Automatic localization of anatomical point landmarks for brain image processing algorithms.
Many brain image processing algorithms require one or more well-chosen seed points because they need to be initialized close to an optimal solution. Anatomical point landmarks are useful for constructing initial conditions for these algorithms because they tend to be highly-visible and predictably-located points in brain image scans. We introduce an empirical training procedure that locates user-selected anatomical point landmarks within well-defined precisions using image data with different resolutions and MRI weightings. Our approach makes no assumptions on the structural or intensity characteristics of the images and produces results that have no tunable run-time parameters. We demonstrate the procedure using a Java GUI application (LONI ICE) to determine the MRI weighting of brain scans and to locate features in T1-weighted and T2-weighted scans.
Neu
Scott C
SC
Department of Neurology, UCLA Laboratory of Neuro Imaging, David Geffen School of Medicine, Suite 225, 635 Charles Young Drive South, Los Angeles, CA 90095-7334, USA.
Toga
Arthur W
AW
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods; D011381:Programming Languages; D012984:Software; D012986:Software Validation; D013663:Teaching / Q000379:methods; D013997:Time Factors