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Automated Tongue Feature Extraction for ZHENG Classification in Traditional Chinese Medicine.

Kanawong R, Obafemi-Ajayi T, Ma T, Xu D, Li S, Duan Y - Evid Based Complement Alternat Med (2012)

Bottom Line: It defines the theoretical abstraction of the symptom profiles of individual patients and thus, used as a guideline in disease classification in Chinese medicine.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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Informatics Institute, University of Missouri, Columbia, MO 65211, USA.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Tongue images of patients with different ZHENG classification. “Normal” represents a healthy person.
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fig2: Tongue images of patients with different ZHENG classification. “Normal” represents a healthy person.

Mentions: The coating on the tongue is discriminated by not only its presence but also its color. The color could be yellow, white, and other colors. However, the color in image is not the exact true color of the tongue. To properly identify the color of the tongue coating, we applied the specular component technique presented in our prior work on tongue detection and analysis [2]. Figure 2 illustrates different tongue images of patients and their corresponding ZHENG class.


Automated Tongue Feature Extraction for ZHENG Classification in Traditional Chinese Medicine.

Kanawong R, Obafemi-Ajayi T, Ma T, Xu D, Li S, Duan Y - Evid Based Complement Alternat Med (2012)

Tongue images of patients with different ZHENG classification. “Normal” represents a healthy person.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3369473&req=5

fig2: Tongue images of patients with different ZHENG classification. “Normal” represents a healthy person.
Mentions: The coating on the tongue is discriminated by not only its presence but also its color. The color could be yellow, white, and other colors. However, the color in image is not the exact true color of the tongue. To properly identify the color of the tongue coating, we applied the specular component technique presented in our prior work on tongue detection and analysis [2]. Figure 2 illustrates different tongue images of patients and their corresponding ZHENG class.

Bottom Line: It defines the theoretical abstraction of the symptom profiles of individual patients and thus, used as a guideline in disease classification in Chinese medicine.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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Informatics Institute, University of Missouri, Columbia, MO 65211, USA.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus