Sawagashira Tsuyoshi, Hayashi Tatsuro, Hara Takeshi, Katsumata Akitoshi, Muramatsu Chisako, Zhou Xiangrong, Iida Yukihiro, Katagi Kiyoji, Fujita Hiroshi
Department of Intelligent Image Information, Division of Regeneration and Advanced Medical Sciences, Graduate School of Medicine, Gifu University, Gifu 501-1194, Japan.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:6208-11. doi: 10.1109/IEMBS.2011.6091533.
The purpose of this study is to develop an automated carotid artery calcification (CAC) detection scheme on dental panoramic radiographs (DPRs). The CAC is one of the indices for predicting the risk of arteriosclerosis. First, regions of interest (ROIs) that include CACs were determined on the basis of inflection points of the mandibular contour. Initial CAC candidates were detected by using a grayscale top-hat filter and simple grayscale thresholding technique. Finally, a rule-based approach and support vector machine to reduce the number of false positive (FP) findings were applied using features such as area, location, and circularity. Thirty-four DPRs were used to evaluate the proposed scheme. The sensitivity for the detection of CACs was 93.6% with 4.4 FPs per image. Experimental results showed that our computer-aided detection scheme may be useful to detect CACs.
本研究的目的是开发一种基于牙科全景X线片（DPR）的自动颈动脉钙化（CAC）检测方案。CAC是预测动脉硬化风险的指标之一。首先，根据下颌轮廓的拐点确定包含CAC的感兴趣区域（ROI）。通过使用灰度高帽滤波器和简单的灰度阈值技术检测初始CAC候选对象。最后，使用面积、位置和圆形度等特征，应用基于规则的方法和支持向量机来减少假阳性（FP）结果的数量。使用34张DPR来评估所提出的方案。检测CAC的灵敏度为93.6%，每张图像有4.4个FP。实验结果表明，我们的计算机辅助检测方案可能有助于检测CAC。
