computation with application to pattern recognition in astronomical imaging. In: IEEE C Image Proc.
Bosilj P, Kijak E, Lefevre S (2018). Partition and inclusion hierarchies of images: A comprehensive survey. J Imaging 4:1–33.
Gonzalez-Diaz R, Jimenez M, Medrano B (2018). Topological tracking of connected components in image sequences. J Comput Syst Sci 95:134–42.
Gutman Z, Vij R, Najman L, Lindenbaum M (2024). Assessing hierarchies by their consistent segmentations. J Math Imaging Vis 66:314–34.
Geraud T, Carlinet E, Crozet S, Najman L (2013). A quasi-linear algorithm to compute the tree of shapes of nd images. In: Int Symp Math Morph.
Kirillov L, Mintun E, Ravi N, Mao H, Rolland C, Gustafson L, Xiao T, Whitehead S, Berg A, Lo WY, Dollar P, Girschick RB (2023). Segment anything. IEEEI Conf Comp Vis :4015–26.
Minaee S (2021). Image segmentation using deep learning: A survey. J Comput Syst Sci 44:3523– 42.
Ojala T, Pietik¨ainen M, M¨aenp¨a¨a T (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24:971–87.
Oliveira LM, Ye Z, Katz A, Alimova A, Wei H, Herman GT, Gottlieb P (2018). Component tree analysis of cystovirus phi6 nucleocapsid cryoem single particle reconstructions. Plos one 13:e0188858.
Ortega-Calvo JJ, Ari˜no X, Hernandez-Marine M, Saiz- Jimenez C (1995). Factors affecting the weathering and colonization of monuments by phototrophic microorganisms. Sci Total Environ 167:329–41.
Perret B, Lef`evre S, Collet C, Slezak E (2010). Connected component trees for multivariate image processing and applications in astronomy. In: Int C Patt Recog.
Posada-Gomez R, Sandoval-Gonz´alez OO, Sibaja AM, Portillo-Rodr´ıguez O, Alor-Hern´andez G (2011). Practical Applications and Solutions Using LabVIEW Software, chap. Digital image processing using LabVIEW. InTech, 297–316.
Salembier P, Oliveras A, Garrido L (1998). Antiextensive connected operators for image and sequence processing. IEEE Image Proc 7:555–70.
Soille P (2003). Morphological image analysis : principles and applications, 2nd edition. Springer Berlin.
Tran TH, A. G, Guyonnet R, Grosseau P, Lors C, Garcia-Diaz E, Ruot B (2012). Influence of the intrinsic characteristics of mortars on biofouling by klebsormidium flaccidum. Int Biodeter Biodegr 70:31–9.
Tran TH, Govin A, Guyonnet R, Grosseau P, Lors C, Damidot D, Ruot B (2013). Avrami’s law based kinetic modeling of colonization of mortar surface by alga klebsormidium flaccidum. Int Biodeter Biodegr 1:73–80.
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Copyright (c) 2024 Juliette Hortemel, Sara Zaghloul, Alexandre Govin, Johan Debayle
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