A method of visualization, characterization, and detection of objects within an image by applying a local micro-contrast convergence algorithm to a first image to produce a second image that is different from the first image, wherein all like objects converge into similar patterns or colors in the second image.

Systems, methods, and computer readable media for determining cognitive impairment (CI) in patients are provided herein. Various regional structural-functional parameters of the retina can serve as biomarkers for the detection of CI. The method can include forming a database including a quantification of retinal structure and retinal function of a plurality of eyes associated with a plurality of patients, providing a baseline cognitive impairment (CI) reference. The method can include determining a measure of functionality of neurons in the retina based on an electroretinogram (ERG) of a patient. The method can include determining a structural measure of the first retina based on a generalized dimension spectrum and singularity spectrum of the skeletonized retinal image, and a lacunarity parameter of the skeletonized retinal image. The method can include determining a level of cognitive impairment based on the structural and functional measures.

The present invention relates to a method for characterizing perfusion abnormalities in tissue by means of fractal analysis (FA) of at least one part of an interface region between adequately and abnormally perfused tissue comprising the steps of providing an imaging dataset of perfusion imaging; wherein said imaging dataset visualizes the at least one part of the interface region; optional pre-processing of said imaging dataset; applying fractal analysis to the imaging dataset; wherein said fractal analysis provides at least one fractal parameter, preferably fractal dimension (FD), of the at least one part of the interface region.

The present invention relates to a method for characterizing perfusion abnormalities in tissue by means of fractal analysis (FA) of at least one part of an interface region between adequately and abnormally perfused tissue comprising the steps of providing an imaging dataset of perfusion imaging; wherein said imaging dataset visualizes the at least one part of the interface region; optional pre-processing of said imaging dataset; applying fractal analysis to the imaging dataset; wherein said fractal analysis provides at least one fractal parameter, preferably fractal dimension (FD), of the at least one part of the interface region.

Methods are herein provided for decision support in diagnosis of a disease in a subject, and for extracting features from a multi-slice data set. Systems for computer-aided diagnosis are provided. The systems take as input a plurality of medical data and produces as output a diagnosis based upon this data. The inputs may consist of a combination of image data and clinical data. Diagnosis is performed through feature selection and the use of one or more classifier algorithms.

Methods are herein provided for decision support in diagnosis of a disease in a subject, and for extracting features from a multi-slice data set. Systems for computer-aided diagnosis are provided. The systems take as input a plurality of medical data and produces as output a diagnosis based upon this data. The inputs may consist of a combination of image data and clinical data. Diagnosis is performed through feature selection and the use of one or more classifier algorithms.

Methods are herein provided for decision support in diagnosis of a disease in a subject, and for extracting features from a multi-slice data set. Systems for computer-aided diagnosis are provided. The systems take as input a plurality of medical data and produces as output a diagnosis based upon this data. The inputs may consist of a combination of image data and clinical data. Diagnosis is performed through feature selection and the use of one or more classifier algorithms.

Methods are herein provided for decision support in diagnosis of a disease in a subject, and for extracting features from a multi-slice data set. Systems for computer-aided diagnosis are provided. The systems take as input a plurality of medical data and produces as output a diagnosis based upon this data. The inputs may consist of a combination of image data and clinical data. Diagnosis is performed through feature selection and the use of one or more classifier algorithms.

A measurement system (301) for determining surface roughness is shown. A coherent illumination device (303) illuminates the surface of, for example, a component (201) with coherent light. An imaging device (304) obtains an image of speckle caused by the scattering of the coherent light from the surface. A processing device (305) converts the image into a binary image according to a threshold, thereby classifying pixels below the threshold as background pixels and pixels above the threshold as foreground pixels. It then evaluates the fractal dimension of the binary image. The fractal dimension correlates with surface roughness. An indication of the surface roughness of the surface is then outputted.

A measurement system (301) for determining surface roughness is shown. A coherent illumination device (303) illuminates the surface of, for example, a component (201) with coherent light. An imaging device (304) obtains an image of speckle caused by the scattering of the coherent light from the surface. A processing device (305) converts the image into a binary image according to a threshold, thereby classifying pixels below the threshold as background pixels and pixels above the threshold as foreground pixels. It then evaluates the fractal dimension of the binary image. The fractal dimension correlates with surface roughness. An indication of the surface roughness of the surface is then outputted.