Non-rigid image registration using a modified fuzzy feature-based inference system for 3D cardiac motion estimation

Hosseini, Monire Sheikh; Moradi, M. H.; Tabassian, Mahdi; D'hooge, Jan

doi:10.1016/j.cmpb.2021.106085

Cited by 5 publications

(2 citation statements)

References 34 publications

(45 reference statements)

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“…In the inference stage, it can directly yield the entire deformation field given the moving and the fixed images. 3) Feature point based method -FeaturePoints [42]. Image local feature points are firstly extracted via the popular scale-invariant feature transform (SIFT) descriptors, and then utilized to register the two images.…”

Section: ) Conventional Non-rigid Image Registration Methods -mentioning

confidence: 99%

“…For instance, Yang et al [41] presented a CNN feature-based motion tracking method for temporal images. Hosseini et al [42] utilize feature points to estimation the cardiac motion problem. Li et al [43] introduced a Multiviewbased Parameter Free framework, where pixel-wise motion estimation has been used to derive feature points for identifying the number groups of people in a crowd video.…”

Section: B Landmark-based Registrationmentioning

confidence: 99%

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Unsupervised Landmark Detection Based Spatiotemporal Motion Estimation for 4D Dynamic Medical Images

Guo¹,

Bi²,

Wei³

et al. 2021

Preprint

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Motion estimation is a fundamental step in dynamic medical image processing for the assessment of target organ anatomy and function. However, existing image-based motion estimation methods, which optimize the motion field by evaluating the local image similarity, are prone to produce implausible estimation, especially in the presence of large motion. In addition, the correct anatomical topology is difficult to be preserved as the image global context is not well incorporated into motion estimation. In this study, we provide a novel motion estimation framework of Dense-Sparse-Dense (DSD), which comprises two stages. In the first stage, we process the raw dense image to extract sparse landmarks to represent the target organ's anatomical topology, and discard the redundant information that is unnecessary for motion estimation. For this purpose, we introduce an unsupervised 3D landmark detection network to extract spatially sparse but representative landmarks for the target organ's motion estimation. In the second stage, we derive the sparse motion displacement from the extracted sparse landmarks of two images of different time-points. Then, we present a motion reconstruction network to construct the motion field by projecting the sparse landmarks' displacement back into the dense image domain. Furthermore, we employ the estimated motion field from our two-stage DSD framework as initialization and boost the motion estimation quality in lightweight yet effective iterative optimization. We evaluate our method on two dynamic medical imaging tasks to model cardiac motion and lung respiratory motion, respectively. Our method has produced superior motion estimation accuracy compared to existing comparative methods. Besides, the extensive experimental results demonstrate that our solution can extract well representative anatomical landmarks without any requirement of manual annotation. Our code is publicly available Online 1 .

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Section: ) Conventional Non-rigid Image Registration Methods -mentioning

confidence: 99%

Section: B Landmark-based Registrationmentioning

confidence: 99%