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Detection of periodontal bone loss in mandibular area from dental panoramic radiograph using image processing techniques
Concurrency and Computation: Practice and Experience ( IF 2 ) Pub Date : 2021-04-22 , DOI: 10.1002/cpe.6323
M. S. Antony Vigil 1 , V. Subbiah Bharathi 2
Affiliation  

Medical imaging plays the vital role in diagnosis of abnormalities. Periodontitis is a common chronic inflammatory disease damaging the soft tissue that untreated can lead to loss of bone, which supports the teeth. The severity of periodontitis is correlated to pocket depth in alveolar area. Even if analyses of the pocket depth can be manually performed, an automatic assistive tool can drastically help radiologists conduct more accurate analyses. In the proposed research, image processing algorithms were developed to compute pocket depth and diagnose the periodontitis. Radiologists manually segmented 350 panoramic radiographic images, dividing them into normal and periodontitis. The same dataset is used in our work to validate the Classification algorithm. The images are preprocessed with median filter and histogram equalization to improve the contrast and then segmented using two-dimensional-Otsu thresholding method into teeth and bony area in the mandibular region. Normal pocket depth of 3 mm as reported by American Academy of Periodontology is equivalently converted to pixel height in the radiographic images. Based on this pocket depth rule based classification method classifies the images into normal and periodontitis. The proposed work achieved 91.34% accuracy, 92.8% sensitivity, and 95.47% F-score in classifying the dental panoramic radiography.

中文翻译:

使用图像处理技术从牙科全景片中检测下颌区域的牙周骨丢失

医学影像在异常诊断中起着至关重要的作用。牙周炎是一种常见的慢性炎症性疾病,会损害软组织,未经治疗会导致支撑牙齿的骨骼流失。牙周炎的严重程度与牙槽区的牙周袋深度有关。即使可以手动执行囊袋深度分析,自动辅助工具也可以极大地帮助放射科医生进行更准确的分析。在拟议的研究中,开发了图像处理算法来计算牙周袋深度和诊断牙周炎。放射科医生手动分割了 350 张全景放射影像,将它们分为正常和牙周炎。在我们的工作中使用相同的数据集来验证分类算法。图像经过中值滤波和直方图均衡预处理以提高对比度,然后使用二维大津阈值法将图像分割成下颌骨区域的牙齿和骨区。美国牙周病学会报告的 3 毫米正常口袋深度等效地转换为射线图像中的像素高度。基于这种基于口袋深度规则的分类方法,将图像分为正常和牙周炎。拟议的工作在对牙科全景射线照相进行分类时达到了 91.34% 的准确度、92.8% 的灵敏度和 95.47% 的 F 分数。美国牙周病学会报告的 3 毫米正常口袋深度等效地转换为射线图像中的像素高度。基于这种基于口袋深度规则的分类方法,将图像分为正常和牙周炎。拟议的工作在对牙科全景射线照相进行分类时达到了 91.34% 的准确度、92.8% 的灵敏度和 95.47% 的 F 分数。美国牙周病学会报告的 3 毫米正常口袋深度等效地转换为射线图像中的像素高度。基于这种基于口袋深度规则的分类方法,将图像分为正常和牙周炎。拟议的工作在对牙科全景射线照相进行分类时达到了 91.34% 的准确度、92.8% 的灵敏度和 95.47% 的 F 分数。
更新日期:2021-04-22
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