TISNet-Enhanced Fully Convolutional Network with Encoder-Decoder Structure for Tongue Image Segmentation in Traditional Chinese Medicine
المؤلفون المشاركون
Huang, Xiaodong
Zhang, Hui
Zhuo, Li
Li, Xiaoguang
Zhang, Jing
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-08-07
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
Extracting the tongue body accurately from a digital tongue image is a challenge for automated tongue diagnoses, as the blurred edge of the tongue body, interference of pathological details, and the huge difference in the size and shape of the tongue.
In this study, an automated tongue image segmentation method using enhanced fully convolutional network with encoder-decoder structure was presented.
In the frame of the proposed network, the deep residual network was adopted as an encoder to obtain dense feature maps, and a Receptive Field Block was assembled behind the encoder.
Receptive Field Block can capture adequate global contextual prior because of its structure of the multibranch convolution layers with varying kernels.
Moreover, the Feature Pyramid Network was used as a decoder to fuse multiscale feature maps for gathering sufficient positional information to recover the clear contour of the tongue body.
The quantitative evaluation of the segmentation results of 300 tongue images from the SIPL-tongue dataset showed that the average Hausdorff Distance, average Symmetric Mean Absolute Surface Distance, average Dice Similarity Coefficient, average precision, average sensitivity, and average specificity were 11.2963, 3.4737, 97.26%, 95.66%, 98.97%, and 98.68%, respectively.
The proposed method achieved the best performance compared with the other four deep-learning-based segmentation methods (including SegNet, FCN, PSPNet, and DeepLab v3+).
There were also similar results on the HIT-tongue dataset.
The experimental results demonstrated that the proposed method can achieve accurate tongue image segmentation and meet the practical requirements of automated tongue diagnoses.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Huang, Xiaodong& Zhang, Hui& Zhuo, Li& Li, Xiaoguang& Zhang, Jing. 2020. TISNet-Enhanced Fully Convolutional Network with Encoder-Decoder Structure for Tongue Image Segmentation in Traditional Chinese Medicine. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1139502
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Huang, Xiaodong…[et al.]. TISNet-Enhanced Fully Convolutional Network with Encoder-Decoder Structure for Tongue Image Segmentation in Traditional Chinese Medicine. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1139502
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Huang, Xiaodong& Zhang, Hui& Zhuo, Li& Li, Xiaoguang& Zhang, Jing. TISNet-Enhanced Fully Convolutional Network with Encoder-Decoder Structure for Tongue Image Segmentation in Traditional Chinese Medicine. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1139502
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1139502
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر