Abstract: The current document is directed to digital-image-normalization methods and systems that generate accurate intensity mappings between the intensities in two digital images. The intensity mapping generated from two digital images is used to normalize or adjust the intensities in one image in order to produce a pair of normalized digital images to which various types of change-detection methodologies can be applied in order to extract differential data. Accurate intensity mappings facilitate accurate and robust normalization of sets of multiple digital images which, in turn, facilitates many additional types of operations carried out on sets of multiple normalized digital images, including change detection, quantitative enhancement, synthetic enhancement, and additional types of digital-image processing, including processing to remove artifacts and noise from digital images.

Abstract: The current document is directed to digital-image-normalization methods and systems that generate accurate intensity mappings between the intensities in two digital images. The intensity mapping generated from two digital images is used to normalize or adjust the intensities in one image in order to produce a pair of normalized digital images to which various types of change-detection methodologies can be applied in order to extract differential data. Accurate intensity mappings facilitate accurate and robust normalization of sets of multiple digital images which, in turn, facilitates many additional types of operations carried out on sets of multiple normalized digital images, including change detection, quantitative enhancement, synthetic enhancement, and additional types of digital-image processing, including processing to remove artifacts and noise from digital images.

Abstract: The current document is directed to digital-image-normalization methods and systems that generate accurate intensity mappings between the intensities in two digital images. The intensity mapping generated from two digital images is used to normalize or adjust the intensities in one image in order to produce a pair of normalized digital images to which various types of change-detection methodologies can be applied in order to extract differential data. Accurate intensity mappings facilitate accurate and robust normalization of sets of multiple digital images which, in turn, facilitates many additional types of operations carried out on sets of multiple normalized digital images, including change detection, quantitative enhancement, synthetic enhancement, and additional types of digital-image processing, including processing to remove artifacts and noise from digital images.

Abstract: The current document is directed to digital-image-normalization methods and systems that generate accurate intensity mappings between the intensities in two digital images. The intensity mapping generated from two digital images is used to normalize or adjust the intensities in one image in order to produce a pair of normalized digital images to which various types of change-detection methodologies can be applied in order to extract differential data. Accurate intensity mappings facilitate accurate and robust normalization of sets of multiple digital images which, in turn, facilitates many additional types of operations carried out on sets of multiple normalized digital images, including change detection, quantitative enhancement, synthetic enhancement, and additional types of digital-image processing, including processing to remove artifacts and noise from digital images.

Abstract: The current document is directed to methods and systems that refine anatomical models to sub-voxel resolution. In certain implementations, sophisticated, composite, digital anatomical atlases provide detailed three-dimensional models of the contents of three-dimensional medical images. However, three-dimensional medical images have limited resolutions characterized by a smallest volume, referred to as a voxel, to which an intensity is assigned by the imaging process. The currently disclosed methods employ computed percentages of different types of tissue within voxel volumes to adjust a three-dimensional model of the contents of the voxel volumes to more accurately model the contents of the voxel volumes.

Abstract: The current document is directed to digital-image-normalization methods and systems that generate accurate intensity mappings between the intensities in two digital images. The intensity mapping generated from two digital images is used to normalize or adjust the intensities in one image in order to produce a pair of normalized digital images to which various types of change-detection methodologies can be applied in order to extract differential data. In one approach, a mapping model is selected to provide a basis for statistically meaningful intensity normalization. In this implementation, a genetic optimization approach is used to determine and refine model parameters. The implementation produces a hybrid intensity mapping that includes both intensity mappings calculated by application of the mapping model and intensity mappings obtained directly from comparison of the images.

Abstract: The current document is directed to digital-image-normalization methods and systems that generate accurate intensity mappings between the intensities in two digital images. The intensity mapping generated from two digital images is used to normalize or adjust the intensities in one image in order to produce a pair of normalized digital images to which various types of change-detection methodologies can be applied in order to extract differential data. Accurate intensity mappings facilitate accurate and robust normalization of sets of multiple digital images which, in turn, facilitates many additional types of operations carried out on sets of multiple normalized digital images, including change detection, quantitative enhancement, synthetic enhancement, and additional types of digital-image processing, including processing to remove artifacts and noise from digital images.

Abstract: The current document is directed to methods and systems that refine anatomical models to sub-voxel resolution. In certain implementations, sophisticated, composite, digital anatomical atlases provide detailed three-dimensional models of the contents of three-dimensional medical images. However, three-dimensional medical images have limited resolutions characterized by a smallest volume, referred to as a voxel, to which an intensity is assigned by the imaging process. The currently disclosed methods employ computed percentages of different types of tissue within voxel volumes to adjust a three-dimensional model of the contents of the voxel volumes to more accurately model the contents of the voxel volumes.

Abstract: The current document is directed to methods and systems that overcome the image-comparison problems attendant with the human visual system by leveraging the motion-detection capabilities of the human visual system. Despite our visual system lacking automatic image-difference detection, our visual system does have an inherent ability to detect motion within our visual field and to direct our attention to this motion. This ability probably evolved as a result of the advantage provided by rapid detection of changes in our environment, including detection of the movement of predators or other threatening creatures, such as a snake in the bushes next to the campfire where we are eating. This method, usually referred to as “Flicker,” involves placing two images to be compared in the same location and rapidly alternating between them.

Abstract: The current document is directed to digital-image-normalization methods and systems that generate accurate intensity mappings between the intensities in two digital images. The intensity mapping generated from two digital images is used to normalize or adjust the intensities in one image in order to produce a pair of normalized digital images to which various types of change-detection methodologies can be applied in order to extract differential data. In one approach, a mapping model is selected to provide a basis for statistically meaningful intensity normalization. In this implementation, a genetic optimization approach is used to determine and refine model parameters. The implementation produces a hybrid intensity mapping that includes both intensity mappings calculated by application of the mapping model and intensity mappings obtained directly from comparison of the images.

Abstract: The current document is directed to digital-image-normalization methods and systems that generate accurate intensity mappings between the intensities in two digital images. The intensity mapping generated from two digital images is used to normalize or adjust the intensities in one image in order to produce a pair of normalized digital images to which various types of change-detection methodologies can be applied in order to extract differential data. In one approach, a mapping model is selected to provide a basis for statistically meaningful intensity normalization. In this implementation, a genetic optimization approach is used to determine and refine model parameters. The implementation produces a hybrid intensity mapping that includes both intensity mappings calculated by application of the mapping model and intensity mappings obtained directly from comparison of the images.

Abstract: The current document is directed to digital-image-normalization methods and systems that generate accurate intensity mappings between the intensities in two digital images. The intensity mapping generated from two digital images is used to normalize or adjust the intensities in one image in order to produce a pair of normalized digital images to which various types of change-detection methodologies can be applied in order to extract differential data. In one approach, a mapping model is selected to provide a basis for statistically meaningful intensity normalization. In this implementation, a genetic optimization approach is used to determine and refine model parameters. The implementation produces a hybrid intensity mapping that includes both intensity mappings calculated by application of the mapping model and intensity mappings obtained directly from comparison of the images.