Screen Holdings Co., Ltd.

Dainippon Screen Mfg. Co., Ltd.

Kyoto Prefectural Public University Corporation

Hiroshi Ogi

Tomoyasu Furuta

Saya Shibata

Hiroshi Asai

Hiroyuki Onishi

Sanzo Moriwaki

Maki Hirai

Shunta Ishihara

Takahiro Tsujikawa

Satoshi Takahashi

Kyoko Itoh

Yoshiki Umemoto

A threshold determination method includes a region specification step of specifying cell regions corresponding to individual cells in an image obtained by imaging the biological specimen, a determination step of setting temporarily a threshold for staining density quantitatively indicating the degree of staining of the cell region, comparing the staining density and the threshold for each of the cell regions and determining whether a stained state is positive or negative for staining, a receiving step of displaying a determination result of the stained state of each of the cell regions and receiving an operation input of a user to change the determination result for each cell region, and a resetting step of resetting the threshold in accordance with the determination result after change. It is possible to optimize effectively a threshold used in determining a stained state from an image of an immunohistostained specimen while human judgment is made.

Threshold determination method

An image processing using a plurality of specimen images obtained by successively applying a plurality of types of staining to a specimen to be evaluated and imaging the specimen after staining for at least two types of staining is performed. The image processing comprises: extracting an inner region corresponding to inward of a cell membrane of a single cell in the specimen based on at least one of the specimen images; specifying a cell region corresponding to an individual cell included in the specimen by expanding the inner region outwardly; and performing image cytometry for the cell region based on the specimen images. In analyzing a pathological specimen on a cell-by-cell basis, it is possible to deal with multiple immunostaining and specify the positions of individual cells and evaluate each cell separately even when a cell density in the specimen is high.

Specimen analysis method and image processing method

An image display method according to this invention includes obtaining an image set including a plurality of stained images, the plurality of stained images being obtained by imaging a multiple immunostained tissue specimen, a registration processing being performed for mutual registration for the plurality of stained images, and switching and displaying the stained images included in the image set on a screen in accordance with a predetermined switch rule with a result of the registration processing reflected. An image includes at least one of the stained images and a graphical user interface for receiving an operation input from a user for editing the switch rule being displayed on the screen. The switch rule is changed and set according to the operation input. The image is displayed on the screen in accordance with the changed and set switch rule.

Image display method and recording medium

An image processing apparatus includes an image acquirer for obtaining an original image of a pathological specimen, an information acquirer for obtaining finding information relating to pathological finding and an image generator for generating an output image including a processed image obtained by superimposing visual information based on the finding information on a source image. A relative density between the source image and the visual information is set according to an operation input from a user. Processing modes for generating the output image include a first mode for generating the output image in which the source image and the processed image are arranged on one screen and a second mode for generating the output image in which a wide range image at a relatively low magnification and an enlarged image representing a partial region in the wide range image at a higher magnification on one screen.

Image processing apparatus, image processing system and image processing method

As light is allowed to impinge from above a microplate M and an imaging unit(a line sensorand an imaging optical system), which moves in a scanning motion along the bottom surface of the microplate M, receives transmitted light, images of wells W formed in the microplate M are captured. The scope of imaging by the line sensoris greater than the diameter of at least one well W, or preferably, encompasses a plurality of wells. When the depth of field of the optical systemis 0.6 mm or less, the influence of reflection of side walls of the wells upon the images is reduced.

Imaging apparatus, detecting apparatus, and imaging method

Light is allowed to be incident from above wells provided on a microplate M and the light transmitted to the lower surface is received to obtain an original image of the wells (Step S). Detection target areas in the original image are specified by an appropriate image processing (Step S), and peripheral areas as backgrounds surrounding the respective detection target areas are specified (Step S). By calculating a density value of the detection target area Ri using luminance information of the detection target area Ri and that of the peripheral area Si surrounding this detection target area Ri for each detection target area Ri (Steps S, S), the influence of a well wall surface reflected on the background is eliminated.

Method for detecting density of area in image

An operation part in a pattern inspection apparatus includes a defect candidate image generator for generating a binary defect candidate image representing a defect candidate area in an inspection image by comparing the inspection image with a reference image, in an inspection image masking part the inspection image is masked with the defect candidate image to obtain a masked inspection image. In a feature value calculation part, an autocorrelation feature value is obtained from the masked inspection image, and outputted to a classifying part. The classifying part comprises a classifier outputting a classification result on the basis of the autocorrelation feature value and a classifier construction part for constructing the classifier by learning. It is thereby possible to easily perform the high accurate classification of defect candidate using the autocorrelation feature value which is hard to characterize as compared with geometric feature value or feature value representing density.

Apparatus and method for inspecting pattern

An inspection systemincludes an image pickup apparatusfor picking up an image of a defect, an inspection and classification apparatusfor performing inspection and automatic classification of defects, and a host computer. The host computerperforms learning for automatic classification at the inspection and classification apparatus. For creation of training data to be used for learning, defect images are arranged on a display of the host computeron the basis of sizes of defects or imaging positions for picking up images of defects. A visual sign is provided to the defect image indicating a category assigned thereto. Further, in response to an operation by an operator, a statistical value of feature values of defect images included in a category, data obtained in inspection, images after being subjected to image processing, similar images or dissimilar images similar to or dissimilar to a defect image targeted for classification, an area directed to calculation of feature values in a defect image targeted for classification, and the like, are suitably displayed.

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Kyoto, Japan

Hiroshi Ogi