Department of Computer Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand.
Graduate School, Chiang Mai University, Chiang Mai 50200, Thailand.
Int J Environ Res Public Health. 2023 Feb 15;20(4):3394. doi: 10.3390/ijerph20043394.
Dental fluorosis in children is a prevalent disease in many regions of the world. One of its root causes is excessive exposure to high concentrations of fluoride in contaminated drinking water during tooth formation. Typically, the disease causes undesirable chalky white or even dark brown stains on the tooth enamel. To help dentists screen the severity of fluorosis, this paper proposes an automatic image-based dental fluorosis segmentation and classification system. Six features from red, green, and blue (RGB) and hue, saturation, and intensity (HIS) color spaces are clustered using unsupervised possibilistic fuzzy clustering (UPFC) into five categories: white, yellow, opaque, brown, and background. The fuzzy k-nearest neighbor method is used for feature classification, and the number of clusters is optimized using the cuckoo search algorithm. The resulting multi-prototypes are further utilized to create a binary mask of teeth and used to segment the tooth region into three groups: white-yellow, opaque, and brown pixels. Finally, a fluorosis classification rule is created based on the proportions of opaque and brown pixels to classify fluorosis into four classes: Normal, Stage 1, Stage 2, and Stage 3. The experimental results on 128 blind test images showed that the average pixel accuracy of the segmented binary tooth mask was 92.24% over the four fluorosis classes, and the average pixel accuracy of segmented teeth into white-yellow, opaque, and brown pixels was 79.46%. The proposed method correctly classified four classes of fluorosis in 86 images from a total of 128 blind test images. When compared with a previous work, this result also indicates 10 out of 15 correct classifications on the blind test images, which is equivalent to a 13.33% improvement over the previous work.
儿童氟斑牙是世界上许多地区普遍存在的一种疾病。其根本原因之一是在牙齿形成过程中，过多地暴露于受污染饮用水中的高浓度氟化物。通常情况下，这种疾病会导致牙釉质上出现不理想的白垩色甚至深褐色斑点。为了帮助牙医筛查氟斑牙的严重程度，本文提出了一种基于图像的自动牙齿氟斑牙分割和分类系统。从红、绿、蓝（RGB）和色调、饱和度和强度（HIS）颜色空间提取六个特征，使用无监督可能性模糊聚类（UPFC）将其聚类为五个类别：白色、黄色、不透明、棕色和背景。使用模糊 k-最近邻方法对特征进行分类，并使用布谷鸟搜索算法优化聚类数。得到的多原型进一步用于创建牙齿的二值掩模，并用于将牙齿区域分割为三组：白色-黄色、不透明和棕色像素。最后，根据不透明和棕色像素的比例创建一个氟斑牙分类规则，将氟斑牙分为四个等级：正常、1 级、2 级和 3 级。在 128 张盲测图像上的实验结果表明，对于四个氟斑牙等级，分割出的二值牙齿掩模的平均像素准确率为 92.24%，分割出的白色-黄色、不透明和棕色像素的平均像素准确率为 79.46%。该方法正确分类了 128 张盲测图像中的 86 张图像的四个氟斑牙等级。与之前的工作相比，这一结果也表明在盲测图像上有 10 张图像的正确分类，相对于之前的工作提高了 13.33%。
