Skip to main content
SpringerLink
Log in

Comparison between different image acquisition methods for grain-size analysis and quantification of ceramic inclusions by digital image processing: how much similar are the results?

  • Original Paper
  • Published:
Archaeological and Anthropological Sciences Aims and scope Submit manuscript

Abstract

This paper focuses on the statistical comparison between the abundance and grain-size distribution of inclusions in pottery, determined by digital image process on images acquired using different methods (crossed-polarized light optical microscopy, back-scattered scanning electron microscopy (SEM) and microchemical mapping also under SEM). The results clearly indicate that the acquisition method deeply affects the absolute quantification, resulting in highly underestimated values for crossed-polarized light images with respect to those obtained from the microchemical mapping. Besides absolute quantification, the grain-size distribution shows some small differences among acquisition methods both in mean and variance values although frequency distributions and cumulative curves of inclusions show some similarities regardless of the acquisition method. Differences in terms of abundance and grain-size distribution of inclusions on the same sample are here analyzed and related to the limitations of each acquisition method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Abedini M, Ziaii M, Negahdarzadeh Y, Ghiasi-Freez J (2018) Porosity classification from thin sections using image analysis and neural networks including shallow and deep learning in Jahrum formation. J Min Environ 9:513–525

    Google Scholar 

  • Andrä H, Combaret N, Dvorkin J, Glatt E, Han J, Kabel M, Keehm Y, Krzikalla F, Lee M, Madonna C, Marsh M, Mukerji T, Saenger EH, Sain R, Saxena N, Ricker S, Wiegmann A, Zhan X (2013) Digital rock physics benchmarks - Part I: Imaging and segmentation. Comput Geosci 50:25–32

    Google Scholar 

  • Aprile A, Castellano G, Eramo G (2014) Combining image analysis and modular neural networks for classification of mineral inclusions and pores in archaeological potsherds. J Archaeol Sci 50:262–272

    Google Scholar 

  • Aprile A, Castellano G, Eramo G (2019) Classification of mineral inclusions in ancient ceramics: comparing different modal analysis strategies. Archaeological and Anthropological Sciences, vol 11, pp 2557–2567

    Google Scholar 

  • Arboretti Giancristofaro R, Bolzan M, Bonnini S, Corain L, Solmi F (2012) Advantages of closed testing method for multiple comparison procedures. Commun Stat Simul Compt 41:746–763

    Google Scholar 

  • Arboretti Giancristofaro R, Bonnini S, Corain L, Salmaso L (2014) A Permutation approach for ranking of multivariate populations. J Multivar Anal 132:39–57

    Google Scholar 

  • Baklouti S, Maritan L, Casas Duocastella L, Laridhi Ouazaa N, Jerrega R, Prevosti M, Mazzoli C, Fouzaï B, Larabi Kassaa S, Fantar M (2016) Establishing a new reference group of Keay 25.2 amphorae from Sidi Zahruni (Nabeul, Tunisia). Appl Clay Sci 132-133:140–154

    Google Scholar 

  • Baragona AJ, Anghelone M, Weber J (2019) A map is worth a thousand pictures: using FTIR-imaging to analyze petrographic thin sections of historical and experimental mortar. In: Álvarez JI, Durán A, Fernández JM, Sirera R, Navarro I (eds) 5th Historic Mortars Conference RILEM Proceedings Pro 130. RILEM Publications, Paris, pp 482–494

    Google Scholar 

  • Bonnini S, Corain L, Marozzi M, Salmaso L (2014) Nonparametric hypothesis testing: rank and permutation methods with applications in R. Wiley, Chichester

    Google Scholar 

  • Bortolussi C, Zoleo A, Maritan L, Collauto A, Marrale M, Parlato A, Usai D (2016) Electron paramagnetic resonance and petrographic analysis for dating Mesolithic and Neolithic pottery from Al Khiday (Sudan). Radiat Meas 89:89–98

    Google Scholar 

  • Bouchain I, Velde B (2001) Grain distribution by image analysis of thin sections in some Gaulo-Roman common ware, St. Machel (Indre) France. In: Druc IC (ed) Archaeology and clay. British Archaeological Reports, Oxford, pp 71–80

    Google Scholar 

  • Buono A, Fullmer S, Luck K, Peterson K, King H, More PJ, LeBlanc S (2019) Quantitative digital petrography: full thin section quantification of pore space and grains. SPE-194899-MS.

  • Carò F, Di Giulio A (2004) Reliability of textural analysis of ancient plasters and mortars through automated image analysis. Mater Charact 53:243–257

    Google Scholar 

  • Casadio F, Chiari G, Simon S (2005) Evaluation of binder/aggregate ratios in archeological lime mortars with carbonate aggregates: a comparative assessment of chemical, mechanical and microscopic approaches. Archaeometry 47:671–689

    Google Scholar 

  • Castro N, Wigum BJ (2012) Assessment of the potential alkali-reactivity of aggregates for concrete by image analysis petrography. Cem Concr Res 42:1635–1644

    Google Scholar 

  • Coletti C, Mazzoli C, Maritan L, Cultrone G (2016) Combined multi-analytical approach for study of pore system in bricks: how much porosity is there? Mater Charact 121:82–92

    Google Scholar 

  • Corain L, Salmaso L (2015) Improving power of multivariate combination-based permutation tests. Stat Comput 25:203–214

    Google Scholar 

  • Corain L, Melas VB, Salmaso L, Pepelyshev A (2014) New insights on permutation approach for hypothesis testing on functional data. ADAC 8:339–356

    Google Scholar 

  • Corain L, Arboretti R, Bonnini S (2016) Ranking of multivariate populations - a permutation approach with applications. Boca Raton, Chapman & Hall/CRC

    Google Scholar 

  • Cozzi B, De Giorgio A, Peruffo A, Montelli S, Panin M, Bombardi C, Grandis A, Pirone A, Zambenedetti P, Corain L, Granato A (2017) The laminar organization of the motor cortex in monodactylous mammals: a comparative assessment based on horse, chimpanzee and macaque. Brain Struct Funct 222:2743–2757

    Google Scholar 

  • Dal Sasso G, Maritan L, Salvatori S, Mazzoli C, Artioli G (2014) Discriminating pottery production by image analysis: a case study of Mesolithic and Neolithic pottery from Al Khiday (Khartoum, Sudan). J Archaeol Sci 46:125–146

    Google Scholar 

  • Daszkiewicz M, Maritan L (2016) Experimental firing and re-firing. In: AMW Hunt (ed), The Oxford Handbook of Archaeological Ceramic Analysis, Oxford Handbooks in Archaeology.

  • Desbois G, Urai JL, Hemes S, Schröppel B, Schwarz JO, Mac M, Weiel D (2016) Multi-scale analysis of porosity in diagenetically altered reservoir sandstone from the Permian Rotliegend (Germany). J Pet Sci Eng 140:128–148

    Google Scholar 

  • Eramo G, Aprile A, Muntoni IA, Zerboni A (2014) Textural and morphometric analysis applied to Holocene pottery from Takarkori rock shelter (SW Lybia, Central Sahara): a quantitative sedimentological approach. Archaeometry 56:36–57

    Google Scholar 

  • Fueten F (1997) A computer controlled rotating polarizer stage for the petrographic microscopy. Comput Geosci 23:203–208

    Google Scholar 

  • Fueten F (2006) Petrographic image processing of thin section using the rotating polarizer stage. Soc Archaeological Sci Bull 29:8–12

    Google Scholar 

  • Ghiasi-Freez J, Soleimanpour I, Kadkhodaie-Ilkhchi A, Ziaii M, Sedighi M, Hatampour A (2012) Semi-automated porosity identification from thin section images using image analysis and intelligent discriminant classifiers. Comput Geosci 45:36–45

    Google Scholar 

  • Gliozzo E, Cavari F, Daminai D, Memmi I (2012) Pigments and plasters from the Roman settlement of Thamusida (Rabat, Morocco). Archaeometry 54:278–293

    Google Scholar 

  • Goodchild JS, Fueten F (1998) Edge detection in petrographic images using the rotating polarizer stage. Comput Geosci 24:745–751

    Google Scholar 

  • Graïc JM, Corain L, Peruffo A, Swaab DF (2018) The bovine anterior hypothalamus: characterization of the vasopressin-oxytocin containing nucleus and changes in relation to sexual differentiation. J Comp Neurol 526:2898–2917

    Google Scholar 

  • Grifa C, De Bonis A, Langella A, Mercurio M, Soricelli G, Morra V (2013) A Late Roman ceramic production from Pompeii. J Archaeol Sci 40:810–826

    Google Scholar 

  • Grisan E, Graïc JM, Corain L, Peruffo A (2018) Resolving single cells in heavily clustered nissl-stained images for the analysis of brain cytoarchitecture, 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018, Volume 2018-April, 23 May 2018, 427-430.

  • Grove C, Jerram DA (2011) jPOR: An ImageJ macro to quantify total optical porosity from blue-stained thin sections. Comput Geosci 37:1850–1859

    Google Scholar 

  • Guan KM, Nazarova M, Guo B, Tchelepi H, Kovscek AR, Creux P (2018) Effects of image resolution on sandstone porosity and permeability as obtained from X-Ray microscopy. Transp Porous Media 127:233–245. https://doi.org/10.1007/s11242-018-1189-9

    Article  Google Scholar 

  • Hofmann P, Marschallinger R, Unterwurzacher M, Zobl F (2013) Marble provenance designation with object based image analysis: state-of-art rock fabric characterization from petrographic micrographs. Austrian J Earth Sci 106:40–49

    Google Scholar 

  • Knappett C, Pirrie D, Power MR, Nikolakopoulou I, Hilditch J, Rollinson GK (2011) Mineralogical analysis and provenancing of ancient ceramics using automated SEM-EDS analysis (QEMSCAN®): a pilot study on LB I pottery from Akrotiri, Thera. J Archaeol Sci 38:219–232

    Google Scholar 

  • Launeau P, Cruden AR, Bouchez JL (1994) Mineral recognition in digital images of rock: a new approach using a multichannel classification. Can Mineral 32:919–933

    Google Scholar 

  • Livingood PC, Cordell AS (2009) Point/counter point: the accuracy of digital image analysis and flexibility of digital techniques in the analysis of ceramic thin sections. J Archaeol Sci 36:867–872

    Google Scholar 

  • Lumbreras F, Serrat J (1996) Segmentation of petrographical images of marbles. Comput Geosci 22:547–558

    Google Scholar 

  • Marinoni N, Pavese A, Foi M, Trombino L (2005) Characterization of mortar morphology in thin section by digital image processing. Cem Concr Res 35:1613–1619

    Google Scholar 

  • Mertens G, Elsen J (2006) Use of computer assisted image analysis for the determination of the grain-size distribution of sand in mortars. Cem Concr Res 36:1453–1459

    Google Scholar 

  • Middendorf B, Schade T, Kraus K (2017) Quantitative analysis of historic mortars by digital image analysis of thin sections. Restor Build Monum 23:83–92

    Google Scholar 

  • Miriello D, Crisci GM (2006) Image analysis and flatbed scanner: a visual procedure in order to study the macroporosity of archaeological and historical mortars. J Cult Herit 7:186–192

    Google Scholar 

  • Miriello D, Barca D, Bloise A, Ciarallo A, Crisci GM, De Rose T, Gattuso C, Gazineo F, La Russa MF (2010) Characterisation of archaeological mortars from Pompeii (Campania, Italy) and identification of construction phases by compositional data analysis. J Archaeol Sci 37:2207–2223

    Google Scholar 

  • Montelli S, Suman M, Corain L, Cozzi B, Peruffo A (2017) Sexually diergic trophic effects of estradiol exposure on developing bovine cerebellar granule cells. Neuroendocrinology 104:51–71

    Google Scholar 

  • Peruffo A, Corain L, Bombardi C, Centelleghe C, Grisan E, Graïc JM, Bontempi P, Grandis A, Cozzi B (2019) The motor cortex of the sheep: laminar organization, projections and diffusion tensor imaging of the intracranial pyramidal and extrapyramidal tracts. Brain Struct Funct 224:1933–1946

    Google Scholar 

  • Prêt D, Sammartino S, Beaufort D, Meunier A, Fialin M, Michot LJ (2010) A new method for quantitative petrography based on image processing of chemical element maps, part I: mineral mapping applied to compacted bentonites. Am Mineral 95:1379–1388

    Google Scholar 

  • Quinn PS (2013) Ceramic petrography: the interpretation of archaeological pottery & related artefacts in thin-sections. Archaeopress, Oxford

    Google Scholar 

  • Quinn PS (2018) Scientific preparations of archaeological ceramics status, value and long term future. J Archaeol Sci 91:43–51

    Google Scholar 

  • Reedy CL (2006) Review of digital image analysis of petrographic thin sections in conservation research. J Am Inst Conserv 45:127–146

    Google Scholar 

  • Reedy CL (2020) 3D Documentation and analysis of porosity in deteriorated historic brick. Stud Conserv:1–4. https://doi.org/10.1080/00393630.2020.1752426

  • Reedy C, Anderson J, Reedy T, Liu Y (2014a) Image analysis in quantitative particle studies of archaeological ceramic thin sections. Adv Archaeol Pract 2:252–268

    Google Scholar 

  • Reedy C, Anderson J, Reedy T, Liu Y (2014b) Quantitative porosity studies of archaeological ceramics by petrographic image analysis. Mater Res Soc Symp Proc 1656:337–353

    Google Scholar 

  • Saxenaa N, Mavko G, Hofmann R, Srisutthiyakorn N (2017) Estimating permeability from thin sections without reconstruction: digital rock study of 3D properties from 2D images. Comput Geosci 102:79–99

    Google Scholar 

  • Smodej J, Lemmens L, Reuning L, Hiller T, Klitzsch N, Claes S, Kukla PA (2019) Nano- to millimeter scale morphology of connected and isolated porosity in the Permo-Triassic Khuff Formation of Oman. Geosciences 10:7. https://doi.org/10.3390/geosciences10010007

    Article  Google Scholar 

  • Soeder DL (1990) Applications of fluorescence microscopy to study of pores in tight rocks. AAPG Bull 74:30–40

    Google Scholar 

  • Song Z, Zhou QY (2019) Micro-scale granite permeability estimation based on digital image analysis. J Pet Sci Eng 180:176–185

    Google Scholar 

  • Stutzman P (2004) Scanning electron microscopy imaging of hydraulic cement microstructure. Cem Concr Compos 26:957–966

    Google Scholar 

  • Tarquini S, Favalli M (2010) A microscopic information system (MIS) for petrographic analysis. Comput Geosci 36:665–674

    Google Scholar 

  • Velde B, Druc IC (1999) Archaeological ceramic materials: origin and utilization. Springer, Berlin

    Google Scholar 

  • Wawrzénczyk J, Molendowska A (2019) The use of a special stereoscopic microscope attachment for the Sieve Analysis of Aggregate in Concrete. Appl Sci 9:1853. https://doi.org/10.3390/app9091853

    Article  Google Scholar 

  • Whitbread IK (1989) A proposal for the systematic description of thin sections towards the study of ancient technology. In Y Maniatis (ed), Archaeometry. Proceedings of the 25th International Symposium. Amsterdam. Elsevier, 127–138.

  • Winkler AM, Ridgway GR, Webster MA, Smith SM, Nichols TE (2014) Permutation inference for the general linear model Anderson. NeuroImage 92:381–397

    Google Scholar 

  • Winkler AM, Webster MA, Brooks JC, Tracey I, Smith SM, Nichols TE (2016) Non-parametric combination and related permutation tests for neuroimaging. Hum Brain Mapp 37:1486–1511

    Google Scholar 

  • Yanagihara H, Yuan KH (2005) Three approximate solutions to the multivariate Behrens–Fisher problem. Commun Stat Simul Compt 34:975–988

    Google Scholar 

Download references

Acknowledgements

We would like to thank D. Usai and M. Fantar for providing the archeological pottery from Al Khiday (Sudan) and Sidi Zahruni (Tunisia), respectively. They also would like to thank an anonymous reviewer and the editor, Elisabetta Gliozzo, for their suggestions and productive discussion.

Funding

This research was carried out with in the project “Petrological, geochemical and morphometric analyses as tools for modelling pottery production technology in antiquity” (CPDA103781) and with the financial support of the Spanish Ministerio de Economía y Competitividad (project: CGL2013-42167-P).

Author information

Authors and Affiliations

  1. Department of Geosciences, University of Padova, Via G. Gradenigo 6, 35131, Padova, Italy

    Lara Maritan, Gregorio Dal Sasso & Claudio Mazzoli

  2. Piovesan Consulting, Via Venozzi 28/E, 31040, Volpago del Montello, Italy

    Rebecca Piovesan

  3. Département de Géologie, Université Tunis El Manar, C.P. 2092, Manar, Tunis, Tunisia

    Siwar Baklouti

  4. Departament de Geologia, Universitat Autònoma de Barcelona, Bellaterra, Catalonia, Spain

    Lluís Casas

  5. Department of Management and Engineering, University of Padova, Stradella San Nicola 3, 36100, Vicenza, Italy

    Luigi Salmaso & Livio Corain

Authors
  1. Lara Maritan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rebecca Piovesan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gregorio Dal Sasso
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Siwar Baklouti
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lluís Casas
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Claudio Mazzoli
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Luigi Salmaso
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Livio Corain
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lara Maritan.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Maritan, L., Piovesan, R., Dal Sasso, G. et al. Comparison between different image acquisition methods for grain-size analysis and quantification of ceramic inclusions by digital image processing: how much similar are the results?. Archaeol Anthropol Sci 12, 167 (2020). https://doi.org/10.1007/s12520-020-01096-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12520-020-01096-0

Keywords

Search

Navigation