Abstract: In classifying images by machine learning, provided are an image classification method, device, and program for classifying the image from which the feature difference is hardly detected, in particular, classifying the interference fringe image of tear fluid layer by the dry eye types. The method includes a step of acquiring a feature value from an interference fringe image of tear fluid layer for learning, a step of constructing a model for classifying an image from the feature value acquired from the interference fringe image of tear fluid layer for learning, a step of acquiring the feature value from an interference fringe image of tear fluid layer for testing, and a step of performing classification processing for classifying the interference fringe image of tear fluid layer for testing by types of dry eye using the model and the feature value acquired from the interference fringe image of tear fluid layer.

Abstract: An ophthalmic image of an evaluation target is acquired, a plurality of subsection images is extracted from the ophthalmic image, a state of a subject's eye is predicted for each of the subsection images based on a learned model in which learning has been performed in advance regarding extracting a plurality of subsection images from an ophthalmic image for learning, and predicting a state of a subject's eye for the each of subsection image by machine learning using correct answer data related to a state of each subsection image, and the subsection image is extracted from the ophthalmic image so as to have an image size corresponding to a state of a subject's eye of an evaluation target.

Abstract: Provided are a method, a computer program and a device for noninvasively evaluating a state of a tear fluid and a tear fluid amount of a tear meniscus. Included are a binarization step of binarizing a tear meniscus image, obtained by capturing at least a part of a tear meniscus of a subject, using a predetermined threshold value; an extraction step of extracting a high luminance region indicating a tear meniscus part from the binarized image; and an evaluation step of evaluating a tear fluid state on the basis of the high luminance region.

Abstract: In classifying images by machine learning, provided are an image classification method, device, and program for classifying the image from which the feature difference is hardly detected, in particular, classifying the interference fringe image of tear fluid layer by the dry eye types. The method includes a step of acquiring a feature value from an interference fringe image of tear fluid layer for learning, a step of constructing a model for classifying an image from the feature value acquired from the interference fringe image of tear fluid layer for learning, a step of acquiring the feature value from an interference fringe image of tear fluid layer for testing, and a step of performing classification processing for classifying the interference fringe image of tear fluid layer for testing by types of dry eye using the model and the feature value acquired from the interference fringe image of tear fluid layer.

Abstract: Provided are a method, a computer program and a device for noninvasively evaluating a state of a tear fluid and a tear fluid amount of a tear meniscus. Included are a binarization step of binarizing a tear meniscus image, obtained by capturing at least a part of a tear meniscus of a subject, using a predetermined threshold value; an extraction step of extracting a high luminance region indicating a tear meniscus part from the binarized image; and an evaluation step of evaluating a tear fluid state on the basis of the high luminance region.

Abstract: The present invention aims to provide an agent for treating meibomian gland dysfunction. The present invention provides an agent for treating meibomian gland dysfunction, which contains activated vitamin D3 or a derivative thereof as an active ingredient.

Abstract: The present invention aims to provide an agent for treating meibomian gland dysfunction. The present invention provides an agent for treating meibomian gland dysfunction, which contains activated vitamin D3 or a derivative thereof as an active ingredient.

Abstract: Ophthalmological image analysis techniques that can present objective information about the state of distribution of the Meibomian glands are provided. An ophthalmological image analyzer 1 comprises a designator 22, an extractor 23 and a calculator 24. The designator 22 is configured to designate an area A to be analyzed in a photographed image I of an eyelid of an eye. The extractor 23 is configured to extract Meibomian-gland subareas B that represent Meibomian glands in the eyelid, on the basis of the luminance value of each pixel in the area A being analyzed which is designated by the designator 22. The calculator 24 is configured to acquire distribution information of Meibomian glands in the area A being analyzed, on the basis of the Meibomian-gland subareas B.

Abstract: Ophthalmological image analysis techniques that can present objective information about the state of distribution of the Meibomian glands are provided. An ophthalmological image analyzer 1 comprises a designator 22, an extractor 23 and a calculator 24. The designator 22 is configured to designate an area A to be analyzed in a photographed image I of an eyelid of an eye. The extractor 23 is configured to extract Meibomian-gland subareas B that represent Meibomian glands in the eyelid, on the basis of the luminance value of each pixel in the area A being analyzed which is designated by the designator 22. The calculator 24 is configured to acquire distribution information of Meibomian glands in the area A being analyzed, on the basis of the Meibomian-gland subareas B.

Abstract: The present invention relates to a device for observing the meibomian glands enables a ready observation necessary for examination without a direct contact of an examination probe to the eyelid and an observation of all of the upper and lower meibomian glands in a relatively short time.