Non-rigid registration of medical images based on ordinal feature and manifold learning

Qi Li; Jin Liu; Bo Zang

doi:10.1117/12.2204937

14 December 2015 Non-rigid registration of medical images based on ordinal feature and manifold learning

Qi Li, Jin Liu, Bo Zang

Proceedings Volume 9814, MIPPR 2015: Parallel Processing of Images and Optimization; and Medical Imaging Processing; 98140E (2015) https://doi.org/10.1117/12.2204937
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China

Abstract

With the rapid development of medical imaging technology, medical image research and application has become a research hotspot. This paper offers a solution to non-rigid registration of medical images based on ordinal feature (OF) and manifold learning. The structural features of medical images are extracted by combining ordinal features with local linear embedding (LLE) to improve the precision and speed of the registration algorithm. A physical model based on manifold learning and optimization search is constructed according to the complicated characteristics of non-rigid registration. The experimental results demonstrate the robustness and applicability of the proposed registration scheme.

Citation Download Citation

Qi Li, Jin Liu, and Bo Zang "Non-rigid registration of medical images based on ordinal feature and manifold learning", Proc. SPIE 9814, MIPPR 2015: Parallel Processing of Images and Optimization; and Medical Imaging Processing, 98140E (14 December 2015); https://doi.org/10.1117/12.2204937

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