Adaptive Diffeomorphic Multiresolution Demons and Their Application to Same Modality Medical Image Registration with Large Deformation.

IF 3.3 Q2 ENGINEERING, BIOMEDICAL
International Journal of Biomedical Imaging Pub Date : 2018-05-16 eCollection Date: 2018-01-01 DOI:10.1155/2018/7314612
Chang Wang, Qiongqiong Ren, Xin Qin, Yi Yu
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引用次数: 4

Abstract

Diffeomorphic demons can guarantee smooth and reversible deformation and avoid unreasonable deformation. However, the number of iterations needs to be set manually, and this greatly influences the registration result. In order to solve this problem, we proposed adaptive diffeomorphic multiresolution demons in this paper. We used an optimized framework with nonrigid registration and diffeomorphism strategy, designed a similarity energy function based on grey value, and stopped iterations adaptively. This method was tested by synthetic image and same modality medical image. Large deformation was simulated by rotational distortion and extrusion transform, medical image registration with large deformation was performed, and quantitative analyses were conducted using the registration evaluation indexes, and the influence of different driving forces and parameters on the registration result was analyzed. The registration results of same modality medical images were compared with those obtained using active demons, additive demons, and diffeomorphic demons. Quantitative analyses showed that the proposed method's normalized cross-correlation coefficient and structural similarity were the highest and mean square error was the lowest. Medical image registration with large deformation could be performed successfully; evaluation indexes remained stable with an increase in deformation strength. The proposed method is effective and robust, and it can be applied to nonrigid registration of same modality medical images with large deformation.

Abstract Image

Abstract Image

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自适应差分同构多分辨率算法及其在大变形同模态医学图像配准中的应用。
微分同胚形可保证变形的平滑和可逆,避免不合理的变形。然而,迭代次数需要手动设置,这极大地影响了注册结果。为了解决这一问题,本文提出了自适应差分同态多分辨率图像。采用非刚性配准和差分同构策略的优化框架,设计基于灰度值的相似能量函数,自适应停止迭代。用合成图像和同模态医学图像对该方法进行了验证。通过旋转变形和挤压变换模拟大变形,对大变形医学图像进行配准,并利用配准评价指标进行定量分析,分析不同驱动力和参数对配准结果的影响。将同模态医学图像的配准结果与使用有源配准、加性配准和差分配准的配准结果进行比较。定量分析表明,该方法的归一化相关系数和结构相似度最高,均方误差最低。可以成功实现大变形医学图像配准;随着变形强度的增加,评价指标保持稳定。该方法具有较强的鲁棒性和有效性,可应用于形变较大的同模态医学图像的非刚性配准。
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来源期刊
CiteScore
12.00
自引率
0.00%
发文量
11
审稿时长
20 weeks
期刊介绍: The International Journal of Biomedical Imaging is managed by a board of editors comprising internationally renowned active researchers. The journal is freely accessible online and also offered for purchase in print format. It employs a web-based review system to ensure swift turnaround times while maintaining high standards. In addition to regular issues, special issues are organized by guest editors. The subject areas covered include (but are not limited to): Digital radiography and tomosynthesis X-ray computed tomography (CT) Magnetic resonance imaging (MRI) Single photon emission computed tomography (SPECT) Positron emission tomography (PET) Ultrasound imaging Diffuse optical tomography, coherence, fluorescence, bioluminescence tomography, impedance tomography Neutron imaging for biomedical applications Magnetic and optical spectroscopy, and optical biopsy Optical, electron, scanning tunneling/atomic force microscopy Small animal imaging Functional, cellular, and molecular imaging Imaging assays for screening and molecular analysis Microarray image analysis and bioinformatics Emerging biomedical imaging techniques Imaging modality fusion Biomedical imaging instrumentation Biomedical image processing, pattern recognition, and analysis Biomedical image visualization, compression, transmission, and storage Imaging and modeling related to systems biology and systems biomedicine Applied mathematics, applied physics, and chemistry related to biomedical imaging Grid-enabling technology for biomedical imaging and informatics
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