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Proceedings Paper

Computer aided periapical lesion diagnosis using quantized texture analysis
Author(s): Yi Wu; Fangfang Xie; Jie Yang; Erkang Cheng; Vasileios Megalooikonomou; Haibin Ling
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Paper Abstract

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.

Paper Details

Date Published: 23 February 2012
PDF: 7 pages
Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 831518 (23 February 2012); doi: 10.1117/12.911500
Show Author Affiliations
Yi Wu, Nanjing Univ. of Information Science and Technology (China)
Temple Univ. (United States)
Fangfang Xie, Temple Univ. (United States)
Jie Yang, Temple Univ. (United States)
Erkang Cheng, Temple Univ. (United States)
Vasileios Megalooikonomou, Temple Univ. (United States)
Haibin Ling, Temple Univ. (United States)

Published in SPIE Proceedings Vol. 8315:
Medical Imaging 2012: Computer-Aided Diagnosis
Bram van Ginneken; Carol L. Novak, Editor(s)

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