| dc.description.abstract | Tongue diagnosis plays an invaluable role in Traditional Chinese Medicine (TCM) and is one of the major methods of diagnosis. It is performed by visual inspection of the tongue and its features, including vitality, shape, color, coating, and moisture. These pieces of information alone or mapping with the topographic regions of the tongue help a TCM doctor to identify and locate the disease in a patient’s body. However, tongue diagnosis is achieved based on the knowledge and experiences of a doctor, and the lack of objective evaluation standards may hinder the application and validity of tongue diagnosis. With the improvement of computing capability and the development of image processing technology, diagnostic equipment that automatically quantifies various aspects of tongues has been developed and used in hospitals. Under the foundation from these technical benefits, we developed a method of tongue image analysis based on artificial intelligence algorithms that allow easy monitoring and recognizing the morphological features of the targeted tongue without time and space limitations. However, in order to achieve these tasks, the contemporary AI algorithms suffer the predicaments, such as the disturbances of tongue position, light damage and/or pollution, and color correction, and so on. To overcome these circumstances, we redesign the conventional method of the tongue image acquisition by introducing the aspect ratio of the tongue area and length of lip into the method. The APP on the mobile phone will help users acquire correct tongue photos by calculating the aspect ratio. Then the Retinex algorithm will be employed to correct the effect of light, including the direction of the light source and the intensity of each light vector. Thus, the intensity of the RGB pixels on the photo would be modified by merging the light information from the directions and the intensities of the light vectors. On the clinical investigations, a large set of tongue photos will be collected from the TCM clinic of Landseed International Hospital to establish a cloud database, and relevant participants and experts will help label the images for the supervised machine learning algorithm. Eventually, the convolutional data density functional theory will be adopted for the feature extraction and image segmentation. The desired outcome of this study is to provide an easy methodology to connect the tongue morphologies and the TCM applications. | en_US |