An Effective Dental Shape Extraction Algorithm Using Contour Information and Matching by Mahalanobis Distance

Abstract

Human identification using dental radiographs is important in biometrics. Dental radiographs are mainly helpful for individual and mass disaster identification. In the 2004 tsunami, dental records were proven as the primary identifier of victims. So, this work aims to produce an automatic person identification system with shape extraction and matching techniques. For shape extraction, the available information is edge details, structural content, salient points derived from contours and surfaces, and statistical moments. Out of all these features, tooth contour information is a suitable choice here because it can provide better matching. This proposed method consists of four stages. The first step is preprocessing. The second one involves integral intensity projection for segmenting upper jaw, lower jaw, and individual tooth separately. Using connected component labeling, shape extraction was done in the third stage. The outputs obtained from the previous stage for some misaligned images are not satisfactory. So, it is improved by fast connected component labeling. The fourth stage is calculating Mahalanobis distance measure as a means of matching dental records. The matching distance observed for this method is comparatively better when it is compared with the semi-automatic contour extraction method which is our earlier work.

Dental work extraction for different radiographic Images in human Forensic Identification

“…The shape based matching can also be performed by Mahalanobis distance measure. A well-known distance measure which takes into account the covariance matrix is the Mahalanobis distance [11]. When comparing with Euclidean distance, this distance measure takes into the account the correlations of the dataset and is scale invariant.…”

A Review on Dental Radiographic Images

“…C. K. Modi proposed feature extraction technique for dental X-ray image. Also the author suggested future work in the given system is finding a novel feature extraction technique which explores the geometry of teeth which remains inherently unique to each individual [3]. Shape extraction algorithm is developed using contour information and mahalanobis distance measure is done for matching dental records.…”

Dental Bio-metrics as Human Personal Identifier using Pixel Neighborhood Segmentation Techniques

“…Otsu algorithm works fine when x-ray images of teeth are under process. [4] Feature vector set normally consists of statistical features and that do not vary when the size or zoom in out effect may have presence on images. [5] Dental profile based biometrics is the most secure biometrics that remains safe and unchanged throughout the life and even after expiry of a person.…”