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A Novel Approach to Tongue Standardization and Feature Extraction

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12265)


Fungiform papillae are large protrusions on the human tongue and contain many taste-buds. Most are found on the tip and the sides of the tongue, and their distribution varies from person to person. In this paper, we introduce a tongue-based coordinate system to investigate the density and other features of fungiform papillae on the surface of the tongue. A traditional method for estimating the density of fungiform papillae is to count the papillae in either a manually selected area or a predefined grid of areas on the tongue. However, depending on how a person presents his or her tongue in a specific image (such as narrowing, widening, and bending), this can cause visual variations in both the papillae’s apparent positions and apparent shapes, which in turn also affects the counts obtained within an area. By transforming the individual tongues into a standardized tongue, our tongue coordinate system minimizes these variations more effectively than current alignment-based methods. We further hypothesize an underlying fungiform papillae distribution for each tongue, which we estimate and use to perform statistical analysis on the different tongue categories. For this, we consider a cohort of 152 persons and the following variables: gender, ethnicity, ability to taste 6-n-propylthiouracil, and texture preference. Our results indicate possible new relations between the distribution of fungiform papillae and some of the aforementioned variables.


  • Tongue mapping
  • Papillae distribution
  • Taste perception

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  1. Breslin, P., Huang, L.: Human taste: peripheral anatomy, taste transduction, and coding. Adv. Otorhinolaryngol. 63, 152–190 (2006).

    CrossRef  Google Scholar 

  2. Cattaneo, C., Liu, J., Wang, C., Pagliarini, E., Sporring, J., Bredie, W.L.P.: Comparison of manual and machine learning image processing approaches to determine fungiform papillae on the tongue. bioRxiv (2020).

  3. Eldeghaidy, S., et al.: An automated method to detect and quantify fungiform papillae in the human tongue: validation and relationship to phenotypical differences in taste perception. Physiol. Behav. 184, 226–234 (2018).

    CrossRef  Google Scholar 

  4. Essick, G., Chopra, A., Guest, S., McGlone, F.: Lingual tactile acuity, taste perception, and the density and diameter of fungiform papillae in female subjects. Physiol. Behav. 80, 289–302 (2003).

    CrossRef  Google Scholar 

  5. Hapa, A., Aksoy, B., Aslan, U., Atakan, N.: Common tongue conditions affect quality of life: an issue to be recognized. Qual. Life Res. 21, 777–782 (2012).

    CrossRef  Google Scholar 

  6. Maris, E., Oostenveld, R.: Nonparametric statistical testing of EEG- and MEG-data. J. Neurosci. Methods 164(2007), 177–190 (2007).

    CrossRef  Google Scholar 

  7. Masi, C., Dinnella, C., Monteleone, E., Prescott, J.: The impact of individual variations in taste sensitivity on coffee perceptions and preferences. Physiol. Behav. 138, 219–226 (2015).

    CrossRef  Google Scholar 

  8. Nachtsheim, R., Schlich, E.: The influence of 6-n-propylthiouracil bitterness, fungiform papilla count and saliva flow on the perception of pressure and fat. Food Qual. Prefer. 29(2), 137–145 (2013).

    CrossRef  Google Scholar 

  9. Piochi, M., Dinnella, C., Prescott, J., Monteleone, E.: Associations between human fungiform papillae and responsiveness to oral stimuli: effects of individual variability, population characteristics, and methods for papillae quantification. Chem. Senses 43(5), 313–327 (2018).

    CrossRef  Google Scholar 

  10. Piochi, M., et al.: Comparing manual counting to automated image analysis for the assessment of fungiform papillae density on human tongue. Chem. Senses 42(7), 553–561 (2017).

    CrossRef  Google Scholar 

  11. Sanders, I., Mu, L.: A three-dimensional atlas of human tongue muscles. Anat. Rec. (Hoboken) 296(7), 1102–1114 (2013).

    CrossRef  Google Scholar 

  12. Sanyal, S., O’Brien, S., Hayes, J., Feeney, E.: TongueSim: development of an automated method for rapid assessment of fungiform papillae density for taste research. Chem. Senses 41(4), 357–365 (2016).

    CrossRef  Google Scholar 

  13. Valencia, E., et al.: Automatic counting of fungiform papillae by shape using cross-correlation. Comput. Biol. Med. 76, 168–172 (2016).

    CrossRef  Google Scholar 

  14. Whitehead, M., Beeman, C., Kinsella, B.: Distribution of taste and general sensory nerve endings in fungiform papillae of the hamster. Am. J. Anat. 173(3), 185–201 (1985).

    CrossRef  Google Scholar 

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This work is supported by The Center for Quantification of Imaging Data from MAX IV (QIM) funded by The Capital Region of Denmark; and also by Arla Foods amba, Viby, Denmark as part of a postdoctoral grant.

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Correspondence to Chenhao Wang .

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Wang, C., Cattaneo, C., Liu, J., Bredie, W., Pagliarini, E., Sporring, J. (2020). A Novel Approach to Tongue Standardization and Feature Extraction. In: , et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12265. Springer, Cham.

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