Application of Image Segmentation Technique in Tongue Diagnosis

Zhai, Xiao; Lu, Hang-dong; Zhang, Lizhong

doi:10.1109/ifita.2009.130

Cited by 28 publications

(32 citation statements)

References 5 publications

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“…In Figure 5, we show visual comparisons of the DGF and two previous studies [17,19]. Specifically, the top row denotes the refined tongue images and the final propagating curves of the DGF (white curves), the second row corresponds to the segmentation results of the first row, the third row denotes the segmentation results of C2G2FSnake [19], and the fourth row denotes the segmentation results using double snakes [17].…”

Section: Resultsmentioning

confidence: 99%

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Computerized tongue image segmentation via the double geo-vector flow

Shi

et al. 2014

Chin Med

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BackgroundVisual inspection for tongue analysis is a diagnostic method in traditional Chinese medicine (TCM). Owing to the variations in tongue features, such as color, texture, coating, and shape, it is difficult to precisely extract the tongue region in images. This study aims to quantitatively evaluate tongue diagnosis via automatic tongue segmentation.MethodsExperiments were conducted using a clinical image dataset provided by the Laboratory of Traditional Medical Syndromes, Shanghai University of TCM. First, a clinical tongue image was refined by a saliency window. Second, we initialized the tongue area as the upper binary part and lower level set matrix. Third, a double geo-vector flow (DGF) was proposed to detect the tongue edge and segment the tongue region in the image, such that the geodesic flow was evaluated in the lower part, and the geo-gradient vector flow was evaluated in the upper part.ResultsThe performance of the DGF was evaluated using 100 images. The DGF exhibited better results compared with other representative studies, with its true-positive volume fraction reaching 98.5%, its false-positive volume fraction being 1.51%, and its false-negative volume fraction being 1.42%. The errors between the proposed automatic segmentation results and manual contours were 0.29 and 1.43% in terms of the standard boundary error metrics of Hausdorff distance and mean distance, respectively.ConclusionsBy analyzing the time complexity of the DGF and evaluating its performance via standard boundary and area error metrics, we have shown both efficiency and effectiveness of the DGF for automatic tongue image segmentation.

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Section: Resultsmentioning

confidence: 99%

“…Zhai et al [17] adopted the H-channel as an initial contour for the outside Snake contour, and a double snake was carried out afterwards. The inside contour kept propagating as long as it did not meet the outside contour.…”

Section: Methodsmentioning

confidence: 99%

Computerized tongue image segmentation via the double geo-vector flow

Shi

et al. 2014

Chin Med

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“…However, Zuo, Wang, Zhang, and Zhang (2004) proposed to obtain initial curve by selecting segments from edge map. Comparing with those focusing on geometrical information, many researchers have attempted to utilize color feature of tongue, especially hue-channel value, to obtain initial contour (Shi, Li, & Li, 2013;Yu, Yang, Wang, & Zhang, 2007;Zhai, Lu, & Zhang, 2009). With regard to the curve evolution, Gradient Vector Flow (GVF) (Yu et al, 2007), Double Snake (Zhai et al, 2009), C2G2FSnake (Shi et al, 2013) and Double Geodesic Flow (Shi, Li, Li, & Xu, 2012) were adopted.…”

Section: Introductionmentioning

confidence: 99%

Robust tongue segmentation by fusing region-based and edge-based approaches

Zhang

2015

Expert Systems with Applications

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“…The most widely used method in this field is the active contour model (ACM) or "Snake", which evolves an energy minimizing curve, initialized manually or automatically, and which propagates to the real boundary by both internal and external forces. Works on Snake mainly concentrate on two topics: 1) how to obtain the initial contours [5,6], and 2) how to evolve the curve quickly and accurately [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…Watershed has also been applied to tongue segmentation as initialization [5], but its parameter settings are complex. Colorspace, especially Hchannel, has been widely used in Snake initialization [8], however, it only works well on red tongues. On the other hand, research on curve evolution has not revealed remarkable properties that can definitely solve any representative problems such as local minima, over-learning and weak gradients.…”

Section: Introductionmentioning

confidence: 99%

C2G2FSnake: automatic tongue image segmentation utilizing prior knowledge

Shi

2011

Sci. China Inf. Sci.

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Extraction of the tongue body from digital images is essential for automated tongue diagnoses in traditional Chinese medicine. This paper presents a fully automated active contour initial method that utilizes prior knowledge of the tongue shape and its location in tongue images. Then colorspace information is introduced to control curve evolution. Combining the geometrical Snake model with the parameterized GVFSnake model, a novel approach for automatic tongue segmentation: C 2 G 2 FSnake (color control-geometric & gradient flow Snake) is proposed. This method increases the curve velocity but decreases the complexity. C 2 G 2 FSnake greatly extends practical usage to tongue segmentation, at the same time increasing the precision. Compared with other state-of-the-art works using different images of tongue color, C 2 G 2 FSnake realizes automatic tongue segmentation with greatly improved accuracy.

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Application of Image Segmentation Technique in Tongue Diagnosis

Cited by 28 publications

References 5 publications

Computerized tongue image segmentation via the double geo-vector flow

Computerized tongue image segmentation via the double geo-vector flow

Robust tongue segmentation by fusing region-based and edge-based approaches

C2G2FSnake: automatic tongue image segmentation utilizing prior knowledge

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