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Our approach is designed for adaptation to different imaging modalities, image features, and histological decision domains.
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Regardless of imaging modality, image stabilization is an important consideration during image analysis as motion artifacts decrease the image resolution and the accuracy of image quantification.
Given a source modality image, this approach first generates multiple target modality candidate values independently for each voxel by performing nearest neighbor search over the training database.
Furthermore, multi-modality image fusion means integrating the image that was collected from diversity medical equipment and obtain the more comprehensive and reliable image information [1].
The multi-modality image fusion can provide image that was combined with anatomical and physiological information for doctors and bring convenience for diagnosis.
A multi-modality image fusion can process images of certain organs or issues which were collected from diverse medical imaging equipment.
The multi-modality image fusion can process images of certain organs or issues which were collected from diverse medical imaging equipment.
Normalized correlation coefficient was used as the cost function metric since it was reported to perform slightly better (compared to mutual information) in intra-modality image coregistration [ 34 ].
This will enable dual-modality image reconstruction paradigms to be developed and tested prior to implementation of EEG-optical tomography to the study of neonatal seizure.
Lack of adequate and reliable tools for visualisation of multi-modality image sets in hospital PACS, which prevents hospital-wide reviewing and demonstration of image fusion, is another important issue.
Furthermore, current standard picture archiving and communication systems (PACS) do not provide adequate and reliable tools for visualisation of multi-modality image sets, which prevents hospital-wide reviewing and demonstration of image fusion.
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