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A Novel Approach to Tongue Standardization and Feature Extraction

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

Abstract

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.

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Acknowledgement

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: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12265. Springer, Cham. https://doi.org/10.1007/978-3-030-59722-1_4

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  • DOI: https://doi.org/10.1007/978-3-030-59722-1_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59721-4

  • Online ISBN: 978-3-030-59722-1

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