Accurate identification of anatomical structures from medical imaging data is a significant and critical function in the medical domain. Past studies in this context have mainly utilized two main approaches, the knowledge and learning methodologies based methods. Further, most of previous reported studies have focused on identification of landmarks from lateral X-ray Computed Tomography (CT) data, particularly in the field of orthodontics. However, this study focused on extracting cranial landmarks from large sets of cross sectional CT slices using a combined method of the two aforementioned approaches. The proposed method of this study is centered mainly on template data sets, which were created using the actual contour patterns extracted from CT cases for each of the landmarks in consideration. Firstly, these templates were used to devise rules which are a characteristic of the knowledge based method. Secondly, the same template sets were employed to perform template matching related to the learning methodologies approach. The proposed method was tested on two landmarks, the Dorsum sellae and the Pterygoid plate, using CT cases of 5 subjects. The results indicate that, out of the 10 tests, the output images were within the expected range (desired accuracy) in 7 instances and acceptable range (near accuracy) for 2 instances, thus verifying the effectiveness of the combined template sets centric approach proposed in this study.


Computed Tomography (CT), Pattern Recognition, Contour Template Matching, OpenCV