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.

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.

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