Non-rigid image registration under non-deterministic deformation bounds

Ge, Qian; Lokare, Namita; Lobatón, Edgar

doi:10.1117/12.2072530

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2022

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“…Besides, for deformation analysis, quasiconformal models have been proposed for multiscale analysis of deformations [18] and decomposition of deformations into normal and abnormal components [19]. [20,21] are recently proposed to study non-deterministic deformation patterns.…”

Section: Related Workmentioning

confidence: 99%

Nondeterministic Deformation Analysis Using Quasiconformal Geometry

Zhang

Lui

2022

2022 IEEE International Conference on Image Processing (ICIP)

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Deformation analysis is crucial in many applications, especially in medical image analysis. Analyzing the deformation pattern of anatomical structures provides important information for disease analysis. With degraded images or uncertainties, getting a deterministic solution of the deformation is challenging. In some cases, there may also be multiple solutions with different probabilities. As such, it is important to analyze the probability distribution of deformations, given data information with uncertainty. In this work, we propose to use computational Quasiconformal (QC) Teichmuller theories to parameterize the space of deformations. A distribution over the space of special features, called the QC features, can be computed and applied for deformation analysis. Extensive experiments are carried out on both synthetic data and real medical images, which demonstrate the efficacy of the proposed framework.

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