Active contour-based tooth segmentation in radiographs using fuzzy logic and CNN

Radiographic imaging is a crucial tool frequently employed by dentists for initial diagnosis and treatment planning. However, these images often suffer from distortion or inaccuracies due to incorrect exposure settings, making it challenging to identify critical regions such as tooth roots and margins. This study addresses these issues by presenting two innovative methods for tooth segmentation from radiographs, aimed at isolating the tooth regions for better analysis. The first method utilizes fuzzy logic rules to detect edges within the radiographic images. These detected edges are then used as a mask for the Active Contour Method (ACM) to segment the teeth accurately. The second method involves the creation of a Convolutional Neural Network (CNN) for tooth segmentation. The segmentation performance of the CNN is further refined using the ACM, leveraging the initial segmentation as a mask. Both methods demonstrated notable results with varying performance metrics. Specifically, the Fuzzy-Based Active Contour Method achieved precision, recall, and F1 score values of 0.6246, 0.4169, and 0.50, respectively. In contrast, the CNN-Based Active Contour Method calculated accuracy and specificity values of 0.9706 and 0.9872, respectively. These findings indicate that both approaches have distinct strengths in different performance aspects. Our study suggests that these advanced segmentation techniques can significantly enhance the diagnostic capabilities of dental professionals by providing clearer images of tooth structures, aiding in the detection of issues such as root problems, fractures, and wear patterns. Implementing these methods either independently or in combination could lead to more accurate diagnoses and better patient outcomes. Future work could explore the integration of these techniques to leverage their complementary strengths, potentially leading to even greater segmentation accuracy and reliability.