Three-dimensional cranial image registration based on geometric transformation

Xiaodan Yu; Liang Hua; Tianyu Cheng

doi:10.1117/12.2558098

3 January 2020 Three-dimensional cranial image registration based on geometric transformation

Xiaodan Yu, Liang Hua, Tianyu Cheng

Proceedings Volume 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019); 113731V (2020) https://doi.org/10.1117/12.2558098
Event: Eleventh International Conference on Graphics and Image Processing, 2019, Hangzhou, China

Abstract

In practice of clinical, two kinds of medical image information should be considered comprehensively to judge the lesion, so it is necessary to register these images. A method for registration based on pure geometric transformation is presented in this paper. Generally, a group of transverse two-dimensional skull slices are obtained. Firstly, using canny operator to extract the edge of these images, three-dimensional modelling and unifying the spatial resolution. Obtain the maximum common imaging area in the z direction to unify imaging region. Calculating the centroid of the region, shifting the centroids to the origin of coordinates respectively. Secondly, using the bridge of the nose which is the obvious feature of skull to construct the first unit vector. Using the rotation operator of Clifford geometric algebra space to rotate the first unit vector to coincide with the y axis. Thirdly, by using the principle that the sum of the distances from the contour points on the xoy plane to the vector which pass through the origin is the smallest to construct the second unit vector, this vector of the floating mode is rotated by affine transformation to coincide with the reference one. The data of cranial images are derived from the "Retrospective Image Registration Assessment" project database of Vanderbilt University, and the results are evaluated using the gold standard of this database. The results of this experiment show that this method has the advantages of simple calculation, intuitive geometric meaning, high registration accuracy and high execution efficiency.

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Xiaodan Yu, Liang Hua, and Tianyu Cheng "Three-dimensional cranial image registration based on geometric transformation", Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 113731V (3 January 2020); https://doi.org/10.1117/12.2558098

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KEYWORDS

Image registration

3D image processing

3D modeling

Ear

Medical imaging

Detection and tracking algorithms

Edge detection

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