• Title/Summary/Keyword: Tongue Image Segmentation

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A development of a Digital tongue diagnosis system using the tongue color analysis of the each taste region (미각 영역별 설색 분석을 이용한 디지털 설진 시스템 개발)

  • Choi, Min;Yang, Dong-Min;Lee, Kyu-Won
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.19 no.2
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    • pp.428-434
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    • 2015
  • A new tongue examination model by using color analysis according to the taste division of tongue. The proposed system consists of a tongue image acquisition in a predefined template, taste region segmentation, color distribution analysis and abnormality decision of tongue by color analysis using Hue-Saturation histograms and the part of a mobile application service. We divided 4 basic taste(bitter, sweet, salty and sour) regions and performed color distribution analysis targeting each region under HSI(Hue Saturation Intensity) color model. To minimize the influence of illumination, the histograms of H and S components only except U are utilized. Using the analyzed results, the abnormality is discriminated by the criteria of the histogram range of normal tongues. Finally, a self tongue diagnosis system which can be used anytime and anywhere on mobile environment.

A development of a new tongue diagnosis model in the oriental medicine by the color analysis of tongue (혀의 색상 분석에 의한 새로운 한방 설진(舌診) 모델 개발)

  • Choi, Min;Lee, Min-taek;Lee, Kyu-won
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2013.05a
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    • pp.801-804
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    • 2013
  • We propose a new tongue examination model according to the taste division of tongue. The proposed sytem consists of image acquisition, region segmentation, color distribution analysis and abnormality decision of tongue. Tongue DB which is classified into abnormality is constructed with tongue images captured from oriental medicine hospital inpatients. We divided 4 basic taste(bitter, sweet, salty and sour) regions and performed color distribution analysis targeting each region under HSI(Hue Saturation Intensity) color model. To minimize the influence of illumination, the histograms of H and S components only except I are utilized. The abnormality of taste regions each by comparing the proposed diagnosis model with diagnosis results by a doctor of oriental medicine. We confirmed the 87.5% of classification results of abnormality by proposed algorithm is coincide with the doctor's results.

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Real-time Tooth Region Detection in Intraoral Scanner Images with Deep Learning (딥러닝을 이용한 구강 스캐너 이미지 내 치아 영역 실시간 검출)

  • Na-Yun, Park;Ji-Hoon Kim;Tae-Min Kim;Kyeong-Jin Song;Yu-Jin Byun;Min-Ju Kang․;Kyungkoo Jun;Jae-Gon Kim
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.46 no.3
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    • pp.1-6
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    • 2023
  • In the realm of dental prosthesis fabrication, obtaining accurate impressions has historically been a challenging and inefficient process, often hindered by hygiene concerns and patient discomfort. Addressing these limitations, Company D recently introduced a cutting-edge solution by harnessing the potential of intraoral scan images to create 3D dental models. However, the complexity of these scan images, encompassing not only teeth and gums but also the palate, tongue, and other structures, posed a new set of challenges. In response, we propose a sophisticated real-time image segmentation algorithm that selectively extracts pertinent data, specifically focusing on teeth and gums, from oral scan images obtained through Company D's oral scanner for 3D model generation. A key challenge we tackled was the detection of the intricate molar regions, common in dental imaging, which we effectively addressed through intelligent data augmentation for enhanced training. By placing significant emphasis on both accuracy and speed, critical factors for real-time intraoral scanning, our proposed algorithm demonstrated exceptional performance, boasting an impressive accuracy rate of 0.91 and an unrivaled FPS of 92.4. Compared to existing algorithms, our solution exhibited superior outcomes when integrated into Company D's oral scanner. This algorithm is scheduled for deployment and commercialization within Company D's intraoral scanner.