Abstract: 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.

Abstract: An analysis apparatus includes a two-dimensional photographing unit 20, a three-dimensional photographing unit 30, and a computer 50 serving as an analysis unit. The two-dimensional photographing unit 20 two-dimensionally photographs a sample, to acquire a two-dimensional image D2. The three-dimensional photographing unit 30 three-dimensionally photographs the sample to acquire a three-dimensional image D3. The computer 50 analyzes the two-dimensional image D2 and outputs a two-dimensional analysis result R2, first. Subsequently, the computer 50 analyzes the three-dimensional image D3 and outputs a three-dimensional analysis result R3. At that time, the computer 50 analyzes the three-dimensional image D3 using an indicator included in the two-dimensional analysis result R2. This can improve the accuracy of the three-dimensional analysis result R3.

Abstract: An image display method includes the following operations (a) to (e). The (a) is of obtaining a plurality of two-dimensional images by two-dimensionally imaging a specimen, in which a plurality of objects to be observed are present three-dimensionally in the specimen, at a plurality of mutually different focus positions. The (b) is of obtaining image data representing a three-dimensional shape of the specimen. The (c) is of obtaining a three-dimensional image of the specimen based on the image data. The (d) is of obtaining the two-dimensional image selected from the plurality of two-dimensional images or a two-dimensional image generated to be focused on the plurality of objects based on the plurality of two-dimensional images as an integration two-dimensional image. The (e) is of integrating the integration two-dimensional image obtained in the (d) with the three-dimensional image obtained in the (c) and displaying an integrated image on a display unit.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: As light is allowed to impinge from above a microplate M and an imaging unit 13 (a line sensor 131 and an imaging optical system 132), 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 sensor 131 is 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 system 132 is 0.6 mm or less, the influence of reflection of side walls of the wells upon the images is reduced.

Abstract: 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 S101). Detection target areas in the original image are specified by an appropriate image processing (Step S102), and peripheral areas as backgrounds surrounding the respective detection target areas are specified (Step S103). 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 S105, S106), the influence of a well wall surface reflected on the background is eliminated.

Abstract: In a technique for displaying images of recess portions formed on a sample holding plate, an image display apparatus or an image display method is provided which has a displaying function which makes it possible for a user to compare and observe the images from various perspectives. From a total image obtained by shooting a micro plate as a whole, areas corresponding to wells are cut out. Image processing, such as color coding and contour enhancement, is performed on images of the wells based on classification results obtained using a classification criterion designated by the user, thereby forming material images. The material images are arranged in such an arrangement in accordance with the sequence on the micro plate, thereby forming an image for displaying. Classification results obtained using different classification criteria are displayed side by side, which makes it easy to compare and observe from different perspectives.

Abstract: As light is allowed to impinge from above a microplate M and an imaging unit 13 (a line sensor 131 and an imaging optical system 132), 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 sensor 131 is 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 system 132 is 0.6 mm or less, the influence of reflection of side walls of the wells upon the images is reduced.

Abstract: 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 S101). Detection target areas in the original image are specified by an appropriate image processing (Step S102), and peripheral areas as backgrounds surrounding the respective detection target areas are specified (Step S103). 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 S105, S106), the influence of a well wall surface reflected on the background is eliminated.

Abstract: 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.

Abstract: 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.

Abstract: An inspection system 1 includes an image pickup apparatus 2 for picking up an image of a defect, an inspection and classification apparatus 4 for performing inspection and automatic classification of defects, and a host computer 5. The host computer 5 performs learning for automatic classification at the inspection and classification apparatus 4. For creation of training data to be used for learning, defect images are arranged on a display of the host computer 5 on 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.

Abstract: In a defect detection apparatus 1, in a reference image inspection circuit 42 compared are a reference image representing a pattern in a die which is determined as a reference on a substrate 9 and a plurality of supervisory images which represent patterns in selected block areas, respectively, to detect defects included in the reference image. Subsequently, in the target image inspection circuit 44, a target image representing a pattern in another die and the reference image are compared to detect a plurality of defect candidates included in the target image. Then, a defect detector 45 excludes defect candidates overlapping with the defects included in the reference image from the plurality of defect candidates on the basis of at least positional information of the defects included in the reference image. This makes it possible to detect defects existing in the pattern in another die accurately while eliminating effects of the defects existing in the pattern in the die which is determined as the reference.

Abstract: An operation part (50) in a pattern inspection apparatus (1) comprises a defect candidate image generator (511) 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 (521) the inspection image is masked with the defect candidate image to obtain a masked inspection image. In a feature value calculation part (531), an autocorrelation feature value is obtained from the masked inspection image, and outputted to a classifying part (54). The classifying part (54) comprises a classifier (541) outputting a classification result on the basis of the autocorrelation feature value and a classifier construction part (542) for constructing the classifier (541) by learning.

Abstract: An inspection system 1 includes an image pickup apparatus 2 for picking up an image of a defect, an inspection and classification apparatus 4 for performing inspection and automatic classification of defects, and a host computer 5. The host computer 5 performs learning for automatic classification at the inspection and classification apparatus 4. For creation of training data to be used for learning, defect images are arranged on a display of the host computer 5 on 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.

Abstract: In a radio transmission/receiving device in which the same communication frequency band is used for transmission and receiving modes, a low pass filter for preventing higher harmonics is arranged between an antenna and a transmission circuit under the transmission mode and a band filter for band selection is arranged between the antenna and a receiving circuit under the receiving mode, whereby a reduction of production costs of the system is achieved at by reducing the number of elements required for the band filter.