Non-invasive differential diagnosis of dental periapical lesions in cone-beam CT

Authors:
Arturo Flores;Steven Rysavy;Reyes Enciso;Kazunori Okada
Affiliations:
San Francisco State University, San Francisco, CA;San Francisco State University, San Francisco, CA;University of Southern California, Los Angeles, CA;San Francisco State University, San Francisco, CA
Venue:
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Year:
2009

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Abstract

This paper proposes a novel application of computer-aided diagnosis to a clinically significant dental problem: non-invasive differential diagnosis of periapical lesions using cone-beam computed tomography (CBCT). The proposed semi-automatic solution combines graph-theoretic random walks segmentation and machine learning-based LDA and AdaBoost classifiers. Our quantitative experiments show the effectiveness of the proposed method by demonstrating 94.1% correct classification rate. Furthermore, we compare classification performances with two independent groundtruth sets from the biopsy and CBCT diagnoses. ROC analysis reveals our method improves accuracy for both cases and behaves more in agreement with the CBCT diagnosis, supporting a hypothesis presented in a recent clinical report.