Recognition of fungiform papillae in tongue images

Ríos, Homero; Valencia, E.; Montes, F.M.; Marín, Antonio; Silva, E.; Herrera, R.

doi:10.1109/conielecomp.2012.6189917

Cited by 4 publications

(7 citation statements)

References 6 publications

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“…Most studies count the number of FP on the tongue manually over a small region(s) on the anterior tongue tip. Recently, automated methods to detect FP from digital images have been proposed [32] , [36] , [37] , [43] . In this study, we adapted an algorithm based on colour segmentation previously proposed by Rios and colleagues [36] to detect FP more accurately, and calculate the density and regional distribution of the FP over the tongue.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, automated methods to detect FP from digital images have been proposed [32] , [36] , [37] , [43] . In this study, we adapted an algorithm based on colour segmentation previously proposed by Rios and colleagues [36] to detect FP more accurately, and calculate the density and regional distribution of the FP over the tongue. Here, we quantified FP count on the first 2 cm of the anterior part of the tongue, where it has previously been reported that 87% of the FP are located [5] , [40] .…”

Section: Discussionmentioning

confidence: 99%

“…An image colour based segmentation algorithm adapted from Rios et al, [36] , implemented using the image processing toolbox in Matlab (MATLAB 2016a, The MathWorks, Natick, USA, 2016), was used to detect and quantify FP on the anterior 2 cm of the tongue (scripts provided as Supplementary material).…”

Section: Methodsmentioning

confidence: 99%

“…To reduce discrepancies between assessors, Nuessle and colleagues [29] proposed the ‘Denver Papillae Protocol’ as a standardised method to characterise FP, this method is based on manual counting papillae in a dyed 10 mm circle of the tongue. Recently, automated methods to detect FP from digital images have been proposed [32] , [36] , [37] , [43] , however most of these methods still require staining of the tongue or specialised methods to acquire the image. Overall, the lack of a standardised approach to measuring FP density contributes to discrepancies between studies, especially when exploring variation across taste phenotypes, thus highlighting the need for a robust, accurate method that can be applied outside of the laboratory environment and across large scale studies.…”

Section: Introductionmentioning

confidence: 99%

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An automated method to detect and quantify fungiform papillae in the human tongue: Validation and relationship to phenotypical differences in taste perception

Eldeghaidy

Thomas

Skinner

et al. 2018

Physiology & Behavior

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Determination of the number of fungiform papillae (FP) on the human tongue is an important measure that has frequently been associated with individual differences in oral perception, including taste sensitivity. At present, there is no standardised method consistently used to identify the number of FP, and primarily scientists manually count papillae over a small region(s) of the anterior tip of a stained tongue. In this study, a rapid automated method was developed to quantify the number of FP across the anterior 2 cm of an unstained tongue from high resolution digital images. In 60 participants, the automated method was validated against traditional manual counting, and then used to assess the relationship between the number of FP and taste phenotype (both 6-n-propylthiouracil (PROP) and Thermal Taster Status). FP count on the anterior 2 cm of the tongue was found to correlate significantly with PROP taster status. PROP supertasters (PSTs) had a significantly higher FP count compared with PROP non-tasters (PNTs). Conversely, the common approach used to determine the number of FP in a small 6 mm diameter circle on the anterior tongue tip, did not show a significant correlation irrespective of whether it was determined via automated or manual counting. The regional distribution of FP was assessed across PROP taster status groups. PSTs had a significantly higher FP count within the first centimetre of the anterior tongue compared with the PNT and PROP medium-tasters (PMT), with no significant difference in the second centimetre. No significant relationship was found with Thermal Taster Status and FP count, or interaction with PROP taster status groups, supporting previous evidence suggesting these phenomena are independent. The automated method is a valuable tool, enabling reliable quantification of FP over the anterior 2 cm surface of the tongue, and overcomes subjective discrepancies in manual counting.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An automated method to detect and quantify fungiform papillae in the human tongue: Validation and relationship to phenotypical differences in taste perception

Eldeghaidy

Thomas

Skinner

et al. 2018

Physiology & Behavior

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“…Then, the procedure allowed to automatically identify the FP as circular-like elements (whit a diameter ranging from 0.30 to 1.05 mm) and to compute the frequency of these circular-like elements in classes with varied diameter size. Eldeghaidy and colleagues proposed a color-based segmentation method based on the procedure used by Rios et al [22]. The authors manually selected three structures of interest -fungiform papillae, filiform papillae and tongue base, and transformed the colour space of these regions from RGB into LAB to minimize the sensitivity for illumination differences, before taking the average colour of each region.…”

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confidence: 99%

Comparison of manual and machine learning image processing approaches to determine fungiform papillae on the tongue

Cattaneo

Liu

Wang

et al. 2020

Preprint

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AbstractHuman taste perception is associated with the papillae on the tongue as they contain a large proportion of chemoreceptors for basic tastes and other chemosensation. Especially the density of fungiform papillae (FP) is considered as an index for responsiveness to oral chemosensory stimuli. The standard procedure for FP counting involves visual identification and manual counting of specific parts of the tongue by trained operators. This is a tedious task and automated image analysis methods are desirable. In this paper a machine learning image processing method based on a convolutional neural network is presented. This automated method was compared with three standard manual FP counting procedures using tongue pictures from 132 subjects. Automated FP counts, within the selected areas and the whole tongue, significantly correlated with the manual counting methods (all ρs ≥ 0.76). When comparing the images for gender and PROP status, the density of FP predicted from automated analysis was in good agreement with data from the manual counting methods, especially in the case of gender. Moreover, the present results reinforce the idea that caution should be applied in considering the relationship between FP density and PROP responsiveness since this relationship can be an oversimplification of the complexity of phenomena arising at the central and peripherical levels. Indeed, no significant correlations were found between FP and PROP bitterness ratings using the automated method for selected areas or the whole tongue. Besides providing estimates of the number of FP, the machine learning approach used a tongue coordinate system that normalizes the size and shape of an individual tongue and generated a heat map of the FP position and normalized area they cover. The present study demonstrated that the machine learning approach could provide similar estimates of FP on the tongue as compared to manual counting methods and provide estimates of more difficult-to-measure parameters, such as the papillae’s areas and shape.

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