A Survey on Evaluation Methods for Medical Image Registration

Cao, Ximiao; Ruan, Qiuqi

doi:10.1109/iccme.2007.4381831

Cited by 10 publications

(4 citation statements)

References 4 publications

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“…A large number of medical image registration algorithms solely relying on image processing methods have been successfully developed over the years (Cao and Ruan, 2007; Jenkinson and Smith, 2001; Sotiras et al, 2013; Wells et al, 1996). Many of them have been demonstrated to be effective for selected organs, such as the brain, breast, prostate and lungs (Goerres et al, 2002; Mattes et al, 2003; Oguro et al, 2011; Rueckert et al, 1999; Warfield et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Patient-specific biomechanical model as whole-body CT image registration tool

Miller

Joldes

et al. 2015

Medical Image Analysis

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Whole-body computed tomography (CT) image registration is important for cancer diagnosis, therapy planning and treatment. Such registration requires accounting for large differences between source and target images caused by deformations of soft organs/tissues and articulated motion of skeletal structures. The registration algorithms relying solely on image processing methods exhibit deficiencies in accounting for such deformations and motion. We propose to predict the deformations and movements of body organs/tissues and skeletal structures for whole-body CT image registration using patient-specific non-linear biomechanical modelling. Unlike the conventional biomechanical modelling, our approach for building the biomechanical models does not require time-consuming segmentation of CT scans to divide the whole body into non-overlapping constituents with different material properties. Instead, a Fuzzy C-Means (FCM) algorithm is used for tissue classification to assign the constitutive properties automatically at integration points of the computation grid. We use only very simple segmentation of the spine when determining vertebrae displacements to define loading for biomechanical models. We demonstrate the feasibility and accuracy of our approach on CT images of seven patients suffering from cancer and aortic disease. The results confirm that accurate whole-body CT image registration can be achieved using a patient-specific non-linear biomechanical model constructed without time-consuming segmentation of the whole-body images.

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Section: Introductionmentioning

confidence: 99%

Patient-specific biomechanical model as whole-body CT image registration tool

Miller

Joldes

et al. 2015

Medical Image Analysis

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“…Numerous methods have been proposed to solve the registration problems of different graphical objects or different imaging modalities [30][31][32]. Most of these algorithms focus on solving the optimization problem of finding an optimal spatial transformation that minimizes a certain similarity measure between the transformed template image and the reference image.…”

Section: Comparison With Registration-based Methodsmentioning

confidence: 99%

Abdominal atlas mapping in CT and MR volume images using a normalized abdominal coordinate system

Wang

Bai

Zhou

et al. 2008

Computerized Medical Imaging and Graphics

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“…Especially in clinical medicine, the image registration, such as computed tomography (CT), single-photon emission computed tomography (SPECT) and positron emission computed tomography (PET), is a basic tool for imaging applications in diagnosis [3]. In diagnosis of lung disease, registration of lung images is increasingly used [4,5].…”

Section: Introductionmentioning

confidence: 99%

Accurate Non-rigid Registration of Lung Images Based on Mutual Information

Cheng¹,

Cui²

2015

Nano BioMed ENG

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In clinical diagnosis of lung disease, registration of lung images from different imaging systems can provide a multi-informative image and improve its diagnostic accuracy. In this study, the original lung images were obtained from computed tomography (CT) and single-photon emission computed tomography (SPECT). After the decomposition of the images using wavelet transform, a non-rigid registration were proposed, in which CT image was used as the reference image and SPECT image was used as the floating image. In registration, the mutual information between the reference and floating images was calculated in the process of translation, rotation and elastic transformation. At last, the result of the registration was evaluated by the edges of the bone, muscle, thorax and lung tissues in CT and SPECT images. It showed an accuracy registration between lung CT and SPECT images.

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A Survey on Evaluation Methods for Medical Image Registration

Cited by 10 publications

References 4 publications

Patient-specific biomechanical model as whole-body CT image registration tool

Patient-specific biomechanical model as whole-body CT image registration tool

Abdominal atlas mapping in CT and MR volume images using a normalized abdominal coordinate system

Accurate Non-rigid Registration of Lung Images Based on Mutual Information

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