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Computer aided periapical lesion diagnosis using quantized texture analysis

Periapical lesion is a common disease in oral health. While many studies have been devoted to image-based diagnosis of periapical lesion, these studies usually require clinicians to perform the task. In this paper we investigate the automatic solutions toward periapical lesion classification using quantized texture analysis. Specifically, we adapt the bag-of-visual-words model for periapical root image representation, which captures the texture information by collecting local patch statistics. Then we investigate several similarity measure approaches with the K-nearest neighbor (KNN) classifier for the diagnosis task. To evaluate these classifiers we have collected a digitized oral X-Ray image dataset from 21 patients, resulting 139 root images in total. The extensive experimental results demonstrate that the KNN classifier based on the bagof- words model can achieve very promising performance for periapical lesion classification.