利用纹理控制实现二维/三维差分多模态图像配准的三阶段方法

IF 2.1 3区数学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

SIAM Journal on Imaging Sciences Pub Date : 2024-07-26 DOI:10.1137/23m1583971

Ke Chen, Huan Han

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引用次数: 0

摘要

SIAM 影像科学杂志》，第 17 卷第 3 期，第 1690-1728 页，2024 年 9 月。摘要：在图像配准中，强度不均匀是一项具有挑战性的任务。以往的研究很少涉及纹理噪声导致的强度不均匀问题。为解决这一难题，我们提出了一种新颖的三阶段二维/三维差分多模态图像配准方法。该方法包括三个阶段：(1) [数学] 分解，将图像对分解为纹理、噪声和光滑分量；(2) Blake-Zisserman 均质化，根据一阶和二阶边缘信息将不同模态的几何特征转换为近似相同的模态；(3) 图像配准，将均质化的几何特征和互信息结合起来。基于所提出的方法，讨论了多模态图像配准的贪婪匹配，并提出了一种从粗到细的算法。此外，还进行了若干数值测试，以验证所提方法的效率。

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Three-Stage Approach for 2D/3D Diffeomorphic Multimodality Image Registration with Textural Control

SIAM Journal on Imaging Sciences, Volume 17, Issue 3, Page 1690-1728, September 2024.
Abstract.Intensity inhomogeneity is a challenging task in image registration. Few past works have addressed the case of intensity inhomogeneity due to texture noise. To address this difficulty, we propose a novel three-stage approach for 2D/3D diffeomorphic multimodality image registration. The proposed approach contains three stages: (1) [math] decomposition which decomposes the image pairs into texture, noise, and smooth component; (2) Blake–Zisserman homogenization which transforms the geometric features from different modalities into approximately the same modality in terms of the first-order and second-order edge information; (3) image registration which combines the homogenized geometric features and mutual information. Based on the proposed approach, the greedy matching for multimodality image registration is discussed and a coarse-to-fine algorithm is also proposed. Furthermore, several numerical tests are performed to validate the efficiency of the proposed approach.

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来源期刊

SIAM Journal on Imaging Sciences COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, SOFTWARE ENGINEERING

CiteScore

3.80

自引率

4.80%

发文量

审稿时长

>12 weeks

期刊介绍： SIAM Journal on Imaging Sciences (SIIMS) covers all areas of imaging sciences, broadly interpreted. It includes image formation, image processing, image analysis, image interpretation and understanding, imaging-related machine learning, and inverse problems in imaging; leading to applications to diverse areas in science, medicine, engineering, and other fields. The journal’s scope is meant to be broad enough to include areas now organized under the terms image processing, image analysis, computer graphics, computer vision, visual machine learning, and visualization. Formal approaches, at the level of mathematics and/or computations, as well as state-of-the-art practical results, are expected from manuscripts published in SIIMS. SIIMS is mathematically and computationally based, and offers a unique forum to highlight the commonality of methodology, models, and algorithms among diverse application areas of imaging sciences. SIIMS provides a broad authoritative source for fundamental results in imaging sciences, with a unique combination of mathematics and applications. SIIMS covers a broad range of areas, including but not limited to image formation, image processing, image analysis, computer graphics, computer vision, visualization, image understanding, pattern analysis, machine intelligence, remote sensing, geoscience, signal processing, medical and biomedical imaging, and seismic imaging. The fundamental mathematical theories addressing imaging problems covered by SIIMS include, but are not limited to, harmonic analysis, partial differential equations, differential geometry, numerical analysis, information theory, learning, optimization, statistics, and probability. Research papers that innovate both in the fundamentals and in the applications are especially welcome. SIIMS focuses on conceptually new ideas, methods, and fundamentals as applied to all aspects of imaging sciences.