Computer Aided Tongue Diagnosis System

Zhang, H.Z.; Wang, K.Q.; Zhang, D.; Pang, Bo; Huang, Bo

doi:10.1109/iembs.2005.1616055

Cited by 56 publications

(48 citation statements)

References 4 publications

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“…Generally, the proposed method can well address the edge enhancement and the contour discontinuity problems. Experimental results also showed that the proposed method is more effective than other tongue body segmentation methods, i.e., BEDC [35], Watershed [36], and PolarSnake [32].…”

Section: Resultsmentioning

confidence: 68%

“…We further compared the proposed GaborFM method with three other tongue body segmentation methods: BEDC [35], Watershed [36], and PolarSnake [32]. Figure 10 shows the segmentation results of three typical tongue images obtained using the four segmentation methods: BEDC [35], Watershed [36], PolarSnake [32], and GaborFM.…”

Section: Comparison With the Other Segmentation Methodsmentioning

confidence: 99%

“…Figure 10 shows the segmentation results of three typical tongue images obtained using the four segmentation methods: BEDC [35], Watershed [36], PolarSnake [32], and GaborFM. In BEDC [35], the bi-elliptical deformable template was adopted for contour initialization.…”

Section: Comparison With the Other Segmentation Methodsmentioning

confidence: 99%

“…Watershed [36] adopted the region merging strategy for contour initialization, and this usually led to a nonsmooth contour, and over-and under-segmentation, as shown in Figure 10d,f. The PolarSnake [32] was designed for detecting dark lines, and location bias occurred for the detection of boundary pixels such as Points 3 and 4 in Figure 2. The location bias of the PolarSnake can be observed in Figure 10g,i.…”

Section: Comparison With the Other Segmentation Methodsmentioning

confidence: 99%

“…A number of feature extraction methods have been proposed for extracting features of tongue color [16,17], coating, and texture [18][19][20][21], fissures [22][23][24][25], shape [26,27], tooth marks [28], petechia spots [29], and the sublingual vein [30,31]. Pattern recognition approaches such as the Bayesian network and support vector machine have been used for syndromes and disease analysis [9,[32][33][34].…”

Section: Introductionmentioning

confidence: 99%

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Fast marching over the 2D Gabor magnitude domain for tongue body segmentation

Cui

Zhang

et al. 2013

EURASIP J. Adv. Signal Process.

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Tongue body segmentation is a prerequisite to tongue image analysis and has recently received considerable attention. The existing tongue body segmentation methods usually involve two key steps: edge detection and active contour model (ACM)-based segmentation. However, conventional edge detectors cannot faithfully detect the contour of the tongue body, and the initialization of ACM suffers from the edge discontinuity problem. To address these issues, we proposed a novel tongue body segmentation method, GaborFM, which initializes ACM by performing fast marching over the two-dimensional (2D) Gabor magnitude domain of the tongue images. For the enhancement of the contour of the tongue body, we used the 2D Gabor magnitude-based detector. To cope with the edge discontinuity problem, the fast marching method was utilized to connect the discontinuous contour segments, resulting in a closed and continuous tongue body contour for subsequent ACM-based segmentation. Qualitative and quantitative results showed that GaborFM is superior to the other methods for tongue body segmentation.

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Section: Resultsmentioning

confidence: 68%

Section: Comparison With the Other Segmentation Methodsmentioning

confidence: 99%

Section: Comparison With the Other Segmentation Methodsmentioning

confidence: 99%

Section: Comparison With the Other Segmentation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fast marching over the 2D Gabor magnitude domain for tongue body segmentation

Cui

Zhang

et al. 2013

EURASIP J. Adv. Signal Process.

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An iterative transfer learning framework for cross‐domain tongue segmentation

Luo

Zhang

et al. 2020

Concurrency and Computation

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SummaryTongue diagnosis is an important clinical examination in Traditional Chinese Medicine. As the first step of the diagnosis, the accuracy of tongue image segmentation directly affects the subsequent diagnosis. Recently, deep learning‐based methods have been applied for tongue image segmentation and achieve promising results. However, these methods usually work well on one dataset and degenerate significantly on different distributed datasets. To deal with this issue, we propose a framework named Iterative cross‐domain tongue segmentation in the study. First, we train a tongue image segmentation U‐Net model on the source dataset. Then, we propose a tongue assessment filter to select satisfying samples based on predictions of the U‐Net model from the target dataset. Following, we fine‐tune the model on the selected samples along with the source domain. Finally, we iterate between the filtering and the fine‐tuning steps until the model is converged. Experimental results on two tongue datasets show that our proposed method can improve the dice score on the target domain from 70.11% to 98.26%, as well as outperform state‐of‐the‐art comparing methods.

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Reconstruction enhanced probabilistic model for semisupervised tongue image segmentation

Zhou

Fan

Zhao

et al. 2020

Concurrency and Computation

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Tongue segmentation is a key step of automatic tongue diagnosis, and the major challenges for the effective segmentation lie in the large appearance variations of tongue caused by different diseases, for example, tongue coating and tongue texture. Moreover, the limited labeled data also hinders traditional supervised methods from their powerful learning ability. To alleviate these challenges, in this work, we propose a reconstruction enhanced probabilistic model for semisupervised tongue segmentation, named SemiTongue, in which, image reconstruction constraint combined with adversarial learning is used to improve the accuracy of tongue segmentation. Specifically, based on a shared feature encoder that served as an inference model, two separate branches in SemiTongue as the generative model, which are composed of a segmentation decoder and a reconstruction decoder, are utilized to generate the tongue segmentation and reconstruct original tongue image respectively. Then, a discriminator is employed to differentiate the generated segmentation map from the ground truth segmentation distribution. Moreover, semisupervised learning is conducted through discriminator by discovering the reliable region in the generated segmentation map of unlabeled images, which is further utilized to supervise the segmentation branch. Experimental results compared with state-of-the-art methods on real-world datasets demonstrate the effectiveness of SemiTongue.

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Computer Aided Tongue Diagnosis System

Cited by 56 publications

References 4 publications

Fast marching over the 2D Gabor magnitude domain for tongue body segmentation

Fast marching over the 2D Gabor magnitude domain for tongue body segmentation

An iterative transfer learning framework for cross‐domain tongue segmentation

Reconstruction enhanced probabilistic model for semisupervised tongue image segmentation

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