Abstract: The invention is directed to a reduction in the influence on a pathological diagnosis when an image reading apparatus is exchanged. In order to solve the above-mentioned problem, there is provided an image processing apparatus that processes a biological image generated by capturing an image of a biological specimen in an image reading apparatus, including: an acquisition unit that acquires identification information of the image reading apparatus; a determination unit that determines a type of biological tissue included in the biological image; and a correction unit that adds a correction process to the biological image in correspondence with the identification information of the image reading apparatus and the type of biological tissue.

Abstract: The pathological diagnosis results assessment system includes: a diagnosis unit to carry out pathological diagnosis of tissue specimen images, and generates diagnosis record information; a report storage unit to store reports in which pathological diagnosis results for the tissue specimen images are described; a report analysis unit to analyze the diagnosis results described in the reports stored in the report storage unit; and a report verification unit to compare the diagnosis result analyzed by the report analysis unit to the diagnosis record information, and determine a degree of matching on the diagnosis degree of the comparison result.

Abstract: This invention relates to an information processing apparatus which evaluates diagnosis based on the tissue sample image of a tissue. The information processing apparatus inputs a plurality of first regions selected as diagnosis targets from a tissue sample image obtained by capturing a tissue, a plurality of second regions selected as diagnosis targets from the tissue sample image, and pieces of position information of the respective regions selected on the tissue sample image. The information processing apparatus calculates a similarity between the plurality of first regions and the plurality of second regions based on correlations considering the distances between the selected regions on the tissue sample image. This arrangement can evaluate whether ROIs selected by a pathologist or apparatus include an important region of interest.

Abstract: The image diagnostic apparatus includes: acquisition means 11 for acquiring data on an image having a first resolution, which is generated by imaging a diagnostic object; conversion means 12 for converting the resolution of the image into a second resolution which is lower than the first resolution; extraction means 13 for analyzing the image of which resolution has been converted into the second resolution and extracting an ROI of the diagnostic object; determination means 14 for determining whether or not a characteristic region which indicates a predetermined image characteristic exists in the ROI extracted by the extraction means 13; and creation means 15 for creating, when the characteristic region exists, an output image by converting the resolution so that the characteristic region is confined to an image size to be output, and occupies the image by a predetermined ratio or more.

Abstract: The pathological diagnosis results assessment system includes: a diagnosis unit to carry out pathological diagnosis of tissue specimen images, and generates diagnosis record in information; a report storage unit to store reports in which pathological diagnosis results for the tissue specimen images are described; a report analysis unit to analyze the diagnosis results described in the reports stored in the report storage unit; and a report, verification unit to compare the diagnosis result analyzed by the report analysis unit to the diagnosis record information, and determine a degree of matching on the diagnosis degree of the comparison result.

Abstract: A diagnostic imaging support device includes an image data acquisition unit that acquires image data of high magnification of sample tissue, an image classifying unit that generates image data of low magnification from the image data of high magnification acquired by the image data acquisition unit, and classifies the generated image data of low magnification into group by each image data pattern of a plurality of pathological tissue, and an image evaluating unit that evaluates whether or not the image data of high magnification, which is to be a base of the image data of low magnification classified by the image classifying unit, is the pathological tissue of the classified group.

Abstract: A pathological diagnosis support apparatus into which a digital color image showing a stained tissue sample is input, the apparatus including: a display that performs a display operation; and an image processor that when the digital color image is input, extracts cell nucleus areas, cytoplasm areas and glandular cavity areas, respectively, based on luminance values of pixels of the digital color image, measures basic feature quantities representing features of shapes of the respective cell nucleus areas, cytoplasm areas and glandular cavity areas, which have been extracted, determines whether or not a particular kind of area that appears in a limited case according to the disease state of the tissue sample exists, for each of the cell nucleus areas, the cytoplasm areas and the glandular cavity areas based on the luminance values and the basic feature quantities, and measures structure feature quantities representing ways in which the cell nucleus areas are positioned, based on the basic feature quantities of

Abstract: A method for training a classifier to be operative as an epithelial texture classifier, includes obtaining a plurality of training micrograph areas of biopsy tissue and for each of the training micrograph areas, identifying probable locations of nuclei that form epithelia, generating a skeleton graph from the probable locations of the nuclei that form the epithelia, manually drawing walls on the skeleton graph outside of the epithelia to divide the epithelia from one another, and manually selecting points that lie entirely inside the epithelia to generate open and/or closed geodesic paths in the skeleton graph between pairs of the selected points. Data is obtained from points selected from the walls and the paths and applied to a classifier to train the classifier as the epithelial texture classifier.

Abstract: This invention relates to an information processing apparatus which evaluates diagnosis based on the tissue sample image of a tissue. The information processing apparatus inputs a plurality of first regions selected as diagnosis targets from a tissue sample image obtained by capturing a tissue, a plurality of second regions selected as diagnosis targets from the tissue sample image, and pieces of position information of the respective regions selected on the tissue sample image. The information processing apparatus calculates a similarity between the plurality of first regions and the plurality of second regions based on correlations considering the distances between the selected regions on the tissue sample image. This arrangement can evaluate whether ROIs selected by a pathologist or apparatus include an important region of interest.

Abstract: Pathologic tissue images are to be output which allow effective image diagnosis to be performed. There are included an index amount calculating unit that divides a captured pathologic image into a predetermined divisional areas and sets a measurement index value related to a reference of pathologic diagnosis for each divisional area; and a significance calculating module that sets, for each divisional area, a significance related to the pathologic measurement on the basis of both the measurement index value of each divisional area and the measurement index value of a respective divisional area adjacent to that divisional area. The significance calculating module extracts that one of the divisional areas in which the areas for which images of the living tissues have been captured represent a given percentage, and further the significance calculating module associates, with the extracted divisional area, the measurement index value and significance set therefor for transmission.

Abstract: To extract a feature advantageous for classification and correlation by using the information difficult to be acquired even when it is impossible to acquire the information difficult-to-be-acquired from all individuals. Sub-information input device inputs information difficult to be acquired and accumulates the inputted sub-information. Main information input device inputs information easy to be acquired as main information, and accumulates the inputted main information. Sub-information selection device evaluates a category attribution degree of each sub-information accumulated and selects the sub-information of a high category attribution degree. The correlation feature extraction device uses the sub-information selected by the sub-information selection device as the feature extraction filter, and extracts a feature corresponding to the main information from a correlation between the main information and the sub-information.

Abstract: The image diagnostic apparatus includes: acquisition means 11 for acquiring data on an image having a first resolution, which is generated by imaging a diagnostic object; conversion means 12 for converting the resolution of the image into a second resolution which is lower than the first resolution; extraction means 13 for analyzing the image of which resolution has been converted into the second resolution and extracting an ROI of the diagnostic object; determination means 14 for determining whether or not a characteristic region which indicates a predetermined image characteristic exists in the ROI extracted by the extraction means 13; and creation means 15 for creating, when the characteristic region exists, an output image by converting the resolution so that the characteristic region is confined to an image size to be output, and occupies the image by a predetermined ratio or more.

Abstract: The invention is directed to a reduction in the influence on a pathological diagnosis when an image reading apparatus is exchanged. In order to solve the above-mentioned problem, there is provided an image processing apparatus that processes a biological image generated by capturing an image of a biological specimen in an image reading apparatus, including: an acquisition unit that acquires identification information of the image reading apparatus; a determination unit that determines a type of biological tissue included in the biological image; and a correction unit that adds a correction process to the biological image in correspondence with the identification information of the image reading apparatus and the type of biological tissue.

Abstract: Pathologic tissue images are to be output which allow effective image diagnosis to be performed. There are included an index amount calculating unit that divides a captured pathologic image into a predetermined divisional areas and sets a measurement index value related to a reference of pathologic diagnosis for each divisional area; and a significance calculating module that sets, for each divisional area, a significance related to the pathologic measurement on the basis of both the measurement index value of each divisional area and the measurement index value of a respective divisional area adjacent to that divisional area. The significance calculating module extracts that one of the divisional areas in which the areas for which images of the living tissues have been captured represent a given percentage, and further the significance calculating module associates, with the extracted divisional area, the measurement index value and significance set therefor for transmission.

Abstract: A diagnostic imaging support device includes an image data acquisition unit that acquires image data of high magnification of sample tissue, an image classifying unit that generates image data of low magnification from the image data of high magnification acquired by the image data acquisition unit, and classifies the generated image data of low magnification into group by each image data pattern of a plurality of pathological tissue, and an image evaluating unit that evaluates whether or not the image data of high magnification, which is to be a base of the image data of low magnification classified by the image classifying unit, is the pathological tissue of the classified group.

Abstract: A method for training a classifier to be operative as an epithelial texture classifier, includes obtaining a plurality of training micrograph areas of biopsy tissue and for each of the training micrograph areas, identifying probable locations of nuclei that form epithelia, generating a skeleton graph from the probable locations of the nuclei that form the epithelia, manually drawing walls on the skeleton graph outside of the epithelia to divide the epithelia from one another, and manually selecting points that lie entirely inside the epithelia to generate open and/or closed geodesic paths in the skeleton graph between pairs of the selected points. Data is obtained from points selected from the walls and the paths and applied to a classifier to train the classifier as the epithelial texture classifier.

Abstract: A face image synthesis apparatus includes a storage device (2) and an arithmetic processing unit (1). The storage device (2) stores a plurality of face images and a plurality of feature points including an origin. The plurality of feature points are set to each of the plurality of face images. The arithmetic processing unit (1) generates a plurality of deformed face images by deforming the plurality of face images so that the origin and the other feature points thereof are matched to each other and generates a synthesized face image from the deformed face images.

Abstract: A 3D shape estimation system has a storage device, a relative shape analysis module, a feature point location search module and an absolute shape analysis module. The storage device stores first and second learning data which represent illumination bases and 3D shapes of objects, respectively. The relative shape analysis module calculates an “illumination basis” of an object based on a 2D image of the object and the first learning data, calculates a “relative shape function” that is partial differential of a “shape function” indicating a 3D shape of the object from the illumination basis, and outputs a relative shape data indicating the relative shape function. The feature point location search module extracts a plurality of feature points from the input 2D face image based on the 2D image and the relative shape data, and outputs a feature point location data indicating locations of the feature points.

Abstract: A shape deformation encoding device includes: a calculation unit for calculating a difference data between a pre-deformation shape data which represents a shape of an object before shape change and a post-deformation shape data which represents a shape of the object after shape change; and a determination unit for determining an action area on which the shape change of the object is arisen and an external force which is acted to the action area for the shape change, based on the pre-deformation shape data and the difference data. The action area includes a plurality of small areas, each of the plurality of small areas includes a plurality of control points, and the external force is calculated as an external force required to deform the plurality of small areas from before to after the shape change independently based on the physical model structure of each of the plurality of small areas.

Abstract: A pathological diagnosis support apparatus into which a digital color image showing a stained tissue sample is input, the apparatus including: a display that performs a display operation; and an image processor that when the digital color image is input, extracts cell nucleus areas, cytoplasm areas and glandular cavity areas, respectively, based on luminance values of pixels of the digital color image, measures basic feature quantities representing features of shapes of the respective cell nucleus areas, cytoplasm areas and glandular cavity areas, which have been extracted, determines whether or not a particular kind of area that appears in a limited case according to the disease state of the tissue sample exists, for each of the cell nucleus areas, the cytoplasm areas and the glandular cavity areas based on the luminance values and the basic feature quantities, and measures structure feature quantities representing ways in which the cell nucleus areas are positioned, based on the basic feature quantities of