Facultad Politécnica, Universidad Nacional de Asunción, Central, Paraguay.
Universitat Politècnica de Catalunya, Barcelona, España.
Stud Health Technol Inform. 2022 Jun 6;290:684-688. doi: 10.3233/SHTI220165.
Panoramic images are one of the most requested exams by dentists for allowing the visualization of the entire mouth. Interpreting X-ray images is a time-consuming task in which misdiagnoses can occur due to the inexperience or fatigue of professionals. In this work, we applied different image enhancement techniques as a pre-processing step to determine which image features correlate with improvements in teeth detection in panoramic images using deep learning architectures. We contrasted the performance of five object-detection architectures using 300 panoramic images of a public dataset. We evaluated the enhancement in the pre-processing step and the detection performance. Quality and detection metrics were considered, and the cross-correlation between them was computed for every object-detection method contemplated. We observe the dependence of the detection performance with some image enhancement techniques, especially those that introduce less noise and preserve the global contrast of the image.
全景图像是牙医最常要求的检查之一，可用于观察整个口腔。解读 X 光图像是一项耗时的任务，由于专业人员经验不足或疲劳，可能会出现误诊。在这项工作中，我们应用了不同的图像增强技术作为预处理步骤，以确定使用深度学习架构在全景图像中检测牙齿时与哪些图像特征相关。我们使用公共数据集的 300 张全景图像对比了五种目标检测架构的性能。我们评估了预处理步骤中的增强效果和检测性能。考虑了质量和检测指标，并为每个考虑的目标检测方法计算了它们之间的互相关。我们观察到检测性能与某些图像增强技术的依赖性，尤其是那些引入较少噪声并保持图像全局对比度的技术。
