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Adaptive fuzzy-SIFT rule-based registration for 3D cardiac motion estimation

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Abstract

3D Speckle tracking techniques are used to quantify cardiac deformation in 3D echocardiographic images. Elastic image registration methods are successful in solving 3D speckle tracking problems. However, a suitable solution should be exploited to deal with the poor spatio-temporal resolution in the echocardiographic images. That is why the registration problem may encounter some challenges in representing accurate features and defining suitable geometric transformation. The strong modeling ability of a fuzzy rule-based inference system can aid the challenge in geometric modeling. This paper, thus, aims to solve the 3D speckle tracking problem in a new scheme through a fuzzy modeling procedure. The algorithm begins to work by extracting a well-suited local feature descriptor, scale- invariant feature transform (SIFT). Then, the relevant features are aligned with sets of fuzzy rules the optimum parameters of which are adaptively learned in the hybrid learning process of adaptive-neuro fuzzy inference system (ANFIS) structure. Applying the adaptive fuzzy method on STRAUS synthetic dataset yields an acceptable tracking error below 1 mm. Further, strain analysis indicates the capacity of the proposed method in discriminating pathological diagnosis from a healthy one.

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  1. https://team.inria.fr/epione/en/data/straus/

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Authors and Affiliations

  1. Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran

    Monire Sheikh Hosseini & Mahammad Hassan Moradi

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  1. Monire Sheikh Hosseini
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Correspondence to Mahammad Hassan Moradi.

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Hosseini, M.S., Moradi, M.H. Adaptive fuzzy-SIFT rule-based registration for 3D cardiac motion estimation. Appl Intell 52, 1615–1629 (2022). https://doi.org/10.1007/s10489-021-02430-2

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