Enhancing Dental Image Segmentation Techniques: Edge Detection and Color Thresholding
Abstract
Rapid advancements in medical technology, particularly in the field of dentistry, have led to significant progress in the application of medical imaging techniques to generate valuable image data. The resulting images often exhibit heterogeneous intensity distributions, with boundaries not always distinctly clear between the tooth roots and bone, along with variations in shape and pose. This study specifically aimed to identify the optimal image for segmenting specific parts of the dental structures. Image segmentation is crucial for ensuring effective diagnosis in the context of dental medicine. To achieve optimal dental image segmentation, this research combines edge detection methods with the determination of color thresholds, specifically grayscale and Hue, Saturation, Value (HSV). The research findings revealed that edge detection using the Sobel gradient operator yielded a relevant count of 17,099 pixels. Using RGB=3 and HSV=0.3 the color thresholds show an enhancement in the brightness of the resulting HSV-segmented image, while in the RGB-segmented image, the selected object appears more prominent. The findings of this study contribute significantly to the evolution of dental image segmentation techniques, potentially enhancing the accuracy and effectiveness of diagnoses within the realm of modern dental practice
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