Tongue Image Segmentation via Color Decomposition and Thresholding

Li, Zuoyong; Zhao, Yu; Liu, Weixia; Zhang, Zuchang

doi:10.1109/icisce.2017.161

Cited by 17 publications

(8 citation statements)

References 10 publications

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“…Motivated by both methods and ACM-based methods, we propose this work to improve tongue image segmentation accuracy. Differences between this work and our previous works [23][24] are as follows: (1) this work simplifies the hue component transformation for parameter reducing;…”

Section: Introductionmentioning

confidence: 98%

“…When the initial contour contains strong fake object contour, it is difficult to converge to the true object contour. Motivated by our observation that there is an obvious hue difference between tongue body pixels and their neighboring face pixels, we proposed two tongue image segmentation methods [23][24]. The first method [23] published on the conference is a preliminary version of the second method [24] that is published on the journal.…”

Section: Introductionmentioning

confidence: 99%

“…Motivated by our observation that there is an obvious hue difference between tongue body pixels and their neighboring face pixels, we proposed two tongue image segmentation methods [23][24]. The first method [23] published on the conference is a preliminary version of the second method [24] that is published on the journal. The method in [23] first maps an image from RGB to HSI, and conducts image thresholding on the hue component to extract an initial object region, and then performs image thresholding on the red component to find the gap region between the upper lip and the tongue body root, and finally uses the gap region to remove fake object region and obtain final tongue body region.…”

Section: Introductionmentioning

confidence: 99%

“…The first method [23] published on the conference is a preliminary version of the second method [24] that is published on the journal. The method in [23] first maps an image from RGB to HSI, and conducts image thresholding on the hue component to extract an initial object region, and then performs image thresholding on the red component to find the gap region between the upper lip and the tongue body root, and finally uses the gap region to remove fake object region and obtain final tongue body region. On the basis of the first method [23], the second method [24] can more accurately find the above gap region by adaptively selecting one of two image thresholding results on the red component.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Tongue Image Segmentation via Thresholding and Gray Projection

2019

KSII TIIS

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Tongue diagnosis is one of the most important diagnostic methods in Traditional Chinese Medicine (TCM). Tongue image segmentation aims to extract the image object (i.e., tongue body), which plays a key role in the process of manufacturing an automated tongue diagnosis system. It is still challenging, because there exists the personal diversity in tongue appearances such as size, shape, and color. This paper proposes an innovative segmentation method that uses image thresholding, gray projection and active contour model (ACM). Specifically, an initial object region is first extracted by performing image thresholding in HSI (i.e., Hue Saturation Intensity) color space, and subsequent morphological operations. Then, a gray projection technique is used to determine the upper bound of the tongue body root for refining the initial object region. Finally, the contour of the refined object region is smoothed by ACM. Experimental results on a dataset composed of 100 color tongue images

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Tongue Image Segmentation via Thresholding and Gray Projection

2019

KSII TIIS

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“…Some researchers utilize the three components of RGB image respectively and then add the results together. Others may use some new color space like bi-color space [31] and method based on the HIS and RGB color model [37]. In our study, we choose a combined color space consisting of CIE-LUV color space and DHT.…”

Section: Color Space and Key Points 331 Color Spacementioning

confidence: 99%

Lip segmentation using automatic selected initial contours based on localized active contour model

Liu

2018

J Image Video Proc.

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With the rapid development of artificial intelligence and the increasing popularity of smart devices, human-computer interaction technology has become a multimedia and multimode technology from being computer-focused to people-centered. Among all ways of human-computer interactions, using language to interact with machines is the most convenient and efficient one. However, the performance of audio speech recognition systems is not satisfied in a noisy environment. Thus, more and more researchers focus their works on visual lip reading technology. By extracting lip movement features of speakers rather than audio features, visual lip reading systems can get superior results when noises and interferences exist. Lip segmentation plays an important role in a visual lip reading system, since the segmentation result is crucial to the final recognition accuracy. In this paper, we propose a localized active contour model-based method using two initial contours in a combined color space. We apply illumination equalization to original RGB images to decrease the interference of uneven illumination. A combined color space consists of the U component in CIE-LUV color space and the sum of C2 and C3 components of the image after discrete Hartley transform. We select a rhombus as the initial contour of a closed mouth, because it has a similar shape to a closed lip. For an open mouth, we utilize a combined semi-ellipse as the initial contours of both outer and inner lip boundaries. After attaining the results of each color component separately, we merge them together to obtain the final segmentation result. From the experiment, we can conclude that this method can get better segmentation results compared with the method using a circle as the initial contour to segment gray images and images in combined color space, especially for open mouth. An extremely obvious advantage of this method is the results of open mouth excluding internal information of mouth such as teeth, black holes, and tongue, because of the introduction of the inner initial contour.

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Tongue image segmentation via color decomposition and thresholding

Zhao

Liu

et al. 2018

Concurrency and Computation

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Summary Tongue image segmentation is a crucial step in developing an automatic tongue diagnosis system. After exploring characteristics of image thresholding in different color spaces, we propose a simple and effective tongue image segmentation method. Specifically, the proposed method first extracts an initial tongue body region by performing image thresholding on the transformed hue component in HSI (Hue, Saturation, and Intensity) color space. Then, one of two image thresholding results on the red component of an original tongue image is adaptively selected to find the gap region between the tongue body root and the upper lip. Finally, the initial tongue body region is refined by removing fake object regions such as the upper lip to obtain final tongue image segmentation result. Experimental results on a tongue image dataset containing 200 subjects demonstrate the effectiveness of the proposed method.

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Tongue Image Segmentation via Color Decomposition and Thresholding

Cited by 17 publications

References 10 publications

Tongue Image Segmentation via Thresholding and Gray Projection

Tongue Image Segmentation via Thresholding and Gray Projection

Lip segmentation using automatic selected initial contours based on localized active contour model

Tongue image segmentation via color decomposition and thresholding

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