A Fast and Effective Segmentation Algorithm With Automatic Removal of Ineffective Features on Tongue Images

Kamarudin, Nur Diyana; Ooi, Chia Yee; Kawanabe, Tadaaki; Mi, Xiaoyu; Odaguchi, Hiroshi; Hanawa, Toshihiko; Kobayashi, Fuminori

doi:10.11113/jt.v78.7129

Cited by 2 publications

(3 citation statements)

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“…This segmentation algorithm employs Hue, Saturation and Value (HSV) color space as threshold variable. Only brightness represented by Value (𝑉𝑉) is used as a threshold component to reduce the complexity and is formulated as: 𝑉𝑉 = (max {R, G, B}) / 255 (1) To segment the tongue area from perioral area, a novel technique also known as Brightness Conformable Multiplier (BCM) threshold technique [20] is formulated using an average brightness value of 𝑉𝑉𝑚𝑚, a standard deviation, 𝜎𝜎, upper threshold brightness, 𝑉𝑉𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑟𝑟 and lower threshold brightness, 𝑉𝑉𝑙𝑙𝑙𝑙𝑙𝑙𝑢𝑢𝑟𝑟 as shown below:…”

Section: Methodology 21 Tongue Segmentationmentioning

confidence: 99%

“…BCM is formulated to discard the unwanted features (e.g. shadows on the root of the tongue image) that lie on the adjustable brightness safe gap in the transitional brightness boundary [20]. 𝜀𝜀 is defined as below: (5) where 0 ≤ 𝜀𝜀 ≤ 1 and (𝑉𝑉1, 𝑉𝑉2,… 𝑉𝑉N) is the brightness value 𝑉𝑉i for each pixel.…”

Section: Methodology 21 Tongue Segmentationmentioning

confidence: 99%

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Prototyping Digital Tongue Diagnosis System on Raspberry Pi

Yusof

Kamarudin

Rahayu

et al. 2021

IJIE

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Tongue inspection is a complementary diagnosis method that widely used in Traditional Chinese Medicine (TCM) by inspecting the tongue body constitution to decide the physiological and pathological functions of the human body. Since tongue manifestation is done by practitioner’s observation using naked eye, many limitations can affect the diagnosis result including environment condition and experiences of the practitioner. Lately, tongue diagnosis has been widely studied in order to solve these limitations via digital system. However, most of recent digital system are bulky and not equipped with intelligent diagnosis system that can finally predict the health status of the patient. In this research, digital tongue diagnosis system that uses intelligent diagnosis consisted of image segmentation analysis, tongue coating recognition analysis, and tongue color classification has been embedded on Raspberry Pi. Tongue segmentation implements Hue, Saturation and Value (HSV) color space with Brightness Conformable Multiplier (BCM) for adaptive brightness filtering to recognized tongue body accurately while eliminating perioral area. Tongue Coating Recognition uses threshold method to detect tongue coating and eliminate the unwanted features including shadow. Tongue color classification uses hybrid method consisted of k-means clustering and Support Vector Machine (SVM) to classify between red, light red and deep red tongue and further gave diagnosis based on color. This experiment concluded that it is feasible to embed the algorithm on Raspberry Pi to promote system portability while attaining similar accuracy for future telemedicine.

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Section: Methodology 21 Tongue Segmentationmentioning

confidence: 99%

Section: Methodology 21 Tongue Segmentationmentioning

confidence: 99%

Prototyping Digital Tongue Diagnosis System on Raspberry Pi

Yusof

Kamarudin

Rahayu

et al. 2021

IJIE

Self Cite

View full text Add to dashboard Cite

show abstract

“…In recent years, owing to the development of computer techniques, the application of digital image processing has been widely investigated with respect to tongue diagnosis. We cannot disregard new possibilities for improving TCM using computer technologies, such as segmentation of the tongue image [2][3][4], separation of tongue substances and tongue coating [5,6], and analysis of tongue images [1,[7][8][9][10][11][12][13]. Ning et al [4] presented a region merging-based automatic tongue segmentation method.…”

Section: U N C O R R E C T E D P R O O F V E R S I O Nmentioning

confidence: 99%

Classification of fissured tongue images using deep neural networks

Hu¹,

Yan²,

Jiang³

2022

THC

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BACKGROUND: Tongue inspection is vital in traditional Chinese medicine. Fissured tongue is an important feature in tongue diagnosis, and primarily corresponds to three Chinese medicine syndromes: syndrome-related hotness, blood deficiency, and insufficiency of the spleen. Diagnosis of the syndrome is significantly affected by the experience of clinicians, and it is difficult for young doctors to perform accurate diagnoses. OBJECTIVE: The syndrome not only depends on the local features based on fissured regions but also on the global features of the whole tongue; therefore, a syndrome diagnosis framework combining the global and local features of a fissured tongue image was developed in the present study to achieve a quantitative and objective diagnosis. METHODS: First, we detected the fissured region of a tongue image using a single-shot multibox detector. Second, we extracted the global and local features from a whole tongue image and a fissured region using TongueNet (developed in-house). Third, we developed a classifier to determine the final syndrome. RESULTS: Based on an experiment involving 721 fissured tongue images, we discovered that TongueNet affords better feature extraction. The accuracy of TongueNet was 4% (p< 0.05) and 3% (p< 0.05) higher than that of InceptionV3 and ResNet18, respectively, for whole tongue images. Meanwhile, at local fissured regions, the accuracy of TongueNet was 3% (p< 0.05) higher than that of InceptionV3 and equal to that of ResNet18. Finally, the fusion features outperformed the global and local features with a 78% accuracy. CONCLUSIONS: Our findings indicate that TongueNet designed with batch normalization and dropout is more suitable for uncomplicated images than InceptionV3 and ResNet18. In addition, compared with the global features, the fusion features supplement the detailed information of the fissures and improve classification accuracy.

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A Fast and Effective Segmentation Algorithm With Automatic Removal of Ineffective Features on Tongue Images

Cited by 2 publications

References 0 publications

Prototyping Digital Tongue Diagnosis System on Raspberry Pi

Prototyping Digital Tongue Diagnosis System on Raspberry Pi

Classification of fissured tongue images using deep neural networks

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