Lateral Cephalometric Landmark Annotation Using Histogram Oriented Gradients Extracted from Region of Interest Patches

• Published in: Journal of maxillofacial and oral surgery 2023-12, Vol.22 (4), p.806-812
• Main Authors: Rashmi, S., Srinath, S., Patil, Karthikeya, Murthy, Prashanth Sadashiva, Deshmukh, Seema
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Annotations; Artificial intelligence; Automation; Datasets; Deep learning; Dentistry; Localization; Machine learning; Medicine; Medicine & Public Health; Oral and Maxillofacial Surgery; Original; Original Article; Otorhinolaryngology; Plastic Surgery
• ISSN: 0972-8279; 0974-942X
• DOI: 10.1007/s12663-023-02025-z

Introduction Two-dimensional cephalometric image analysis plays a crucial role in orthodontic diagnosis and treatment planning. While deep learning-based algorithms have emerged to automate the laborious task of anatomical landmark annotation, their effectiveness is hampered by the challenges of acquiring and labelling clinical data. In this study, we propose a model that leverages conventional machine learning techniques to enhance the accuracy of landmark detection using limited dataset. Materials and methods Our methodology involves coarse localization through region of interest (ROI) extraction and fine localization utilizing histogram-oriented gradient (HOG) feature. The image patch containing landmark pixels is classified using the light gradient boosting machine (LGBM) algorithm. To evaluate our model’s performance, we conducted rigorous tests on the ISBI Cephalometric dataset and Dental Cepha dataset, aiming to achieve accuracy within a 2 mm radial precision range. We also employed cross-validation to assess our approach, providing a robust evaluation. Results Our model’s performance on the ISBI Cephalometric dataset showed an accuracy rate of 77.11% within the desired 2 mm radial precision range. The cross-validation results further confirmed the effectiveness of our approach, yielding a mean accuracy of 78.17%. Additionally, we applied our model to the Dental Cepha dataset, where we achieved a remarkable landmark detection accuracy of 84%. Conclusion The results demonstrate that traditional machine learning techniques can be effective for accurate landmark detection in cephalometric images, even with limited data. Our findings highlight the potential of these techniques for clinical applications, where large datasets of labelled images may not be available.