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Development of a Novel Constellation Based Landmark Detection Algorithm

1Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA.
2Department of Psychiatry, University of Iowa Hospitals & Clinics, Iowa City, IA, USA.
SPIE Medical Imaging 2013
Publication Date:
Volume Number:
Proceedings of SPIE Medical Imaging 2013 Feb;8669:86693F-6.
Automatic landmark detection, Morphometric measures, Statistical shape models
Appears in Collections:
R01 NS040068/NS/NINDS NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Ghayoor A., Vaidya J.G., Johnson H.J. Development of a Novel Constellation Based Landmark Detection Algorithm. Proceedings of SPIE Medical Imaging 2013 Feb;8669:86693F-6.
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Anatomical landmarks such as the anterior commissure (AC) and posterior commissure (PC) are commonly used by researchers for co-registration of images. In this paper, we present a novel, automated approach for landmark detection that combines morphometric constraining and statistical shape models to provide accurate estimation of landmark points. This method is made robust to large rotations in initial head orientation by extracting extra information of the eye centers using a radial Hough transform and exploiting the centroid of head mass (CM) using a novel estimation approach. To evaluate the effectiveness of this method, the algorithm is trained on a set of 20 images with manually selected landmarks, and a test dataset is used to compare the automatically detected against the manually detected landmark locations of the AC, PC, midbrain-pons junction (MPJ), and fourth ventricle notch (VN4). The results show that the proposed method is accurate as the average error between the automatically and manually labeled landmark points is less than 1 mm. Also, the algorithm is highly robust as it was successfully run on a large dataset that included different kinds of images with various orientation, spacing, and origin.

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