Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 2 de 2
Filtrar
Mais filtros

Base de dados
País/Região como assunto
Ano de publicação
Tipo de documento
País de afiliação
Intervalo de ano de publicação
1.
Comput Methods Programs Biomed ; 174: 51-64, 2019 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-29307471

RESUMO

Tongue features are important objective basis for clinical diagnosis and treatment in both western medicine and Chinese medicine. The need for continuous monitoring of health conditions inspires us to develop an automatic tongue diagnosis system based on built-in sensors of smartphones. However, tongue images taken by smartphone are quite different in color due to various lighting conditions, and it consequently affects the diagnosis especially when we use the appearance of tongue fur to infer health conditions. In this paper, we captured paired tongue images with and without flash, and the color difference between the paired images is used to estimate the lighting condition based on the Support Vector Machine (SVM). The color correction matrices for three kinds of common lights (i.e., fluorescent, halogen and incandescent) are pre-trained by using a ColorChecker-based method, and the corresponding pre-trained matrix for the estimated lighting is then applied to eliminate the effect of color distortion. We further use tongue fur detection as an example to discuss the effect of different model parameters and ColorCheckers for training the tongue color correction matrix under different lighting conditions. Finally, in order to demonstrate the potential use of our proposed system, we recruited 246 patients over a period of 2.5 years from a local hospital in Taiwan and examined the correlations between the captured tongue features and alanine aminotransferase (ALT)/aspartate aminotransferase (AST), which are important bio-markers for liver diseases. We found that some tongue features have strong correlation with AST or ALT, which suggests the possible use of these tongue features captured on a smartphone to provide an early warning of liver diseases.


Assuntos
Processamento de Imagem Assistida por Computador/métodos , Medicina Tradicional Chinesa/métodos , Smartphone , Máquina de Vetores de Suporte , Língua/fisiopatologia , Algoritmos , Cor , Diagnóstico por Computador/métodos , Desenho de Equipamento , Humanos , Iluminação , Hepatopatias/diagnóstico , Hepatopatias/fisiopatologia , Taiwan , Temperatura
2.
J Med Syst ; 40(1): 18, 2016 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-26525056

RESUMO

BACKGROUND: An automatic tongue diagnosis framework is proposed to analyze tongue images taken by smartphones. Different from conventional tongue diagnosis systems, our input tongue images are usually in low resolution and taken under unknown lighting conditions. Consequently, existing tongue diagnosis methods cannot be directly applied to give accurate results. MATERIALS AND METHODS: We use the SVM (support vector machine) to predict the lighting condition and the corresponding color correction matrix according to the color difference of images taken with and without flash. We also modify the state-of-the-art work of fur and fissure detection for tongue images by taking hue information into consideration and adding a denoising step. RESULTS: Our method is able to correct the color of tongue images under different lighting conditions (e.g. fluorescent, incandescent, and halogen illuminant) and provide a better accuracy in tongue features detection with less processing complexity than the prior work. CONCLUSIONS: In this work, we proposed an automatic tongue diagnosis framework which can be applied to smartphones. Unlike the prior work which can only work in a controlled environment, our system can adapt to different lighting conditions by employing a novel color correction parameter estimation scheme.


Assuntos
Cor , Aumento da Imagem/instrumentação , Medicina Tradicional Chinesa/instrumentação , Smartphone , Máquina de Vetores de Suporte , Língua/fisiopatologia , Humanos , Iluminação , Análise de Regressão
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA