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Automatic detection of alveolar bone-loss areas in dental periapical radiographs is a very challenging task because of the common uneven illumination problem of dental radiographs and complex topology of bone-loss areas. In this paper, we propose an effective automatic detection method ABL-Imm, which uses weighted average of both the intensity and the texture measured by the H-value of fractal Brownian motions (mm) model. The weights are trained with receiver operating characteristics (ROC) curve based on leave-one-out cross validation mechanism and the principle of the minimum area under the ROC curve (AUC). Through the weighted average of both features, radiograph images are transformed into feature images with the histogram near bimodal distribution. Finally, feature images are segmented into normal and bone-loss regionsby Otsu's auto-thresholding. We test on eight periodontitis radiograph images using the proposed ABL-Imm, the methods with only the feature of mm-H or the intensity, and a method based on level set segmentation, respectively. Experimental results showed that among all the test methods, our proposed ABL-Imm has the highest average TPVF and the lowest average FPVF, when compared with the ground truth (GT) provided by dentists.

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