Disclosed are systems and methods for clinical trial assessment of skin disease treatment. The disclosure includes obtaining a series of digital images over a period of time, wherein each digital image includes an affected area of the subject; identifying characteristic morphologies and lesions in the affected area of the subject in each of the digital images; classifying each of the detected and segmented morphologies and lesions into one or more identified categories for each of the digital images; assigning a global score to each of the digital images based on a count of the detected and segmented characteristic morphologies and lesions in each of the one or more identified categories; analyzing the global scores of each of the digital images; and making an assessment of the clinical trial based on the analysis of the global scores of each of the digital images.

An image processing apparatus includes a correction unit configured to correct first image data acquired via an image pickup optical system and to generate second image data using a filter generated based on a characteristic of the image pickup optical system, a decomposition unit configured to decompose each of the first image data and the second image data into a first frequency component and a second frequency component, a combination unit configured to combine the first frequency component of the first image data and the first frequency component of the second image data with each other, and a generation unit configured to generate third image data based on a frequency component including the first frequency component combined by the combination unit and the second frequency component of the second image data.

The present invention relates to an image analysis method for providing information for supporting illness development prediction regarding a neoplasm in a human or animal body. The method includes receiving for the neoplasm first and second image data at a first and second moment in time, and deriving for a plurality of image features a first and a second image feature parameter value from the first and second image data. These feature parameter values being a quantitative representation of a respective image feature. Further, calculating an image feature difference value by calculating a difference between the first and second image feature parameter value, and based on a prediction model deriving a predictive value associated with the neoplasm for supporting treatment thereof. The prediction model includes a plurality of multiplier values associated with image features. For calculating the predictive value the method includes multiplying each image feature difference value with its associated multiplier value and combining the multiplied image feature difference values.

The communication support device includes a position acquisition unit, an imaging unit, a storage, a category ranking setting unit, a counterpart detector, and a notification unit. The position acquisition unit acquires position information indicating a position of a user. The imaging unit captures an image of a surrounding environment of the user to acquire a captured image. The storage stores the counterpart database. In the counterpart database, an image of a counterpart and a category indicating a property of the counterpart are associated with the counterpart. The category ranking setting unit sets a priority to the category according to the position information acquired by the position acquisition unit. The counterpart detector detects a counterpart belonging to the category in the captured image in order of the priority set by the category ranking setting unit. The notification unit notifies the user of information regarding the counterpart detected by the counterpart detector.

Apparatuses and methods are disclosed for generating a quantitative indication of the degree of oiliness of skin. In exemplary embodiments, a difference image generated from parallel- and a cross-polarized images of an area of skin is subjected to an intensity thresholding operation. An oiliness metric is generated based on the average intensity of those pixels whose intensities do not exceed the threshold, and/or on the average intensity of those pixels whose intensities exceed the threshold. An indication based on the metric is generated and output.

In the present invention, subband signals are obtained by performing multiresolution decomposition on image data by using a broad-sense pinwheel framelet that is a set of an approximate filter with no orientation and a plurality of detail filters with respective orientations, and that has a degree. When an image is reconstructed by summing the obtained subband signals, the reconstructed image data is generated by attenuating or amplifying a subband signal corresponding to at least one of filters that have predetermined frequency characteristics and/or a predetermined orientation among the filters.

A multi-scale detail representation of an image is computed as a weighted sum of translation difference images. A denoising operator is applied to the translation difference images so that translation differences are modified as a function of an estimated local signal-to-noise ratio and at least one denoised center difference image at a specific scale is computed by combining denoised translation difference images at scale s or a finer scale. A denoised image is computed by applying a reconstruction algorithm to the denoised center difference images.

Separations or images relating to film or other fields may be registered using a variety of features, such as, for example: (1) correcting one or more film distortions; (2) automatically determining a transformation to reduce a film distortion; (3) applying multiple criteria of merit to a set of features to determine a set of features to use in determining a transformation; (4) determining transformations for areas in an image or a separation in a radial order; (5) comparing areas in images or separations by weighting feature pixels differently than non-feature pixels; (6) determining distortion values for transformations by applying a partial distortion measure and/or using a spiral search configuration; (7) determining transformations by using different sets of features to determine corresponding transformation parameters in an iterative manner; and (8) applying a feathering technique to neighboring areas within an image or separation.

A novel medical imaging system that is based on radio-wave signals at microwave frequencies and has unique properties. The system can be used for various diagnostic applications such as breast cancer detection, brain stroke detection, and assessment of internal bleeding (trauma emergencies).

A method includes receiving a stream of synthetic images from a synthetic vision system and receiving a stream of sensor images from at least one sensor. The method also includes performing, by a processing array including a plurality of parallel processing elements, one or more fusion operations on a particular synthetic image of the stream of the synthetic images and one or more particular sensor images of the stream of the sensor images. Upon performing one or more fusion operations on the particular synthetic image of the stream of the synthetic images and the one or more particular sensor images of the stream of sensor images, the method further includes outputting a combined image to a display for presentation to a user.