3D optical flow estimation in cardiac CT images using the Hermite transform

Ernesto Moya-Albor; Carlos Mira; Jorge Brieva; Boris Escalante-Ramírez; Enrique Vallejo Venegas

doi:10.1117/12.2256777

26 January 2017 3D optical flow estimation in cardiac CT images using the Hermite transform

Ernesto Moya-Albor, Carlos Mira, Jorge Brieva, Boris Escalante-Ramírez, Enrique Vallejo Venegas

Proceedings Volume 10160, 12th International Symposium on Medical Information Processing and Analysis; 1016010 (2017) https://doi.org/10.1117/12.2256777
Event: 12th International Symposium on Medical Information Processing and Analysis, 2016, Tandil, Argentina

Abstract

Heart diseases are one of the most important causes of death in the Western world. It is, then, important to implement algorithms to aid the specialist in analyzing the heart motion. We propose a new strategy to estimate the cardiac motion through a 3D optical flow differential technique that uses the Steered Hermite transform (SHT). SHT is a tool that performs a decomposition of the images in a base that model the visual patterns used by the human vision system (HSV) for processing the information. The 3D + t analysis allows to describe most of motions of the heart, for example, the twisting motion that takes place on every beat cycle and to identify abnormalities of the heart walls. Our proposal was tested on two phantoms and on two sequences of cardiac CT images corresponding to two different patients. We evaluate our method using a reconstruction schema, for this, the resulting 3D optical flow was applied over the volume at time t to obtain a estimated volume at time t + 1. We compared our 3D optical flow approach to the classical Horn and Shunk's 3D algorithm for different levels of noise.

Citation Download Citation

Ernesto Moya-Albor, Carlos Mira, Jorge Brieva, Boris Escalante-Ramírez, and Enrique Vallejo Venegas "3D optical flow estimation in cardiac CT images using the Hermite transform", Proc. SPIE 10160, 12th International Symposium on Medical Information Processing and Analysis, 1016010 (26 January 2017); https://doi.org/10.1117/12.2256777

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