Sublingual vein extraction algorithm based on hyperspectral tongue imaging technology

Li, Qingli; Wang, Yiting; Liu, Hongying; Guan, Yuanyuan; Xu, Liang

doi:10.1016/j.compmedimag.2010.10.001

Cited by 16 publications

(12 citation statements)

References 41 publications

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“…54 In recent studies, different kinds of HSI systems have been developed and used for tongue body segmentation, tongue color analysis and discrimination, tongue cracks extraction and classification, sublingual veins analysis, and malignant changes associated with oral cancer detection. 4,[37][38][39]80,195 These preliminary experiments show that the spectral oral imaging system is superior to the traditional CCD-based methods because it can provide more information about the oral surface. However, further studies are needed on how to extract the quantitative features of the tongue surface and model the relationship between these features and certain diseases.…”

Section: In Vivo Spectral Imagingmentioning

confidence: 87%

Review of spectral imaging technology in biomedical engineering: achievements and challenges

et al. 2013

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Spectral imaging is a technology that integrates conventional imaging and spectroscopy to get both spatial and spectral information from an object. Although this technology was originally developed for remote sensing, it has been extended to the biomedical engineering field as a powerful analytical tool for biological and biomedical research. This review introduces the basics of spectral imaging, imaging methods, current equipment, and recent advances in biomedical applications. The performance and analytical capabilities of spectral imaging systems for biological and biomedical imaging are discussed. In particular, the current achievements and limitations of this technology in biomedical engineering are presented. The benefits and development trends of biomedical spectral imaging are highlighted to provide the reader with an insight into the current technological advances and its potential for biomedical research.

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Section: In Vivo Spectral Imagingmentioning

confidence: 87%

Review of spectral imaging technology in biomedical engineering: achievements and challenges

et al. 2013

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“…Being able to change not only gain K, but also contrast gain k and thresholding value b, as well as the output enhancement wavelength, provides a rather simple 4-parameter method that can be modified in real time during surgery. Compared to other similar methods [11,12,16], our technique allows for vein visualization regardless of the background data, and with no motion artifacts due to capturing Vis and NIR images separately. The method can be deployed to any HSI system, no matter the configuration.…”

Section: Vein Visualization and Enhancementmentioning

confidence: 99%

“…The main focus of these methods is to make spectral anomalies significantly more visible in color reconstructions of MSI/HSI images, obtaining an intuitive, direct visual outlier and anomaly inspection and detection framework. Examples include changing RGB channels from entropy measures of spectral information to enhance contrast in gastrointestinal HSI images [10], or extracting sublingual veins from hyperspectral images [11], as well as vein identification via blending NIR and VisNIR data [12]. However, the most significant references are the results and findings of M. Mitsui et al [13] and N. Hashimoto et al [14], in which abnormal variations across different wavelengths can be enhanced or turned into a different color.…”

Section: Introductionmentioning

confidence: 99%

Context-free hyperspectral image enhancement for wide-field optical biomarker visualization

Pardo

Gutiérrez-Gutiérrez

López‐Higuera

et al. 2019

Biomed. Opt. Express

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Many well-known algorithms for the color enhancement of hyperspectral measurements in biomedical imaging are based on statistical assumptions that vary greatly with respect to the proportions of different pixels that appear in a given image, and thus may thwart their application in a surgical environment. This article attempts to explain why this occurs with SVD-based enhancement methods, and proposes the separation of spectral enhancement from analysis. The resulting method, termed affinity-based color enhancement, or ACE for short, achieves multi-and hyperspectral image coloring and contrast based on current spectral affinity metrics that can physically relate spectral data to a particular biomarker. This produces tunable, real-time results which are analogous to the current state-of-the-art algorithms, without suffering any of their inherent context-dependent limitations. Two applications of this method are shown as application examples: vein contrast enhancement and high-precision chromophore concentration estimation.

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“…Recently, with the progress in image processing and pattern recognition, computational tongue diagnosis has received considerable interests to make tongue diagnosis more objective and automated. Many tongue diagnosis systems have been developed, as hyperspectral-based tongue diagnosis system [4], 3D tongue acquisition system [5], and so forth [6][7][8][9][10]. Different features of tongue, for example, shape [11], color [8,12,13], texture, coating [14,15], and sublingual vein [16], have been suggested for the representation of tongue images.…”

Section: Introductionmentioning

confidence: 99%

Weighted Nuclear Norm Minimization Based Tongue Specular Reflection Removal

Cui

Zhang

Wang

et al. 2015

Mathematical Problems in Engineering

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In computational tongue diagnosis, specular reflection is generally inevitable in tongue image acquisition, which has adverse impact on the feature extraction and tends to degrade the diagnosis performance. In this paper, we proposed a two-stage (i.e., the detection and inpainting pipeline) approach to address this issue: (i) by considering both highlight reflection and subreflection areas, a superpixel-based segmentation method was adopted for the detection of the specular reflection areas; (ii) by extending the weighted nuclear norm minimization (WNNM) model, a nonlocal inpainting method is proposed for specular reflection removal. Experimental results on synthetic and real images show that the proposed method is accurate in detecting the specular reflection areas and is effective in restoring tongue image with more natural texture information of tongue body.

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Sublingual vein extraction algorithm based on hyperspectral tongue imaging technology

Cited by 16 publications

References 41 publications

Review of spectral imaging technology in biomedical engineering: achievements and challenges

Review of spectral imaging technology in biomedical engineering: achievements and challenges

Context-free hyperspectral image enhancement for wide-field optical biomarker visualization

Weighted Nuclear Norm Minimization Based Tongue Specular Reflection Removal

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