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

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

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

Predicting and auralizing acoustics in classrooms

Although classrooms have fairly simple geometries, this type of room is known to cause problems when trying to predict their acoustics using room acoustics computer modeling. Some typical features from a room acoustics point of view are: Parallel walls, low ceilings (the rooms are flat), uneven distribution of absorption, and most of the floor being covered with furniture which at long distances act as scattering elements, and at short distance provide strong specular components. The importance of diffraction and scattering is illustrated in numbers and by means of auralization, using ODEON 8 Beta