Computational analysis of tongue image for health diagnosis

Tongue, the primary taste organ in the mouth, can reflect the whole body's health conditions based on the Traditional Chinese Medical (TCM) theories. Watching the tongue is one of the most common, essential and reliable methods for the TCM doctor to make diagnoses. In this thesis, a new health system is introduced based on tongue image analysis. The technologies adopted in this system ranged from tongue image processing algorithms to machine learning applications. The tongue image algorithms used in this work include image segmentation, tongue recognition and tongue image classification. Image segmentation was used to get rid of other unrelated parts, such as lip, face and neck, while keeping the tongue only. Then two recognition methods were applied to check whether the segmented result is a tongue or not. For different tongue patterns, the Support Vector Machine is applied to train a machine learning model and make predictions to classify the tongue into different labeled groups. An app named 'iTongue' is designed to monitor the body status by taking and processing tongue images in smart phones. The app provides a user-friendly, fast and powerful health tool based on TCM theories. The whole system is implemented in a webbased environment. An advanced portal was developed to connect the users and the TCM doctors. The users will not only obtain the analysis label of tongue images, but also get some life style recommendations based on the tongue image analysis. This portal helps the user understand more about his body status and guide him to adopt a more suitable diet and improve exercise

Tongue Image Analysis for Diabetes Mellitus Diagnosis Based on SOM Kohonen

Tongue diagnosis is an important diagnostic method for evaluating the condition of internal organ by looking at the image of tongue . However, due to its qualitative, subjective and experience-based nature, traditional tongue diagnosis has a very limited application in clinical medicine. Moreover, traditional tongue diagnosis is always concerned with the identification of syndromes rather than with the connection between tongue abnormal appearances and diseases. This is not well understood in Western medicine, thus greatly obstruct its wider use in the world. In this paper, we present a novel computerized tongue inspection method aiming to address these problems. First, two kinds of quantitative features, chromatic and textural measures, are extracted from tongue images by using popular digital image processing techniques. Then, SOM Kohonen are employed to model the relationship between these quantitative features and diseases. The effectiveness of the method is tested on 35 patients affected by Diabetes Mellitus as well as other 30 healthy volunteers, and the diagnostic results predicted by the previously trained SOM Kohonen classifiers are compared with the HOMA-B

Prognostic significance of serine-phosphorylated STAT3 expression in pT1-T2 oral tongue carcinoma

Objectives. Phosphorylated (activated) STAT3 (pSTAT3) is a regulator of numerous genes that play an essential part in the onset, development and progression of cancer; it is involved in cell proliferation and preventing apoptosis, and in invasion, angiogenesis, and the evasion of immune surveillance. This study aimed mainly to investigate the potential prognostic role of pSTAT3 expression in oral tongue squamous cell carcinoma (SCC). Methods. Phospho-ser727 STAT3 immunolabeling was correlated with prognostic parameters in 34 consecutive cases of pT1\u2013T2 tongue SCCs undergoing primary surgery. Computer-based image analysis was used for the immunohistochemical reactions analysis. Results. Statistical analysis showed a difference in disease-free survival (DFS) when patients were stratified by pN status (P=0.031). Most tumors had variable degrees (mean\ub1SD, 80.7%\ub123.8%) of intense nuclear immunoreaction to pSTAT3. Our findings rule out any significant association of serine-phosphorylated nuclear STAT3 expression with tumor stage, grade, lymph node metastasis, recurrence rate, or DFS. Conclusion. In spite of these results, it is worth further investigating the role of pSTAT3 (serine-and tyrosine-pSTAT3) in oral tongue SCC in larger series because preclinical models are increasingly showing that several anticancer strategies would benefit from STAT3 phosphorylation inhibition

Computer-aided tongue image diagnosis and analysis

Title from PDF of title page (University of Missouri--Columbia, viewed on May 14, 2013).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Dissertation advisor: Dr. Ye DuanIncludes bibliographical references.Vita.Ph. D. University of Missouri--Columbia 2012."May 2012"This work focuses on computer-aided tongue image analysis, specifically, as it relates to Traditional Chinese Medicine (TCM). Computerized tongue diagnosis aid medical practitioners capture quantitative features to improve reliability and consistence of diagnosis. A total computer-aided tongue analysis framework consists of tongue detection, tongue segmentation, tongue feature extraction, tongue classification and analysis, which are all included in our work. We propose a new hybrid image segmentation algorithm that integrates the region-based method with the boundary-based method. We apply this segmentation algorithm in designing an automatic tongue detection and segmentation framework. We also develop a novel color space based feature set for tongue feature extraction to implement an automated ZHENG (TCM syndrome) classification system using machine learning techniques. To further enhance the performance of our classification system, we propose preprocessing the tongue images using the Modified Specular-free technique prior to feature extraction, and explore the extraction of geometry features from the Petechia. Lastly, we propose a new feature set for automated tongue shape classification.Includes bibliographical reference

Tongue Tumor Detection in Medical Hyperspectral Images

A hyperspectral imaging system to measure and analyze the reflectance spectra of the human tongue with high spatial resolution is proposed for tongue tumor detection. To achieve fast and accurate performance for detecting tongue tumors, reflectance data were collected using spectral acousto-optic tunable filters and a spectral adapter, and sparse representation was used for the data analysis algorithm. Based on the tumor image database, a recognition rate of 96.5% was achieved. The experimental results show that hyperspectral imaging for tongue tumor diagnosis, together with the spectroscopic classification method provide a new approach for the noninvasive computer-aided diagnosis of tongue tumors

Prototyping Digital Tongue Diagnosis System on Raspberry Pi

Tongue inspection is a complementary diagnosis method that widely used in Traditional Chinese Medicine (TCM) by inspecting the tongue body constitution to decide the physiological and pathological functions of the human body. Since tongue manifestation is done by practitioner’s observation using naked eye, many limitations can affect the diagnosis result including environment condition and experiences of the practitioner. Lately, tongue diagnosis has been widely studied in order to solve these limitations via digital system. However, most of recent digital system are bulky and not equipped with intelligent diagnosis system that can finally predict the health status of the patient. In this research, digital tongue diagnosis system that uses intelligent diagnosis consisted of image segmentation analysis, tongue coating recognition analysis, and tongue color classification has been embedded on Raspberry Pi. Tongue segmentation implements Hue, Saturation and Value (HSV) color space with Brightness Conformable Multiplier (BCM) for adaptive brightness filtering to recognized tongue body accurately while eliminating perioral area.  Tongue Coating Recognition uses threshold method to detect tongue coating and eliminate the unwanted features including shadow. Tongue color classification uses hybrid method consisted of k-means clustering and Support Vector Machine (SVM) to classify between red, light red and deep red tongue and further gave diagnosis based on color. This experiment concluded that it is feasible to embed the algorithm on Raspberry Pi to promote system portability while attaining similar accuracy for future telemedicine

Purple-bluish tongue is associated with platelet counts, and the recurrence of epithelial ovarian cancer

AbstractObjectiveTo evaluate the relationship between purple-bluish tongue and platelet counts, and further to examine their associations with the recurrence of epithelial ovarian cancer.MethodsA total of 82 epithelial ovarian cancer patients were enrolled in this study. Cluster analysis was used for grouping patients’ Prgb (Red-R; Green-G; Blue-B; Average percentage of RGB, Prgb) values. Receiver operating characteristic (ROC) curve was performed for detecting the diagnostic standard of purple-bluish tongue. χ2 test was used to assess the relationship between purple-bluish tongue and platelet counts, and the recurrence of epithelial ovarian cancer. The perioperative (preoperative) platelet level was examinedwith tongue image and disease recurrence.ResultsTongue images were classified into two groups basing on Prgb values of images by cluster analysis. The numbers of cases in cluster “1” (normal color tongue) was 16 and cluster “2” (purple-bluish tongue) was 66. Two groups of Prgb values, classified by cluster analysis, were significantly correlated with vision-based tongue color recognition (Kappa = 0.852, P < 0.001). ROC curve showed that the ratio of Pb to Pr had the highest diagnostic value. The sensitivity and the specificity of the ratio of Pb to Pr were 95.3% and 88.9% respectively and the optimal cut-off point was 0.71. Purple-bluish tongue was significantly correlated with increased platelet counts (P < 0.001). Both the increased platelet counts (P = 0.01) and purple-bluish tongue were associated with recurrence of epithelial ovarian cancer (P < 0.001).ConclusionThe ratio of Pb to Pr greater than 0.71 could serve as an indicator for purple-bluish tongue diagnosing used in symptom pattern identification in Traditional Chinese Medicine. Purple-bluish tongue, associated with increased platelet counts, was also closely correlated with the recurrence of epithelial ovarian cancer