Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6920811 | Computers in Biology and Medicine | 2016 | 12 Pages |
Abstract
The proposed method has been validated on 97 Cone Beam Computed Tomography (CBCT) sets containing various jaw cysts which were collected from various image acquisition centers. Validation is performed using three similarity indicators (Jaccard index, Dice's coefficient and Hausdorff distance). The mean Dice's coefficient of 0.83, 0.87 and 0.80 is achieved for Radicular, Dentigerous and KCOT classes, respectively. For most of the experiments done, we achieved high true positive (TP). This means that a large number of cyst pixels are correctly classified. Quantitative results of automatic segmentation show that the proposed method is more effective than one of the recent methods in the literature.
Keywords
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Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Fatemeh Abdolali, Reza Aghaeizadeh Zoroofi, Yoshito Otake, Yoshinobu Sato,