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

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

ZHENG Classification In Traditional Chinese Medicine Based On Modified Specular-free Tongue Images

Traditional Chinese Medicine practitioners usually observe the color and coating of a patient\u27s tongue to determine ZHENG (such as Cold or Hot ZHENG) and to diagnose different stomach disorders including gastritis. In our previous work, we explored new modalities for clinical characterization of ZHENG in gastritis patients via tongue image analysis using various supervised machine-learning algorithms. We proposed a system that learns from the clinical practitioner\u27s subjective data how to classify a patients health status by extracting meaningful features from tongue images based on color-space models. In this paper, we propose an enhancement to the ZHENG classification system: a coating separation technique using the MSF images such that feature extraction is applied only to the coated region on the tongue surface. The results obtained over a set of 263 gastritis patients (most of whom are either Cold Zheng or Hot ZHENG), and a control group of 48 healthy volunteers demonstrate an improved performance for most of the classification types considered. © 2012 IEEE

Automated Tongue Feature Extraction for ZHENG Classification in Traditional Chinese Medicine

ZHENG, Traditional Chinese Medicine syndrome, is an integral and essential part of Traditional Chinese Medicine theory. It defines the theoretical abstraction of the symptom profiles of individual patients and thus, used as a guideline in disease classification in Chinese medicine. For example, patients suffering from gastritis may be classified as Cold or Hot ZHENG, whereas patients with different diseases may be classified under the same ZHENG. Tongue appearance is a valuable diagnostic tool for determining ZHENG in patients. In this paper, we explore new modalities for the clinical characterization of ZHENG using various supervised machine learning algorithms. We propose a novel-color-space-based feature set, which can be extracted from tongue images of clinical patients to build an automated ZHENG classification system. Given that Chinese medical practitioners usually observe the tongue color and coating to determine a ZHENG type and to diagnose different stomach disorders including gastritis, we propose using machine-learning techniques to establish the relationship between the tongue image features and ZHENG by learning through examples. The experimental results obtained over a set of 263 gastritis patients, most of whom suffering Cold Zheng or Hot ZHENG, and a control group of 48 healthy volunteers demonstrate an excellent performance of our proposed system

Tongue Image Analysis And Its Mobile App Development For Health Diagnosis

Computer-aided diagnosis provides a medical procedure that assists physicians in interpretation of medical images. This work focuses on computer-aided tongue image analysis specifically, based on Traditional Chinese Medicine (TCM). Tongue diagnosis is an important component of TCM. Computerized tongue diagnosis can aid medical practitioners in capturing quantitative features to improve reliability and consistency of diagnosis. Recently, researchers have started to develop computer-aided tongue analysis algorithms based on new advancement in digital photogrammetry, image analysis, and pattern recognition technologies. In this chapter, we will describe our recent work on tongue image analysis as well as a mobile app that we developed based on this technology

An automatic tongue detection and segmentation framework for computer-aided tongue image analysis,”

Abstract-Traditional Chinese Medicine (TCM) has a long history and has been recognized as a popular alternative medicine in western countries. Tongue diagnosis is a significant procedure in computer-aided TCM, where tongue image analysis plays a dominant role. In this paper, we proposed a fully automatic tongue detection and tongue segmentation framework, which is an essential step in computer-aided tongue image analysis. Comparing with other existing methods, our method is fully automatic without any need of adjusting parameters for different images and do not need any initialization. Index Terms-Tongue analysis, TCM, computer-aide diagnosis