Various methods for the detection and enhanced visualization of a particular structure or pathology of interest in a human eye are discussed in the present disclosure. An example method to visualize a given pathology (e.g., CNV) in an eye includes collecting optical coherence tomography (OCT) image data of the eye from an OCT system. The OCT image data is segmented to identify two or more retinal layer boundaries located in the eye. The two or more retinal layer boundaries are located at different depth locations in the eye. One of the identified layer boundaries is moved and reshaped to optimize visualization of the pathology located between the identified layer boundaries. The optimized visualization is displayed or stored or for a further analysis thereof.

An image processing apparatus including a division unit configured to divide first image data having a first dynamic range into a plurality of regions, an obtaining unit configured to obtain distance information indicating a distance from a focal plane in each of the plurality of regions, a determining unit configured to determine a conversion characteristic of each of the plurality of regions based on the distance information, a conversion unit configured to convert each of the plurality of regions into second image data having a second dynamic range smaller than the first dynamic range by using the conversion characteristic determined by the determining unit, and a storage unit configured to store a first conversion characteristic and a second conversion characteristic that can be used for the conversion.

A time sequenced series of optical images of a patient is obtained at a rate faster than cardiac frequency, wherein the time sequenced series of images capture one or more physical properties of intrinsic contrast. A cross-correland signal from the patient is obtained. A cross-correlated wavelet transform analysis is applied to the time sequenced series of optical images to yield a spatiotemporal representation of cardiac frequency phenomena. The cross-correlated wavelet transform analysis comprises performing a wavelet transform on the time-sequenced series of optical images to obtain a wavelet transformed signal, cross-correlating the wavelet transformed signal with the cross-correland signal to obtain a cross-correlated signal, filtering the cross-correlated signal at cardiac frequency to obtain a filtered signal, and performing an inverse wavelet transform on the filtered signal to obtain a spatiotemporal representation of the time sequenced series of optical images. Images of the cardiac frequency phenomena are generated.

Disclosed herein are systems and methods for adjusting appearance of objects in medical images.

The present disclosure relates generally to medical imaging, and more specifically to enhancing medical images (e.g., images taken in low-light conditions) using machine-learning techniques. An exemplary method of obtaining an enhanced fluorescence medical image of a subject comprises: receiving a fluorescence medical image of the subject (e.g., NIR images); providing the fluorescence medical image to a generator of a trained generative adversarial network (GAN) model trained using a plurality of white light images; and obtaining, from the generator, the enhanced fluorescence medical image of the subject.

An electronic apparatus is disclosed. The electronic apparatus includes: a memory storing an input image, and a processor configured to: apply a filter to the input image to identify the input image as a plurality of areas, apply a first low-frequency filter to a first area among the plurality of areas, and apply a second low-frequency filter to a second area among the plurality of areas to perform downscaling, wherein a cut-off frequency of the second low-frequency filter is configured to be higher than a cut-off frequency of the first low-frequency filter.

The present invention provides a method, system and device for reflectional glare reduction in endoscope images. The reflective glare refers to clusters of congruent bright and often saturated image pixels caused by heterogenous surfaces of a tissue anatomy reflecting the illumination from a light source, which could be as annoying as distractive. Under automatic exposure control, in a scene of overexposure due to a reflection glare, the brightness of the glare is significantly higher than that of background image of tissue anatomy, and there is usually a gap between the two. Through mapping the pixel depth of the glare into the gap, the contrast between the glare and the background image is reduced, which improves the user experience of the endoscopy.

Systems and methods are disclosed for image signal processing. For example, methods may include accessing an image from an image sensor; detecting an object area on the image; classifying the object area on the image; applying a filter to an object area of the image to obtain a low-frequency component image and a high-frequency component image; determining a first enhanced image based on a weighted sum of the low-frequency component image and the high-frequency component image, where the high-frequency component image is weighted more than the low-frequency component image; determining a second enhanced image based on the first enhanced image and a tone mapping; and storing, displaying, or transmitting an output image based on the second enhanced image.

An apparatus includes a generation unit configured to generate shape information of an object in a captured image, a component acquisition unit configured to acquire an auxiliary light component representing intensity of an auxiliary light at each pixel of the captured image based on a light amount characteristic representing a light amount of the auxiliary light received by the object when the auxiliary light is emitted and the shape information of the object, a first correction unit configured to generate a first corrected image in which color of the captured image is corrected according to environmental light, a second correction unit configured to generate a second corrected image in which color of the captured image is corrected according to the auxiliary light, and a combining unit configured to combine the first corrected image and the second corrected image at a combination ratio calculated based on the auxiliary light component.

In a computer-implemented method for generating an image processing model that generates output data defining a stylized contrast image from a microscope image, model parameters of the image processing model are adjusted by optimizing at least one objective function using training data. The training data comprises microscope images as input data and contrast images, wherein the microscope images and the contrast images are generated by different microscopy techniques. In order for the output data to define a stylized contrast image, the objective function forces a detail reduction or the contrast images are detail-reduced contrast images with a level of detail that is lower than in the microscope images and higher than in binary images.