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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...
This paper proposes a novel approach to improve the segmentation quality of a 3D random walks algorithm using classifiability criteria. We produce a range of potential threshold values by extending the decision function of a random walks algorithm using a likelihood ratio test. Optimal threshold values are quantitatively isolated using two data-driven methods: maximum total accuracy and Bayesian cross...
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