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Automatic Landmark Identification in Lateral Cephalometric Images Using Optimized Template Matching

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Cephalometric analysis has long helped researchers and orthodontic practitioners for evaluation of facial growth, understanding facial morphology and its ethnic variations, orthodontic diagnosis and treatment planning for patients presenting with malocclusion and dentofacial deformities. Mostly, inaccuracy in cephalometric measurements is a reflection of errors in identification and accurate localization of anatomical landmarks. The accuracy of landmark identification is greatly influenced by knowledge of the operator and experience. Moreover, the process of manual detection is tedious and time consuming. Therefore, a need for development of robust and accurate algorithms for automatic detection of landmarks on cephalometric images has been comprehended. In this work, we hereby propose an optimized template matching (OTM) algorithm which could automatically localize hard and soft tissue anatomical landmarks on lateral cephalometric images. This algorithm was tested for sixteen hard and eight soft tissue landmarks chosen in 12 regions on 37 lateral cephalograms obtained from subjects of either sex covering wide spectrum of malocclusion cases. The results of proposed automatic algorithm were compared to that of manual marking conducted by three experienced orthodontic specialists. All the 24 landmarks (100%) were detected within 3.0 mm error range of manual marking, 23 (96%) were detected within 2.5 mm error range and 16 (66.6%) landmarks were detected within 2.0 mm error range. The optimized template matching (OTM) algorithm may prove to be a promising approach in automatic detection of anatomical landmarks on cephalometric images.
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Document Type: Research Article

Publication date: June 1, 2015

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  • Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas.
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