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CBCT image based segmentation method for tooth pulp cavity region extraction.

OBJECTIVES:
A method was proposed to segment the tooth pulp cavity region in cone beam CT) images, which aimed to make the extraction process more efficient and generate more reliable results for further research.


METHODS:
Cone beam CT images of 50 teeth from 10 patients were randomly collected with the help of Peking University Hospital of Stomatology. All slice images have a ground truth tooth pulp cavity region delineated by two doctors manually. After necessary gamma transform in pre-processing stage, three kinds of information in an image such as greyscale, neighbour average greyscale and gradient were fused to search an optimal segmentation threshold by using plane intercept histogram of reciprocal cross entropy algorithm. With the optimal threshold, binarization was conducted and the tooth pulp cavity regions in slice images can be extracted. Qualitative and quantitative analyses compared to ground truth are involved with the evaluation criterion of average non-coincidence rate ( RANOA ). Independent repeated experiments were carried out to test the stability of this segmentation method.


RESULTS:
Accurate and complete segmentation results are obtained. The proposed method reaches the lowest RANOA values in most cases and owns more competitive robustness under various interferences compared with the other popular segmentation methods like reciprocal cross entropy method, active contour-based method, region growing method and level set method. Quantitative analysis verified the effectiveness of this method.


CONCLUSIONS:
The proposed method can extract tooth pulp cavity regions from teeth efficiently. The segmentation results of this method are more accurate compared to other popular methods under different circumstances and can be used for subsequent applications.