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 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

Visualization Techniques for Tongue Analysis in Traditional Chinese Medicine

Visual inspection of the tongue has been an important diagnostic method of Traditional Chinese Medicine (TCM). Clinic data have shown significant connections between various viscera cancers and abnormalities in the tongue and the tongue coating. Visual inspection of the tongue is simple and inexpensive, but the current practice in TCM is mainly experience-based and the quality of the visual inspection varies between individuals. The computerized inspection method provides quantitative models to evaluate color, texture and surface features on the tongue. In this paper, we investigate visualization techniques and processes to allow interactive data analysis with the aim to merge computerized measurements with human expert's diagnostic variables based on five-scale diagnostic conditions: Healthy (H), History Cancers (HC), History of Polyps (HP), Polyps (P) and Colon Cancer (C)

A novel computerized test for detecting and monitoring visual attentional deficits and delirium in the ICU

Objectives: Delirium in the ICU is associated with poor outcomes but is under-detected. Here we evaluated performance of a novel, graded test for objectively detecting inattention in delirium, implemented on a custom-built computerized device (Edinburgh Delirium Test Box–ICU). Design: A pilot study was conducted, followed by a prospective case-control study. Setting: Royal Infirmary of Edinburgh General ICU. Patients: A pilot study was conducted in an opportunistic sample of 20 patients. This was followed by a validation study in 30 selected patients with and without delirium (median age, 63 yr; range, 23–84) who were assessed with the Edinburgh Delirium Test Box–ICU on up to 5 separate days. Presence of delirium was assessed using the Confusion Assessment Method for the ICU. Measurements and Main Results: The Edinburgh Delirium Test Box–ICU involves a behavioral assessment and a computerized test of attention, requiring patients to count slowly presented lights. Thirty patients were assessed a total of 79 times (n = 31, 23, 15, 8, and 2 for subsequent assessments; 38% delirious). Edinburgh Delirium Test Box–ICU scores (range, 0–11) were lower for patients with delirium than those without at the first (median, 0 vs 9.5), second (median, 3.5 vs 9), and third (median, 0 vs 10.5) assessments (all p < 0.001). An Edinburgh Delirium Test Box–ICU score less than or equal to 5 was 100% sensitive and 92% specific to delirium across assessments. Longitudinally, participants’ Edinburgh Delirium Test Box–ICU performance was associated with delirium status. Conclusions: These findings suggest that the Edinburgh Delirium Test Box–ICU has diagnostic utility in detecting ICU delirium in patients with Richmond Agitation and Sedation Scale Score greater than –3. The Edinburgh Delirium Test Box–ICU has potential additional value in longitudinally tracking attentional deficits because it provides a range of scores and is sensitive to change

Computerized tongue diagnosis based on Bayesian networks

2004-2005 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Features For Automated Tongue Image Shape Classification

Inspection of the tongue is a key component in Traditional Chinese Medicine. Chinese medical practitioners diagnose the health status of a patient based on observation of the color, shape, and texture characteristics of the tongue. The condition of the tongue can objectively reflect the presence of certain diseases and aid in the differentiation of syndromes, prognosis of disease and establishment of treatment methods. Tongue shape is a very important feature in tongue diagnosis. A different tongue shape other than ellipse could indicate presence of certain pathologies. In this paper, we propose a novel set of features, based on shape geometry and polynomial equations, for automated recognition and classification of the shape of a tongue image using supervised machine learning techniques. We also present a novel method to correct the orientation/deflection of the tongue based on the symmetry of axis detection method. Experimental results obtained on a set of 303 tongue images demonstrate that the proposed method improves the current state of the art method. © 2012 IEEE