Medical image registration: a review

Oliveira, Francisco P. M.; Tavares, João Manuel R. S.

doi:10.1080/10255842.2012.670855

Cited by 588 publications

(350 citation statements)

References 218 publications

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“…al [40] proposed a method that assumes nonrigid lung deformation and rigid tumor. They use the B-Spline-based nonrigid transformation to model the lung deformation while imposing rigid transformation on the tumor to preserve its volume and shape [104,105,106]. A 2D graph-cut algorithm is used to segment a 3D dataset.…”

Section: Selected Workmentioning

confidence: 99%

Automatic 3D pulmonary nodule detection in CT images: A survey

Valente

Cortez

Neto

et al. 2016

Computer Methods and Programs in Biomedicine

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198

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This work presents a systematic review of techniques for the 3D automatic detection of pulmonary nodules in computerized-tomography (CT) images.Its main goals are to analyze the latest technology being used for the development of computational diagnostic tools to assist in the acquisition, storage and, mainly, processing and analysis of the biomedical data. Also, this work identifies the progress made, so far, evaluates the challenges to be overcome and provides an analysis of future prospects. As far as the authors know, this is the first time that a review is devoted exclusively to automated 3D techniques for the detection of pulmonary nodules from lung CT images, which makes this work of noteworthy value. The research covered the published works in the Web of Science, PubMed, Science Direct and IEEEXplore up to December 2014. Each work found that referred to automated 3D segmentation of the lungs was individually analyzed to identify its objective, methodology and results. Based on the analysis of the selected works, several studies were seen to be useful for the construction of medical diagnostic * Corresponding author. E-mail: tavares@fe.up.pt. 31, 2015 aid tools. However, there are certain aspects that still require attention such as increasing algorithm sensitivity, reducing the number of false positives, improving and optimizing the algorithm detection of different kinds of nodules with different sizes and shapes and, finally, the ability to integrate with the Electronic Medical Record Systems and Picture Archiving and Communication Systems. Based on this analysis, we can say that further research is needed to develop current techniques and that new algorithms are needed to overcome the identified drawbacks. Preprint submitted to Computer Methods and Programs in Biomedicine August

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Section: Selected Workmentioning

confidence: 99%

Automatic 3D pulmonary nodule detection in CT images: A survey

Valente

Cortez

Neto

et al. 2016

Computer Methods and Programs in Biomedicine

Self Cite

198

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“…Specially, PET [330] and MRI [331] modalities are affected by this type of distortions, due to hardware imperfections, motion of subjects and to the intrinsic physics behind the image acquisition and reconstruction process. A throughout review of medical image registration techniques was recently published by Oliveira and Tavares [332].…”

Section: Standard Methods For Medical Imaging Analysismentioning

confidence: 99%

On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey

Alberdi

Aztiria

Basarab

2016

Artificial Intelligence in Medicine

149

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“…In recent years, the use of intensity-based registration methods has been growing compared to the feature-based methods [16], and this trend can be seen in the methods implemented in the tools reviewed in this paper. However, the most recent developments and the newer registration methods may not always be implemented in publicly available tools.…”

Section: Discussionmentioning

confidence: 99%

“…Medical image registration methods in general have been reviewed by several authors [12][13][14][15], most recently by Oliveira & Tavares [16]. Indeed, most reviews focus on algorithms, modalities involved, and the characteristics of the registration task.…”

Section: Introductionmentioning

confidence: 99%

Survey of Non-Rigid Registration Tools in Medicine

2016

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We catalogue available software solutions for non-rigid image registration to support scientists in selecting suitable tools for specific medical registration purposes. Registration tools were identified using nonsystematic search in Pubmed, Web of Science, IEEE Xplore® Digital Library, Google Scholar, and through references in identified sources (n = 22). Exclusions are due to unavailability or inappropriateness. The remaining (n = 18) tools were classified by (i) access and technology, (ii) interfaces and application, (iii) living community, (iv) supported file formats, and (v) types of registration methodologies emphasizing the similarity measures implemented. Out of the 18 tools, (i) 12 are open source, 8 are released under a permissive free license, which imposes the least restrictions on the use and further development of the tool, 8 provide graphical processing unit (GPU) support; (ii) 7 are built on software platforms, 5 were developed for brain image registration; (iii) 6 are under active development but only 3 have had their last update in 2015 or 2016; (iv) 16 support the Analyze format, while 7 file formats can be read with only one of the tools; and (v) 6 provide multiple registration methods and 6 provide landmark-based registration methods. Based on open source, licensing, GPU support, active community, several file formats, algorithms, and similarity measures, the tools Elastics and Plastimatch are chosen for the platform ITK and without platform requirements, respectively. Researchers in medical image analysis already have a large choice of registration tools freely available. However, the most recently published algorithms may not be included in the tools, yet.

show abstract

Medical image registration: a review

Cited by 588 publications

References 218 publications

Automatic 3D pulmonary nodule detection in CT images: A survey

Automatic 3D pulmonary nodule detection in CT images: A survey

On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey

Survey of Non-Rigid Registration Tools in Medicine

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