A level set segmentation for computer-aided dental x-ray analysis

Abstract

A level-set-based segmentation framework for Computer Aided Dental X-rays Analysis (CADXA) is proposed. In this framework, we first employ level set methods to segment the dental X-ray image into three regions: Normal Region (NR), Potential Abnormal Region (PAR), Abnormal and Background Region (ABR). The segmentation results are then used to build uncertainty maps based on a proposed uncertainty measurement method and an analysis scheme is applied. The level set segmentation method consists of two stages: a training stage and a segmentation stage. During the training stage, manually chosen representative images are segmented using hierarchical level set region detection. The segmentation results are used to train a support vector machine (SVM) classifier. During the segmentation stage, a dental X-ray image is first classified by the trained SVM. The classifier provides an initial contour which is close to the correct boundary for the coupled level set method which is then used to further segment the image. Different dental X-ray images are used to test the framework. Experimental results show that the proposed framework achieves faster level set segmentation and provides more detailed information and indications of possible problems to the dentist. To our best knowledge, this is one of the first results on CADXA using level set methods.