Superpixel Approach in Computer-Aided Detection System of Dental Cavities in X-Ray Images

Abstract

Dental caries can be characterized and segmented using a computer-aided diagnosis (CADx) system. In most CADx systems, segmentation is a key step in identifying dental cavities at an early stage. With well-segmented lesions, dentists can provide accurate diagnoses. This study proposes a semi-automated method based on superpixel segmentation to identify dental cavities in digital X-ray images. Superpixel segmentation partitions an image into multiple homogeneous segments, wherein the pixels of each segment contain certain comparable characteristics. The proposed method is compared with two previous methods; in one method, an active contour model, or snake, iteratively performs the initial contour, self-examination, and correction of the segmentation results, whereas in the other method, distance regularized level set evolution (DRLSE) eliminates the need for re-initialization, thereby avoiding its induced numerical errors. The proposed method combines DRLSE with superpixels. For the comparison of these three algorithms, 11 authentic teeth, ranging from premolars to molars, were used. With regard to the detection results and classification accuracies, the proposed superpixel DRLSE method received lower error metric scores than the other methods.