Abstract
We propose a measure of patient alignment in a video by combining different image representations : grey level, edges, and a set of feature points. When patient head is correctly positionned, a reference image with its ellipse is stored as a template of correct alignment. Edges detection results in a second template of the correct head location. Corners inside the ellipse are detected and tracked: a set of N feature points composes a third template. Template matching computes a measure of similarity between a representation of the reference image and a window sliding around the reference location. Similarity with these three models are combined by the product rule. Location of window the most similar to the templates gives the translation T of the reference model in the image plane. This measure of patient misalignment could avoid X-ray verification of patient alignment, reducing patient dose and duration of treatment sessions.
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Belaroussi, R., Morel, G. (2008). Combination of Image Registration Algorithms for Patient Alignement in Proton Beam Therapy. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds) Image and Signal Processing. ICISP 2008. Lecture Notes in Computer Science, vol 5099. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69905-7_21
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DOI: https://doi.org/10.1007/978-3-540-69905-7_21
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