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![Tongue body colour samples [(left-right) light white, light red, red, deep red and purple]. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)](publication/301758325/figure/fig1/AS:357955343011851@1462354612829/Tongue-body-colour-samples-left-right-light-white-light-red-red-deep-red-and.png)
Tongue body colour samples [(left-right) light white, light red, red, deep red and purple]. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Source publication
The evaluation of tongue colour has been an important approach to examine human health in Kampo medicine (traditional Japanese medicine) because the change in tongue colour may suggest physical or mental disorders. Several tongue colour quantification methods have been published to objectify clinical information among East Asian countries. However,...
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... also evaluated three (k = 3) and five (k = 5) cluster approaches to verify the appropriateness of the four cluster approach, as shown in Figs. 9 and 10, respectively. For k = 3, overdetection of the tongue coating area (including the tongue body area) and insufficient detection of the tongue body area were evident, as shown by 2 and 3 in Fig. 9, respectively. For k = 5, excessively separated tongue body images were evident, as shown by 3, 4 and 5 in Fig. ...Context 2
... cluster approach, as shown in Figs. 9 and 10, respectively. For k = 3, overdetection of the tongue coating area (including the tongue body area) and insufficient detection of the tongue body area were evident, as shown by 2 and 3 in Fig. 9, respectively. For k = 5, excessively separated tongue body images were evident, as shown by 3, 4 and 5 in Fig. ...Context 3
... in Tables 1 and 2, respectively. Because of the small sample size (n < 10), the median values of light white, purple, grey and black are also described. Clusters comprising five tongue body colours (light white, light red, red, deep red and purple) and six tongue coating colours (white, white-yellow, yellow, brown, grey and black) are shown in Figs. 11 and 12, respectively. Each cluster centre is shown as *. In tongue body colour analysis, the statistical differences (P < 0.01) can be observed in a* and b* values among light red, red and deep red. In tongue coating colour analysis, statistical differences (P < 0.01) can be observed in L* and b* values among white, white-yellow, yellow and ...Context 4
... average L à a à bà colour values of summed tongue body and coating colour groups are shown in Table 3. Clusters comprising entire tongue body and coating colours are shown in Fig. 13. Each cluster centre is shown as *. The statistical differences (P < 0.01) can be observed only for a* ...Context 5
... difficult to obtain sufficient sample images for all tongue colours, we were able to demonstrate a partial statistical difference between the primary body and coating colours (Tables 1 and 2). Although overlaps were evident among the tongue body and coating clusters in the CIELAB colour space, the difference of each cluster's centre was evident (Figs. 11 and 12). The difference between 2 clusters comprising entire tongue body and coating colours was also evident (Fig. 13). These results suggest Table 2 Average L*a*b* values of each tongue coating colour [yMedian value (interquartile range), **P < 0.01]. that all of the differences depend on a* or b* values constantly and the appropriateness of ...Context 6
... difference between the primary body and coating colours (Tables 1 and 2). Although overlaps were evident among the tongue body and coating clusters in the CIELAB colour space, the difference of each cluster's centre was evident (Figs. 11 and 12). The difference between 2 clusters comprising entire tongue body and coating colours was also evident (Fig. 13). These results suggest Table 2 Average L*a*b* values of each tongue coating colour [yMedian value (interquartile range), **P < 0.01]. that all of the differences depend on a* or b* values constantly and the appropriateness of K-means (k = 4) clustering for tongue image analysis under the illumination uniformity of DS01-B. Similar ...Context 7
... difference is diagnostic procedure or physician ability for tongue colour identification [2][3][4][5]26]. A tongue colour evaluation method with appropriate viewing conditions has not been established among physicians, researchers or facilities, and globally unified tongue colour diagnostic criteria have not been established to date. As shown in Figs. 11 and 12, physicians evaluate the differences in tongue colour in a very narrow range of CIELAB colour, and it is difficult to form consensus about diagnostic criteria. If we can establish a robust tongue colour quantification method with high reliability, it will become possible to overcome these unsolved problems because we can use a common ...Similar publications
Objectives
Evaluation of tongue colour is an important approach for assessment of human health in traditional East Asian medicine, which originated in ancient China. However, tongue colour analyses are unreliable due to poor quantification and reproducibility. Given these limitations, the utility of this technique as a clinical index has not been d...
Citations
... One of the diagnostic methods used in Kampo medicine, TCM, and Korean medicine is tongue diagnosis. Tongue diagnosis is a noninvasive diagnostic method and involves observation of the patient's tongue color (light white, light red, red, deep red, and purple), tongue coating (white, white-yellow, yellow, brown, gray, and black), and shape [10]. Tongue fndings can provide information on the patient's constitution, blood circulation, water metabolism, and other systemic physiological/pathological conditions [11][12][13][14][15][16][17][18][19]; therefore, tongue diagnosis can lead to the selection of the appropriate prescriptions for the patients. ...
Tongue diagnosis is one of the important diagnostic methods in Kampo (traditional Japanese) medicine, in which the color and shape of the tongue are used to determine the patient’s constitution and systemic symptoms. Tongue diagnosis is performed with the patient in the sitting or supine positions; however, the differences in tongue color in these two different positions have not been analyzed. We developed tongue image analyzing system (TIAS), which can quantify tongue color by capturing tongue images in the sitting and supine positions. We analyzed the effects on tongue color in two different body positions. Tongue color was quantified as L ∗ a ∗ b ∗ from tongue images of 18 patients in two different body positions by taking images with TIAS. The CIEDE 2000 color difference equation (ΔE00) was used to assess the difference in tongue color in two different body positions. Correlations were also determined between ΔE00, physical characteristics, and laboratory test values. The mean and median ΔE00 for 18 patients were 2.85 and 2.34, respectively. Of these patients, 77.8% had a ΔE00 < 4.1. A weak positive correlation was obtained between ΔE00 and systolic blood pressure and fasting plasma glucose. Approximately 80% of patients’ tongue color did not change between the sitting and supine positions. This indicates that the diagnostic results of tongue color are trustworthy even if medical professionals perform tongue diagnosis in two different body positions.
... These systems are costly since, it uses dedicated image acquisition, color calibration, image segmentation, feature extraction and processing software which can't be used at remote places [4][5][6][7][8]. Different ATDS uses different image acquisition and pre-processing techniques with consistent results [9][10][11][12] also, different color correction methods are proposed to overcome noise [9,13,14] in the system. For image segmentation different researchers used different methods using supervised and unsupervised methods. ...
... In that tongue analysis is one the method where expert observes the tongue of patient and gives judgment as per his/her knowledge. This examination majorly depends on expertise and knowledge of examiner [9,10,13,28]. To decrease the dependency and improve efficiency Automated tongue diagnosis systems (ATDS) are designed but the these are costly and can't be available at remote places [14,29]. ...
... To decrease the dependency and improve efficiency Automated tongue diagnosis systems (ATDS) are designed but the these are costly and can't be available at remote places [14,29]. Research is being going on to make the system less costly and portable [13]. Now a days digital camera/mobile phone based camera are available and can be used at remote places. ...
Due to the change in life style after covid-19 there is demand for non-invasive contact less healthcare monitoring systems. In India oral cancer rate are increasing and becoming the community health issue with high mortality rate. Mortality rate can be reduced by identification of disease at initial stages. The major hindrance in disease identification is availability of dedicated hardware at remote and identification at initial stage. Research is going on to make the device portable and less costly using digital images. Also, research is going on to have hybrid algorithm which can segment smaller area abnormal area and classify reliably. This paper focuses on tongue cancer which is one of the types of oral cancer and presents comparison of hybrid algorithm using firefly and watershed transformation to segment smaller area using digital images which reduces cost of dedicated hardware required. 150 digital images are used which are available on internet or provided by cancer hospital for analysis and classification using CNN along with augmentation. 90.48% accuracy is achieved and desirable results are obtained using hybrid algorithm being used.
... Bi-Elliptical Deformable Contour is used to segment the image of the tongue. The front image of the tongue is used to extract the tongue's geometry, texture, and colour features, which can be categorized by utilizing an SVM classifier [6][7][8][9]. ...
Oral cancer is a main worldwide health problem accounting for 606,520 deaths in 2020, and it is most predominant in the middle-and low-income nations. Permitting computerization in identifying potentially malignant and malignant lesions in the oral cavity would possibly result in low-expense and early detection of the disease. The most important purpose of this research is to find the Oral Cancer Lesions affected region in the tongue images. The current work utilized the GVF algorithm to detect Oral Cancer Lesions using features involved in tongue images. This article offers a novel approach to merging bounding box annotations from different medical practitioners. Additionally, gradient vector flow was used to segment images, where the complicated patterns have been obtained for tackling this difficult task. Using the initial data gathered in this study, a hybrid classifier algorithm was assessed to detect Oral cancer lesions, and features like colour, texture and geometry were extracted. BioMed Chinese Medicine Repository collects the tongue images. Additionally, performances are described categorizing as per the kind of referral decision. Our initial findings establish support vector machine has the probability of challenging this stimulating task.
... Colorimetric information, including brightness, contrast, and uniformity, are frequently used in characterizing and describing properties of objects and substances of interest, both in daily life and in specific circumstances. Its applications are very broad from art, the fashion industry, food science, medical science, oriental medicine, agriculture, geology, chemistry, biology, material science, environmental engineering, etc. [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. For ease of access and portable solutions, smartphone-based spectrometers and colorimeters have been studied worldwide [21][22][23][24][25][26][27][28][29][30]. ...
... As an effective alternative medicine, oriental medicine (called by different names in different countries: traditional Chinese medicine (TCM, 中医, Zhōngyī), traditional Korean medicine (ᄒ ᅡ ᆫᄇ ᅡ ᆼ, 韓方, Hanbang) and traditional Chinese medicine in Japan (漢方, Kampo)) utilizes tongue diagnosis as a major method to assess the patient's health conditions. Oriental medicine doctors examine the color, shape, and texture of a patient's tongue [12][13][14][15][16][17][18][19][20]44]. Tongue color, shape and texture can give the pre-disease indications without any significant health problems or disease-related symptoms. ...
... To provide more consistent and objective health assessments through tongue diagnosis, quantification of tongue color and texture from digital images seem to be a very attractive approach. Many researchers have reported their ideas and approaches, and they seen very encouraging results for years [12][13][14][15][16][17][18][19][20]44]. Figure 10 shows a sample tongue image from an acupuncturist's site [44] and color analysis results using the color analysis of ROIs. Figure 10a,b are sample tongue photos of a patient. ...
Image-based colorimetry has been gaining relevance due to the wide availability of smart phones with image sensors and increasing computational power. The low cost and portable designs with user-friendly interfaces, and their compatibility with data acquisition and processing, are very attractive for interdisciplinary applications from art, the fashion industry, food science, medical science, oriental medicine, agriculture, geology, chemistry, biology, material science, environmental engineering, and many other applications. This work describes the image-based quantification of color and its machine vision and offline applications in interdisciplinary fields using specifically developed image analysis software. Examples of color information extraction from a single pixel to predetermined sizes/shapes of areas, including customized regions of interest (ROIs) from various digital images of dyed T-shirts, tongues, and assays, are demonstrated. Corresponding RGB, HSV, CIELAB, Munsell color, and hexadecimal color codes, from a single pixel to ROIs, are extracted for machine vision and offline applications in various fields. Histograms and statistical analyses of colors from a single pixel to ROIs are successfully demonstrated. Reliable image-based quantification of color, in a wide range of potential applications, is proposed and the validity is verified using color quantification examples in various fields of applications. The objectivity of color-based diagnosis, judgment and control can be significantly improved by the image-based quantification of color proposed in this study.
... The collected vascular bundles were used as the initial cluster centers. The distances between the pixel points and vascular bundle centers were calculated, and the pixel points were clustered into the classes where the nearest vascular bundles were located (Kawanabe et al., 2016). ...
Stone cells are the brachysclereid cells in pear (Pyrus) fruit, consisting almost entirely of lignified secondary cell walls. They are distributed mainly near the fruit core and spread radially in the whole fruit. However, the development of stone cells has not been comprehensively characterized. The regulation of stone cell formation on transcriptomic, proteomic and metabolomic levels remains largely unknown. In this study, we performed phenomic analysis on the stone cells and their associated vascular bundles distributed near the fruit cores. Transcriptomic, proteomic and metabolomic analyses revealed significantly positive regulation of biological processes, which contributed to the lignification and lignin deposition in stone cells near the fruit core, including sucrose metabolism, phenylalanine, tyrosine, tryptophan, and phenylalanine biosynthesis. We then found many metabolites generated from the phenylpropanoid pathway, contributing to the cell wall formation of stone cells near the fruit core. Furthermore, we identified a key transcription factor, PbbZIP48, which was significantly highly expressed near the fruit core and was then proven to regulate the lignin biosynthesis in stone cells. In conclusion, we provide new insights into understanding the mechanism of lignified stone cell formation near the pear fruit core at multi‐omics levels.
... Common unsupervised methods for segmentation of biomedical images include Kmeans clustering and thresholding [7,8]. However, they might fail in complex scenarios like the segmentation of brain tumors, where thresholding failed to provide consistent outputs, whilst DL approaches have achieved better results [9]. ...
... However, they might fail in complex scenarios like the segmentation of brain tumors, where thresholding failed to provide consistent outputs, whilst DL approaches have achieved better results [9]. K-means also suffers from a lack of consistent outcomes and might need human validation due to its unsupervised nature [8]. DL models, particularly convolutional neural networks (CNNs), have proven to be effective tools for the segmentation of a wide range of biomedical images [10][11][12][13]. ...
Efforts have been made to diagnose and predict the course of different neurodegenerative diseases through various imaging techniques. Particularly tauopathies, where the tau polypeptide is a key participant in molecular pathogenesis, have significantly increased their morbidity and mortality in the human population over the years. However, the standard approach to exploring the phenomenon of neurodegeneration in tauopathies has not been directed at understanding the molecular mechanism that causes the aberrant polymeric and fibrillar behavior of the tau protein, which forms neurofibrillary tangles that replace neuronal populations in the hippocampal and cortical regions. The main objective of this work is to implement a novel quantification protocol for different biomarkers based on pathological post-translational modifications undergone by tau in the brains of patients with tauopathies. The quantification protocol consists of an adaptation of the U-Net neural network architecture. We used the resulting segmentation masks for the quantification of combined fluorescent signals of the different molecular changes tau underwent in neurofibrillary tangles. The quantification considers the neurofibrillary tangles as an individual study structure separated from the rest of the quadrant present in the images. This allows us to detect unconventional interaction signals between the different biomarkers. Our algorithm provides information that will be fundamental to understanding the pathogenesis of dementias with another computational analysis approach in subsequent studies.
... K-Means clustering and thresholding are common unsupervised methods that have been used recently to segment biomedical images (Kawanabe et al., 2016;Salvi et al., 2019). However, they might fail in complex scenarios like the segmentation of brain tumors, where thresholding failed to provide consistent outputs, whilst DL approaches have achieved better results (Van Eycke et al., 2018). ...
... However, they might fail in complex scenarios like the segmentation of brain tumors, where thresholding failed to provide consistent outputs, whilst DL approaches have achieved better results (Van Eycke et al., 2018). K-Means also suffers from lack of consistent outcomes and might need human validation due to its unsupervised nature (Kawanabe et al., 2016). ...
Through various imaging techniques, efforts have been made to diagnose and predict the course of different neurodegenerative diseases, which significantly increase their morbidity and mortality in the human population over the years, especially proteinopathies where the Tau polypeptide is a key participant in molecular pathogenesis. However, this standard approach to explore the phenomenon of neurodegener-ation has not been directed at understanding the molecular mechanism that causes the aberrant polymeric and fibrillar behavior of Tau protein in the neurofibrillary tangles that replace neuronal populations comprising the hippocampal formation and cortical regions of clinical cases with various proteinopathies. Our main objective was to implement a novel approach based on computational seg-mentation protocols in triple-labeled immunofluorescence imaging with different biomarkers based on pathological post-translational modifications undergone by Tau in brains of patients with tauopathies, implementing an adaptation of the U-Net neural network architecture. Importantly, we used the resulting segmentation masks and significantly quantified the individual and combined fluorescent signals of the different molecular changes Tau underwent in neurofibrillary tangles as individual study structures. Then, we separated them from the rest of the quadrant, detecting unconventional interaction signals between the different biomarkers. Our algorithm provides information that will be fundamental to understand the pathogenesis of dementias with another computational analysis approach in subsequent studies.
... The quantification of tongue findings has been attracting attention globally (19)(20)(21)(22), and if the accumulation of data for TIAS is encouraged in the future, it will facilitate better understanding of the pathological conditions and tongue diagnosis. This would provide support to medical personnel who are inexperienced in tongue diagnosis. ...
Aim
In tongue diagnosis, a dark purple tongue and enlarged sublingual vein are important findings of Oketsu (blood stasis). However, the association between the tongue color and the sublingual vein has not been reported. This study investigated the association between the tongue color values and the sublingual vein width using tongue image analyzing system (TIAS) for the objective assessment of blood stasis.
Methods
A total of 38 patients (age 68.7 ± 11.3 years, 14 men and 24 women) who visited the Department of Kampo Medicine at Chiba University Hospital were included. Physical findings, blood test results, blood stasis score from medical records, and tongue images obtained with TIAS were analyzed. The patients were classified into two groups: patients with a sublingual vein width of ≤2.5 mm (20 patients) and those with a width of >2.5 mm (18 patients). The physical findings and the blood test results of the two groups were analyzed by Wilcoxon's rank-sum test or χ ² -test, whereas logistic regression analysis was used to determine the association between the tongue color values and sublingual vein width. Receiver operating characteristic (ROC) analysis was used to differentiate blood stasis.
Results
The color values significantly related to the sublingual vein width (mm) were the P1-L * and P4-L * (darkness of the tongue edge and tongue apex) and the P1-b * and P2-b * (blueness of the tongue edge and tongue posterior). The area under the curve was greater for the combination of the tongue color values and the sublingual vein width than that for either of them.
Conclusion
This study demonstrated an objective evaluation of blood stasis in the tongue of patients with dark-blue discoloration and an enlarged sublingual vein. In addition, the combination of the tongue color and the sublingual vein is expected to facilitate a more reliable diagnosis of blood stasis.
... The color of tongue represents the effective working of internal organs such as pancreas, liver, intestines, liver and so on 15 . Kawanabe et al. employed machine learning strategies to perform tongue color based diagnosis strategy based on Japanese Traditional Medicine 16 . For early prognosis of diabetes disease support vector machines (SVM) based strategy was designed with 296 diabetic and 531 non-diabetic patient tongue images and the model reported 83.06% of classification accuracy on employing PCA for feature selection and GA for parameter optimization 17 . ...
... The studies mostly employed few dataset for training the model that affects the model performance and also they highly concentrated on tongue colour and tooth markings as significant features. Based on the review made on all the existing works of this application area, the various limitations inferred includes: -Few methods are not applicable for large volumes of data [15][16][17][18][19][20] . ...
Diabetes is a serious metabolic disorder with high rate of prevalence worldwide; the disease has the characteristics of improper secretion of insulin in pancreas that results in high glucose level in blood. The disease is also associated with other complications such as cardiovascular disease, retinopathy, neuropathy and nephropathy. The development of computer aided decision support system is inevitable field of research for disease diagnosis that will assist clinicians for the early prognosis of diabetes and to facilitate necessary treatment at the earliest. In this research study, a Traditional Chinese Medicine based diabetes diagnosis is presented based on analyzing the extracted features of panoramic tongue images such as color, texture, shape, tooth markings and fur. The feature extraction is done by Convolutional Neural Network (CNN)-ResNet 50 architecture, and the classification is performed by the proposed Deep Radial Basis Function Neural Network (RBFNN) algorithm based on auto encoder learning mechanism. The proposed model is simulated in MATLAB environment and evaluated with performance metrics-accuracy, precision, sensitivity, specificity, F1 score, error rate, and receiver operating characteristics (ROC). On comparing with existing models, the proposed CNN based Deep RBFNN machine learning classifier model outperformed with better classification performance and proving its effectiveness.
... To quantify tongue body and coating information k-means clustering algorithm a machine learning method was implemented. 60 The database used for their research consisted of 1080 images of Japanese test subjects which were divided into two groups of 550 for tongue body and 516 for coating. Chinese developed image acquisition system DS01-B was used for data collection. ...
Tongue diagnosis is used in various traditional medicine cultures as a non-invasive method for assessing an individual's health. Tongue image analysis has the potential for assessing the metabolism and functionality of the internal organs, making it a quick method of diagnosis. As automated systems give quantitative and objective results thereby effective in facilitating diagnosis, a review was conducted to evaluate literature on current methods of tongue diagnosis. Different methods of tongue diagnosis in the literature were identified and compared. Information on automated tongue diagnosis system, such as image acquisition, color correction, segmentation, feature extraction and classification, particularly in traditional medicine were reviewed. The aim of the review was to identify effective image processing techniques to be compatible with automated system for tongue diagnosis using some easily available to all imaging device rather than a dedicated state of art acquisition systems, which may not be easily accessible to general public. All methods identified were either being researched or developed and no specific system was identified that is currently available for routine use in clinics or home monitoring for patients. The healthcare sector could benefit from access to validated and automated tongue diagnosis systems. The feasibility of a mobile enabled platform to intelligently make use of this traditional method of diagnosis should be explored. In order to provide cheap and quick preliminary diagnosis for clinical practice automation of this noninvasive traditional technique can prove to be a boon for health care sector.
