Hyperspectral Tongue Imaging System Used in Tongue Diagnosis

Li, Qingli; Liu, Jingao; Xiao, Gonghai; Xue, Yong

doi:10.1109/icbbe.2008.978

Cited by 9 publications

(5 citation statements)

References 7 publications

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“…A 3CCD camera and hyperspectral imaging are specialized for color image acquisition, and they have thus been adopted in TDSs. Li et al 31 and Yamamoto et al 3 developed hyperspectral imaging TDSs, but they did not use the ColorChecker for color correction. Because the hyperspectral imaging systems are capable of acquiring both spatial and spectral information, the color correction can be performed by normalization procedure in the spectral domain.…”

Section: Discussionmentioning

confidence: 99%

“… First author (y) ref. Advantages Disadvantages Cai (2002) 2 Using a ColorChecker for color correction Low image resolution (640 × 480) No illuminator Yamamoto (2011) 3 Hyperspectral imaging for the tongue image acquisition Using an artificial sunlight lamp as a light source Adopted multiple scattering reflection technique to the light source in order to reduce specular reflection Using a semi-closed box to avoid other unexpected light Low image resolution (640 × 480) No color correction method Jeon (2008) 6 A diffusing acrylic with the LED lighting to reduce specular reflection A ColorChecker for color correction High image resolution with a long focal length Using a fully closed box to avoid other unexpected light Low color temperature of the light source Wang (2005) 8 Using several consistent color patches for color calibration to keep the consistency of the colors transmitted from camera to monitor Using a fully closed box Jiang (2008) 9 High-quality digital camera with a 7.2 megapixel resolution A ColorChecker for color correction No illuminator Zhang (2005) 10 Using an optical fiber as a waveguide to compensate for the high heat emission of the light source Potable face contact region No color correction method Meiling (2008) 11 Portable type system Low color temperature of the light source Li (2008) 31 Hyperspectral imaging for the tongue image acquisition Low image resolution (652 × 620) No color correction method Liu (2011) 34 3D image system for 3D tongue modeling High image resolution No color correction method …”

Section: Methodsmentioning

confidence: 99%

“…This technique of dividing images into bands can be extended beyond the visible spectrum. Li et al 31 developed a hyperspectral imaging system to capture tongue images with a spectrum range of 400 nm – 800 nm and a resolution of less than 5 nm. The number of efficient pixels is 652 pixels × 620 pixels including 120 bands.…”

Section: Methodsmentioning

confidence: 99%

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Review on the current trends in tongue diagnosis systems

Jung

Jeon

Kim

et al. 2012

Integrative Medicine Research

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Tongue diagnosis is an essential process to noninvasively assess the condition of a patient's internal organs in traditional medicine. To obtain quantitative and objective diagnostic results, image acquisition and analysis devices called tongue diagnosis systems (TDSs) are required. These systems consist of hardware including cameras, light sources, and a ColorChecker, and software for color correction, segmentation of tongue region, and tongue classification. To improve the performance of TDSs, various types TDSs have been developed. Hyperspectral imaging TDSs have been suggested to acquire more information than a two-dimensional (2D) image with visible light waves, as it allows collection of data from multiple bands. Three-dimensional (3D) imaging TDSs have been suggested to provide 3D geometry. In the near future, mobile devices like the smart phone will offer applications for assessment of health condition using tongue images. Various technologies for the TDS have respective unique advantages and specificities according to the application and diagnostic environment, but this variation may cause inconsistent diagnoses in practical clinical applications. In this manuscript, we reviewed the current trends in TDSs for the standardization of systems. In conclusion, the standardization of TDSs can supply the general public and oriental medical doctors with convenient, prompt, and accurate information with diagnostic results for assessing the health condition.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Review on the current trends in tongue diagnosis systems

Jung

Jeon

Kim

et al. 2012

Integrative Medicine Research

View full text Add to dashboard Cite

show abstract

“…Automation of tongue analysis system essentially requires an image capturing device with high resolution images for accurate extraction of tongue features for disease predictions in agreement with the traditional medicine practitioners. Different imaging setups are explored using high end CCD cameras [3][4][5][6][7][8][9], Hyperspectral cameras [10][11][12][13][14][15][16] to smartphone cameras [17][18][19][20][21][22][23][24]. Tongue region needs to be segmented from the images captured which consist of teeth lips skin area before feature extraction and classification.…”

Section: Related Workmentioning

confidence: 99%

Convolution Neural Network Based Multi- Label Disease Detection using Tongue Images

Bhatnagar,

Bansod

2024

Preprint

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Purpose: Tongue image analysis for disease diagnosis is an ancient traditional non-invasive diagnosis technique widely used by traditional medicine practitioners. Deep Learning based multi-label disease detection models offer tremendous potential to clinical decision support systems, by facilitating preliminary diagnosis. Methods: In this work, we propose a multi-label disease detection pipeline, where in observation and analysis of tongue images captured and received via smartphones assist in predicting the health status of an individual. All images are voluntarily given by subjects consulting collaborating physicians. Images thus acquired are first and foremost classified either into a diseased or a normal category by a 5-fold cross-validation algorithm using a convolution neural network (MobileNetV2) model for binary classification. Once the diseased label is predicted, the image is used to diagnose multiple diseases using the prediction algorithm based on DenseNet121. Results: Average accuracy of 93 % was achieved in classifying diseased from normal healthy tongue by detection model with MobileNetV2 architecture. Multilabel disease classification produced more than 90% accurate results for the seven class labels considered. Conclusion: AI based image analysis shows promising results and an extensive dataset could provide further improvements to this approach. Rather than employing high-cost sophisticated image capturing setup, experimenting with smartphone images opens opportunity to provide preliminary health status to individuals on smartphone prior to further line of treatment and diagnosis.

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“…Artificially, this can be generated with a source with a colour rendering index greater than ninety and a colour temperature around 5000 K. The automation of tongue analysis systems essentially requires an image-capturing device with high-resolution images for accurate extraction of tongue features for disease predictions, in agreement with traditional medicine practitioners. Researchers explore various imaging setups using high-end CCD cameras [3][4][5][6][7][8][9], hyperspectral cameras [10][11][12][13][14][15][16], and smartphone cameras [17][18][19][20][21][22][23][24]. Before feature extraction and classification, it is necessary to segment the tongue region from the captured images, which include teeth, lips, and skin areas.…”

Section: Related Workmentioning

confidence: 99%

Convolution Neural Network Based Multi-Label Disease Detection Using Smartphone Captured Tongue Images

Bhatnagar,

Bansod

2024

Applied Sciences

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Purpose: Tongue image analysis for disease diagnosis is an ancient, traditional, non-invasive diagnostic technique widely used by traditional medicine practitioners. Deep learning-based multi-label disease detection models have tremendous potential for clinical decision support systems because they facilitate preliminary diagnosis. Methods: In this work, we propose a multi-label disease detection pipeline where observation and analysis of tongue images captured and received via smartphones assist in predicting the health status of an individual. Subjects, who consult collaborating physicians, voluntarily provide all images. Images thus acquired are first and foremost classified either into a diseased or a normal category by a 5-fold cross-validation algorithm using a convolutional neural network (MobileNetV2) model for binary classification. Once it predicts the diseased label, the disease prediction algorithm based on DenseNet-121 uses the image to diagnose single or multiple disease labels. Results: The MobileNetV2 architecture-based disease detection model achieved an average accuracy of 93% in distinguishing between diseased and normal, healthy tongues, whereas the multilabel disease classification model produced more than 90% accurate results for the disease class labels considered, strongly indicating a successful outcome with the smartphone-captured image dataset. Conclusion: AI-based image analysis shows promising results, and an extensive dataset could provide further improvements to this approach. Experimenting with smartphone images opens a great opportunity to provide preliminary health status to individuals at remote locations as well, prior to further treatment and diagnosis, using the concept of telemedicine.

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Hyperspectral Tongue Imaging System Used in Tongue Diagnosis

Cited by 9 publications

References 7 publications

Review on the current trends in tongue diagnosis systems

Review on the current trends in tongue diagnosis systems

Convolution Neural Network Based Multi- Label Disease Detection using Tongue Images

Convolution Neural Network Based Multi-Label Disease Detection Using Smartphone Captured Tongue Images

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