Objective assessment of blood stasis using computerized inspection of sublingual veins

The relative area score for sublingual varices reliability measurement: a diagnostic study

BACKGROUND

Sublingual varices (SV) and their predictive potential for other clinical parameters is a much studied topic in oral medicine. SVs have been well studied as predictive markers for many common diseases such as arterial hypertension, cardiovascular disease, smoking, type 2 diabetes mellitus and age. Despite many prevalence studies, it is still unclear how the reliability of SV inspection affects its predictive power. The aim of this study was to quantify the inspection reliability of SV.

METHODS

In a diagnostic study, the clinical inspection of 78 patients by 23 clinicians was examined for the diagnosis of SV. Digital images of the underside of the tongue were taken from each patient. The physicians were then asked to rate them for the presence of sublingual varices (0/1) in an online inspection experiment. Statistical analysis for inter-item and inter-rater reliability was performed in a τ-equivalent measurement model with Cronbach's [Formula: see text] and Fleiss κ.

RESULTS

The interrater reliability for sublingual varices was relatively low with κ = 0.397. The internal consistency of image findings for SV was relatively high with α≈ 0.937. This shows that although SV inspection is possible in principle, it has a low reliability R. This means that the inspection finding (0/1) of individual images often cannot be reproduced stably. Therefore, SV inspection is a difficult task of clinical investigation. The reliability R of SV inspection also limits the maximum linear correlation [Formula: see text] of SV with an arbitrary other parameter Y. The reliability of SV inspection R = 0.847 limits the maximum correlation to [Formula: see text] (SV, Y) = 0,920-a 100% correlation was a priori not achievable in our sample. To overcome the problem of low reliability in SV inspection, we propose the RA (relative area) score as a continuous classification system for SV, which normalises the area of visible sublingual veins to the square of the length of the tongue, providing a dimensionless measure of SV.

CONCLUSIONS

The reliability of the SV inspection is relatively low. This limits the maximum possible correlation of SV with other (clinical) parameters. SV inspection reliability is an important indicator for the quality of SV as a predictive marker. This should be taken into account when interpreting previous studies on SV and has implications for future studies. The RA score could help to objectify the SV examination and thus increase its reliability.

Multiple color representation and fusion for diabetes mellitus diagnosis based on back tongue images

Tongue images have been proved to be effective in diabetes mellitus (DM) diagnosis. Without requirement of collecting blood sample, tongue image based diagnosis approach is non-invasive and convenient for the patients. Meanwhile, the colors of tongues play an important in aiding accurate diagnosis. However, the tongues' colors fall on a small color gamut that makes it difficult for the existing color descripts to identify and distinguish the tiny difference of the tongues. To tackle this problem, we introduce a novel color descriptor by representing the colors with the clustering centers, namely color centroid points, of the color points sampled from tongue images. In order to boost the capacity of the descriptor, we extend it into three color spaces, i.e., RGB, HSV and LAB to mine a rich set of color information and exploit the complementary information among the three spaces. Since there exist correlation and complementarity among the features extracted from the three color spaces, we propose a novel multiple color features fusion method for DM diagnosis. Particularly, two projections are learned to project the multiple features to their corresponding shared and specific subspaces, in which their similarity and diversity are firstly measured by the Euclidean Distance and Hilbert Schmidt Independence Criterion (HSIC), respectively. To fully exploit the similar and complementary information, the two components are jointly transformed to their label vector, efficiently embedding the discriminant prior into the model, leading to significant improvement in the diagnosis outcomes. Experimental results on clinical tongue dataset substantiated the effectiveness of our proposed clustering-based color descriptor and the proposed multiple colors fusion approach. Overall, the proposed pipeline for the diagnosis of DM using back tongue images, achieved an average accuracy of up to 93.38%, indicating its potential toward realization of a clinical diagnostic tool for DM. Without loss generality, we also assessed the performance of the novel multiple features fusion method on two public datasets. The experiments prove the superiority of our multiple features learning model on general real-life application.

Missing-view completion for fatty liver disease detection

Fatty liver disease is a common disease that causes extra fat storage in an individual's liver. Patients with fatty liver disease may progress to cirrhosis and liver failure, further leading to liver cancer. The prevalence of fatty liver disease ranges from 10% to 30% in many countries. In general, detecting fatty liver requires professional neuroimaging modalities or methods such as computed tomography, ultrasound, and medical experts' practical experiences. Considering this point, finding intelligent electronic noninvasive diagnostic approaches are desired at present. Currently, most existing works in the area of computerized noninvasive disease detection often apply one view (modality) or perform multi-view (several modalities) analysis, e.g., face, tongue, and/or sublingual for disease detection. The multi-view data of patients provides more complementary information for diagnosis. However, due to the conditions of data acquisition, interference by human factors, etc., many multi-view data are defective with some missing-view information, making these multi-view data difficult to evaluate. This factor largely affects the performance of classifying disease and the development of fully computerized noninvasive methods. Thus, the purpose of this study is to address the missing view issue among noninvasive disease detection. In this work, a multi-view dataset containing facial, sublingual vein, and tongue images are initially processed to produce corresponding feature for incomplete multi-view disease diagnostic evaluation. Hereby, we propose a novel method, i.e., multi-view completion, to process the incomplete multi-view data in order to complete the missing-view information for classifying fatty liver disease from healthy candidates. In particular, this method can explore the intra-view and inter-view information to produce the missing-view data effectively. Extensive experiments on a collected dataset with 220 fatty liver patients and 220 healthy samples show that our proposed approach achieves better diagnostic results with missing-view completion compared to the original incomplete multi-view data under various classifiers. Related results prove that our method can effectively process the missing-view issue and improve the noninvasive disease detection performance.

Two-phase non-invasive multi-disease detection via sublingual region

Non-invasive multi-disease detection is an active technology that detects human diseases automatically. By observing images of the human body, computers can make inferences on disease detection based on artificial intelligence and computer vision techniques. The sublingual vein, lying on the lower part of the human tongue, is a critical identifier in non-invasive multi-disease detection, reflecting health status. However, few studies have fully investigated non-invasive multi-disease detection via the sublingual vein using a quantitative method. In this paper, a two-phase sublingual-based disease detection framework for non-invasive multi-disease detection was proposed. In this framework, sublingual vein region segmentation was performed on each image in the first phase to achieve the region with the highest probability of covering the sublingual vein. In the second phase, features in this region were extracted, and multi-class classification was applied to these features to output a detection result. To better represent the characterisation of the obtained sublingual vein region, multi-feature representations were generated of the sublingual vein region (based on color, texture, shape, and latent representation). The effectiveness of sublingual-based multi-disease detection was quantitatively evaluated, and the proposed framework was based on 1103 sublingual vein images from patients in different health status categories. The best multi-feature representation was generated based on color, texture, and latent representation features with the highest accuracy of 98.05%.

Computational Traditional Chinese Medicine diagnosis: A literature survey

BACKGROUND AND OBJECTIVE

Traditional Chinese Medicine (TCM) diagnosis is based on the theoretical principles and knowledge, where it is steeped in thousands of years of history to diagnose various types of diseases and syndromes. It can be generally divided into four main diagnostic approaches: 1. Inspection, 2. Auscultation and olfaction, 3. Inquiry, and 4. Palpation, which are widely used in TCM hospitals in China and around the world. With the development of intelligent computing technology in recent years, computational TCM diagnosis has grown rapidly.

METHODS

In this paper, we aim to systematically summarize the development of computational TCM diagnosis based on four diagnostic approaches, mainly focusing on digital acquisition devices, collected datasets, and computational detection approaches (algorithms). Furthermore, all related works of this field are compared and explored in detail.

RESULTS

This survey provides the principles, applications, and current progress in computing for readers and researchers in terms of computational TCM diagnosis. Moreover, the future development direction, prospect, and technological trend of computational TCM diagnosis will also be discussed in this study.

CONCLUSIONS

Recent computational TCM diagnosis works are compared in detail to show the pros/cons, where we provide some meaningful suggestions and opinions on the future research approaches in this area. This work is useful for disease detection in computational TCM diagnosis as well as health management in the smart healthcare area.

INDEX TERMS

Computational diagnosis, Traditional Chinese Medicine, survey, smart healthcare.

Can Traditional Chinese Medicine Diagnosis Be Parameterized and Standardized? A Narrative Review

The integration of Traditional Chinese Medicine (TCM) in Western health systems and research requires a rational communicable theory, scientific proof of efficacy and safety, and quality control measures. The existence of clear definitions and the diagnosis standardization are critical factors to establish the patient's vegetative functional status accurately and, therefore, systematically apply TCM therapeutics such as the stimulation of reflex skin areas known as acupoints. This science-based conceptualization entails using validated methods, or even developing new systems able to parameterize the diagnosis and assess TCM related effects by objective measurements. Traditionally, tongue and pulse diagnosis and the functional evaluation of action points by pressure sensitivity and physical examination may be regarded as essential diagnostic tools. Parameterizing these techniques is a future key point in the objectification of TCM diagnosis, such as by electronic digital image analysis, mechanical pulse diagnostic systems, or the systematic evaluation of acupoints' electrophysiology. This review aims to demonstrate and critically analyze some achievements and limitations in the clinical application of device-assisted TCM diagnosis systems to evaluate functional physiological patterns. Despite some limitations, tongue, pulse, and electrophysiological diagnosis devices have been reported as a useful tool while establishing a person's functional status.

Effect and Safety of Hydroxysafflor Yellow A for Injection in Patients with Acute Ischemic Stroke of Blood Stasis Syndrome: A Phase II, Multicenter, Randomized, Double-Blind, Multiple-Dose, Active-Controlled Clinical Trial

OBJECTIVE

To assess the effect and safety of Hydroxysafflor Yellow A for Injection (HSYAI) in treating patients with acute ischemic stroke (AIS) and blood stasis syndrome (BSS).

METHODS

A multicenter, randomized, double-blind, multiple-dose, active-controlled phase II trial was conducted at 9 centers in China from July 2013 to September 2015. Patients with moderate or severe AIS and BSS were randomly assigned to low-, medium-, high-dose HSYAI groups (25, 50 and 70 mg/d HSYAI by intravenous infusion, respectively), and a control group (Dengzhan Xixin Injection (, DZXXI) 30 mL/d by intravenous infusion), for 14 consecutive days. The primary outcome was the Modified Rankin Scale (mRS) score ⩽1 at days 90 after treatment. The secondary outcomes included the National Institute of Health Stroke Scale (NIHSS) score ⩽1, Barthel Index (BI) score ⩾95, and BSS score reduced ⩾30% from baseline at days 14, 30, 60, and 90 after treatment. The safety outcomes included any adverse events during 90 days after treatment.

RESULTS

Of the 266 patients included in the effectiveness analysis, 66, 67, 65 and 68 cases were in the low-, medium-, and high-dose HSYAI and control groups, respectively. The proportions of patients in the medium- and high-dose HSYAI groups with mRS score ⩽1 at days 90 after treatment were significantly larger than the control group (P<0.05). The incidences of favorable outcomes of NIHSS and BI at days 90 after treatment as well as satisfactory improvement of BSS at days 30 and 60 after treatment in the medium- and high-dose HSYAI groups were all significantly higher than the control group (P<0.05). No significant difference was reported among the 4 groups in any specific adverse events (P>0.05).

CONCLUSIONS

HSYAI was safe and well-tolerated at all doses for treating AIS patients with BSS. The medium (50 mg/d) or high dose (75 mg/d) might be the optimal dose for a phase III trial. (Registration No. ChiCTR-2000029608).

A Novel Discovery: Holistic Efficacy at the Special Organ Level of Pungent Flavored Compounds from Pungent Traditional Chinese Medicine

Pungent traditional Chinese medicines (TCMs) play a vital role in the clinical treatment of hepatobiliary disease, gastrointestinal diseases, cardiovascular diseases, diabetes, skin diseases and so on. Pungent TCMs have a vastness of pungent flavored (with pungent taste or smell) compounds. To elucidate the molecular mechanism of pungent flavored compounds in treating cardiovascular diseases (CVDs) and liver diseases, five pungent TCMs with the action of blood-activating and stasis-resolving (BASR) were selected. Here, an integrated systems pharmacology approach is presented for illustrating the molecular correlations between pungent flavored compounds and their holistic efficacy at the special organ level. First, we identified target proteins that are associated with pungent flavored compounds and found that these targets were functionally related to CVDs and liver diseases. Then, based on the phenotype that directly links human genes to the body parts they affect, we clustered target modules associated with pungent flavored compounds into liver and heart organs. We applied systems-based analysis to introduce a pungent flavored compound-target-pathway-organ network that clarifies mechanisms of pungent substances treating cardiovascular diseases and liver diseases by acting on the heart/liver organ. The systems pharmacology also suggests a novel systematic strategy for rational drug development from pungent TCMs in treating cardiovascular disease and associated liver diseases.

Pharmacokinetic Comparison of Seven Major Bio-Active Components in Normal and Blood Stasis Rats after Oral Administration of Herb Pair Danggui-Honghua by UPLC-TQ/MS

The compatibility between Danggui (Angelicae Sinensis Radix) and Honghua (Carthami Flos) is a known herb pair, which could activate blood circulation and dissipate blood stasis effects. In this paper, we quantified seven main bio-active components (hydroxysafflor yellow A, caffeic acid, p-coumaric acid, kaempferol-3-O-rutinoside, ferulic acid, 3-n-butylphthalide, and ligustilide) in plasma samples in vivo by UPLC-TQ/MS method and investigatedwhether the pharmacokinetic (PK) behaviors of the seven components could be altered in blood stasis rats after oral administration of the Gui-Hong extracts. It was found that the C_max and AUC_0-t of these components in blood stasis rats had increasing tendency compared with normal rats. Most components in model and normal rats had significant difference in some pharmacokinetic parameters, which indicated that the metabolism enzymes and transporters involved in the metabolism and disposition of these bio-active componentsmay bealtered in blood stasis rats. This study was the first report about the pharmacokinetic investigation between normal and blood stasis rats after oral administrationof Gui-Hong extracts, and these results are important and valuable for better clinical applications of Gui-Hong herb pair and relatedTCM formulae.

Herb pair Danggui-Honghua: mechanisms underlying blood stasis syndrome by system pharmacology approach

Herb pair Danggui-Honghua has been frequently used for treatment of blood stasis syndrome (BSS) in China, one of the most common clinical pathological syndromes in traditional Chinese medicine (TCM). However, its therapeutic mechanism has not been clearly elucidated. In the present study, a feasible system pharmacology model based on chemical, pharmacokinetic and pharmacological data was developed via network construction approach to clarify the mechanisms of this herb pair. Thirty-one active ingredients of Danggui-Honghua possessing favorable pharmacokinetic profiles and biological activities were selected, interacting with 42 BSS-related targets to provide potential synergistic therapeutic actions. Systematic analysis of the constructed networks revealed that these targets such as HMOX1, NOS2, NOS3, HIF1A and PTGS2 were mainly involved in TNF signaling pathway, HIF-1 signaling pathway, estrogen signaling pathway and neurotrophin signaling pathway. The contribution index of every active ingredient also indicated six compounds, including hydroxysafflor yellow A, safflor yellow A, safflor yellow B, Z-ligustilide, ferulic acid, and Z-butylidenephthalide, as the principal components of this herb pair. These results successfully explained the polypharmcological mechanisms underlying the efficiency of Danggui-Honghua for BSS treatment, and also probed into the potential novel therapeutic strategies for BSS in TCM.

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification.

Core bioactive components promoting blood circulation in the traditional Chinese medicine compound xueshuantong capsule (CXC) based on the relevance analysis between chemical HPLC fingerprint and in vivo biological effects

Compound xueshuantong capsule (CXC) is an oral traditional Chinese herbal formula (CHF) comprised of Panax notoginseng (PN), Radix astragali (RA), Salvia miltiorrhizae (SM), and Radix scrophulariaceae (RS). The present investigation was designed to explore the core bioactive components promoting blood circulation in CXC using high-performance liquid chromatography (HPLC) and animal studies. CXC samples were prepared with different proportions of the 4 herbs according to a four-factor, nine-level uniform design. CXC samples were assessed with HPLC, which identified 21 components. For the animal experiments, rats were soaked in ice water during the time interval between two adrenaline hydrochloride injections to reduce blood circulation. We assessed whole-blood viscosity (WBV), erythrocyte aggregation and red corpuscle electrophoresis indices (EAI and RCEI, respectively), plasma viscosity (PV), maximum platelet aggregation rate (MPAR), activated partial thromboplastin time (APTT), and prothrombin time (PT). Based on the hypothesis that CXC sample effects varied with differences in components, we performed grey relational analysis (GRA), principal component analysis (PCA), ridge regression (RR), and radial basis function (RBF) to evaluate the contribution of each identified component. Our results indicate that panaxytriol, ginsenoside Rb₁, angoroside C, protocatechualdehyde, ginsenoside Rd, and calycosin-7-O-β-D-glucoside are the core bioactive components, and that they might play different roles in the alleviation of circulation dysfunction. Panaxytriol and ginsenoside Rb₁ had close relevance to red blood cell (RBC) aggregation, angoroside C was related to platelet aggregation, protocatechualdehyde was involved in intrinsic clotting activity, ginsenoside Rd affected RBC deformability and plasma proteins, and calycosin-7-O-β-D-glucoside influenced extrinsic clotting activity. This study indicates that angoroside C, calycosin-7-O-β-D-glucoside, panaxytriol, and protocatechualdehyde may have novel therapeutic uses.

Comparative pharmacokinetics of rhein and chrysophanol after oral administration of Quyu Qingre granules in normal and acute blood stasis rabbits

ETHNOPHARMACOLOGICAL RELEVANCE

Quyu Qingre granules (QYQRGs) are useful traditional Chinese composite prescription in the treatment of blood stasis syndrome. Comparing differences of pharmacokinetic properties of compounds in QYQRG between normal and blood stasis syndrome rabbits can provide much helpful information. The primary objective of this study was to compare the pharmacokinetics of rhein and chrysophanol after orally administering 2.0 g/kg b.w. QYQRG in normal and acute blood stasis model rabbits.

MATERIALS AND METHODS

The blood samples were collected subsequently at 5, 10, 15, 20, 30, 45, 60, 75, 90, 120, 240, 360 and 480 min after orally administrating QYQRG. The concentrations of rhein and chrysophanol in rabbit plasma were determined by HPLC and main pharmacokinetic parameters were obtained.

RESULTS

The pharmacokinetic parameters AUC(0-∞), T(lag), Cmax and K21 of both rhein and chrysophanol were markedly different in the acute blood stasis model rabbits. It was also found that parameters A, β, MRT and T(1/2β) of rhein and the parameters α and T1/2α of chrysophanol all exhibited significant difference between the normal and acute blood stasis model rabbits.

CONCLUSIONS

The absorption time of rhein and chrysophanol was accelerated and the absorption amount of these two compounds was increased in rabbits with acute blood stasis, suggesting that rhein and chrysophanol would possibly be the two effective compounds in QYQRG.

Sublingual vein extraction algorithm based on hyperspectral tongue imaging technology

Among the parts of the human tongue surface, the sublingual vein is one of the most important ones which may have pathological relationship with some diseases. To analyze this information quantitatively, one primitive work is to extract sublingual veins accurately from tongue body. In this paper, a hyperspectral tongue imaging system instead of a digital camera is used to capture sublingual images. A hidden Markov model approach is presented to extract the sublingual veins from the hyperspectral sublingual images. This approach characterizes the spectral correlation and the band-to-band variability using a hidden Markov process, where the model parameters are estimated by the spectra of the pixel vectors forming the observation sequences. The proposed algorithm, the pixel-based sublingual vein segmentation algorithm, and the spectral angle mapper algorithm are tested on a total of 150 scenes of hyperspectral sublingual veins images to evaluate the performance of the new method. The experimental results demonstrate that the proposed algorithm can extract the sublingual veins more accurately than the traditional algorithms and can perform well even in a noisy environment.

Pharmacokinetic comparisons of hydroxysafflower yellow A in normal and blood stasis syndrome rats

ETHNOPHARMACOLOGICAL RELEVANCE

Safflower is a popular Traditional Chinese Medicine (TCM) to invigorate the blood and dispel 'blood stasis', which arises from poor blood circulation. The differences of pharmacokinetic properties between normal and blood stasis syndrome rats were seldom reported.

AIM OF THE STUDY

The present study was conducted to evaluate the pharmacokinetics of hydroxysafflower yellow A (HSYA) following oral administration of hydroxysafflower yellow A and safflower extract with approximately the same dose of HSYA 100mg/kg in both normal and acute blood stasis rats.

MATERIALS AND METHODS

The animals were orally administered with HYSA monomer and safflower extract. The blood samples were collected according to the time schedule. The concentrations of HSYA in rat plasma were determined by HPLC. Various pharmacokinetic parameters were estimated from the plasma concentration versus time data using non-compartmental methods.

RESULTS

It was found that AUC(0-t), C(max), Vd and CL of HSYA in both HSYA monomer and safflower extract in acute blood stasis rats were with significant difference (P<0.05) comparing with that in normal rats.

CONCLUSIONS

The results indicated that HSYA was with high uptake and eliminated slowly in the animals with blood stasis syndrome, suggesting that the rate and extent of drug metabolism was altered in acute blood stasis animals.

Diagnostic accuracy of pattern differentiation algorithm based on Chinese medicine theory: a stochastic simulation study

Background

Clinical practice of Chinese medicine requires little information for differentiation of Zang-fu patterns. This study is to test the impact of information amount on the diagnostic accuracy of pattern differentiation algorithm (PDA) using stochastic simulation of cases.

Methods

A dataset with 69 Zang-fu single patterns was used with manifestations according to the Four Examinations, namely inspection (Ip), auscultation and olfaction (AO), inquiry (Iq) and palpation (P). A variable quantity of available information (N_%) was randomly sampled to generate 100 true positive and 100 true negative manifestation profiles per pattern to which PDA was applied. Four runs of simulations were used according to the Four Examinations: Ip, Ip+AO, Ip+AO+Iq and Ip+AO+Iq+P. The algorithm performed pattern differentiation by ranking a list of diagnostic hypotheses by the amount of explained information F_%. Accuracy, sensitivity, specificity and negative and positive predictive values were calculated.

Results

Use the Four Examinations resulted in the best accuracy with the smallest cutoff value (N_% = 28.5%), followed by Ip+AO+Iq (33.5%), Ip+AO (51.5%) and Ip (52.0%). All tested combinations provided concave-shaped curves for accuracy, indicating an optimal value subject to N_%-cutoff. Use of N_%-cutoff as a secondary criterion resulted in 94.7% (94.3; 95.1) accuracy, 89.8% (89.1; 90.6) sensitivity, and 99.5% (99.3; 99.7) specificity with the Four Examinations.

Conclusion

Pattern differentiation based on both explained and optimum available information (F_% and N_%-cutoff) is more accurate than using explained and available information without cutoff (F_% and N_%). Both F_% and N_%-cutoff should be used as PDA's objective criteria to perform Zang-fu single pattern differentiation.

Computerized feature quantification of sublingual veins from color sublingual images

Characteristics of tongue pose the most important information for Traditional Chinese Medicine diagnosis. So far, extensive studies have been made on extracting tongue surface features, but rarely refer to sublingual vein that is also diagnostically important. This paper focuses on establishing a feature quantification framework for the inspection of sublingual veins, composed of two parts: the segmentation of sublingual veins and the feature quantification of them. Pixel-based sublingual vein segmentation algorithm and adaptive sublingual vein segmentation algorithm for color sublingual images with visible contrast and low contrast are proposed respectively. The experiments prove that the proposed algorithms perform well on the segmentation of sublingual veins from color sublingual images with both visible contrast and low contrast. A chromatic system in conformity with diagnostic standard of tongue diagnosis is established to characterize the chromatic feature of sublingual veins. Experimental results reveal that the breadth and chromatic features quantified by the proposed framework are properly consistent with the diagnostic standard summarized by tongue diagnosis.