Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: 1) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; 2) longitudinal studies, where temporal structural or anatomical changes are investigated; and 3) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner.

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Deformable Medical Image Registration: A Survey

[서평]「Component Software」 - Beyond Object-Oriented Programming -

Computerized Image Registration approaches can offer automatic and accurate image alignments without extensive user involvement and provide tools for visualizing combined images. The aim of this survey is to present a review of publications related to Medical Image Registration. This paper paints a comprehensive picture of image registration methods and their applications. This paper is an introduction for those new to the ﬁeld, an overview for those working in the ﬁeld and a reference for those searching for literature on a speciﬁc application. Methods are classiﬁed according to the diﬀerent aspects of Medical Image Registration.

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Survey of Medical Image Registration

Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multi-modal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. This review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion. We characterize the medical image fusion research based on (1) the widely used image fusion methods, (2) imaging modalities, and (3) imaging of organs that are under study. This review concludes that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.

Medical Image Fusion: A survey of the state of the art

Medical image fusion: A survey of the state of the art

Our 3D ultrasound computer tomograph (USCT) is an device method aimed at early breast cancer diagnosis. It is capable of producing images with sub-millimeter resolution and high signal-to-noise ratio. This method is universally useable for the 3D analysis of sufficient small bodies similar to the size of a breast, which may be immersed in a liquid coupling medium. In this paper, an overview of the developed method and the first results for static test examples (phantoms) and the perspective of our 3D-USCT at Forschungszentrum Karlsruhe are presented.

3D ultrasound computer tomography for medical imaging

We present a new approach for automatic registration of X-ray mammograms and MR images. Multimodal breast cancer diagnosis is supported by automatic localization of small lesions, which are only visible in the mammograms or in the MR image. To cope with the huge deformation of the breast during mammography, a finite element model of the deformable behavior of the breast is applied during the registration. An evaluation of the registration with six clinical data sets resulted in an accurate localization with a mean displacement of 4.3 mm (/spl plusmn/1 mm) and 3.9 mm (/spl plusmn/1.7 mm) for predicting the lesion position in mammograms and in the MR images, respectively.

Model-based registration of X-ray mammograms and MR images of the female breast

Ultrasound is frequently used to evaluate suspicious masses in breasts. These evaluations could be improved by taking advantage of advanced imaging algorithms, which become feasible for low frequencies if accurate knowledge about the phase and amplitude of the wave field illuminating the volume of interest is available. In this study, we compare five imaging and inversion methods: time-of-flight tomography, synthetic aperture focusing technique, backpropagation, Born inversion, and contrast source inversion. All methods are tested on the same full-wave synthetic data representing a 2-D scan using a circular array enclosing a cancerous breast submerged in water. Of the tested methods, only contrast source inversion yielded an accurate reconstruction of the speed-ofsound profile of the tumor and its surroundings, because only this method takes effects such as multiple scattering, refraction, and diffraction into account.

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Comparing different ultrasound imaging methods for breast cancer detection

Automatic multimodal 2D/3D breast image registration using biomechanical FEM models and intensity-based optimization

The paper is focused on sound-speed image reconstruction in 3-D ultrasound transmission tomography. Along with ultrasound reflectivity and the attenuation coefficient, sound speed is an important parameter which is related to the type and pathological state of the imaged tissue. This is important in the intended application, breast cancer diagnosis. In contrast to 2-D ultrasound transmission tomography systems, a 3-D system can provide an isotropic spatial resolution in the x-, y-, and z-directions in reconstructed 3-D images of ultrasound parameters. Several challenges must, however, be addressed for 3-D systems-namely, a sparse transducer distribution, low signal-to-noise ratio, and higher computational complexity. These issues are addressed in terms of sound-speed image reconstruction, using edge-preserving regularized algebraic reconstruction in combination with synthetic aperture focusing. The critical points of the implementation are also discussed, because they are crucial to enable a complete 3-D image reconstruction. The methods were tested on a synthetic data set and on data sets measured with the Karlsruhe 3-D ultrasound computer tomography (USCT) I prototype using phantoms. The sound-speed estimates in the reconstructed volumes agreed with the reference values. The breast-phantom outlines and the lesion-mimicking objects were also detectable in the resulting sound-speed volumes.

Nicole V. Ruiter

Papers

3D ultrasound computer tomography for medical imaging

Model-based registration of X-ray mammograms and MR images of the female breast

Comparing different ultrasound imaging methods for breast cancer detection

Automatic multimodal 2D/3D breast image registration using biomechanical FEM models and intensity-based optimization

Sound-speed image reconstruction in sparse-aperture 3-D ultrasound transmission tomography