Fujitsu Corporation

Susumu Endo

Takayuki Baba

Yusuke Uehara

Daiki Masumoto

Shuichi Shiitani

Masaki Ishihara

Shigemi Nagata

Masahiko Sugimura

Hiroaki Takebe

Rujie Liu

Hirohisa Naito

Akira Miyazaki

Shugo Nakamura

Yasutaka Moriwaki

Masaki Watanabe

Atsushi Oguchi

Yuji Matsuda

Masashi Urushihara

Shuchi Shiitani

Youji Kohda

Hiroaki Harada

Naoki Sashida

Sokichi Fujita

Tatsuma Muramatsu

Masaaki Oota

Yuehong Wang

Dai Miyawaki

Yuusuke Uehara

Syuuichi Shiitani

A similar case image search method performed by a computer, the method includes: extracting a lung field area from a medical image and identifying a contour of the lung field area including a chest wall and a mediastinum; identifying a position at which the chest wall and the mediastinum are internally divided and dividing the lung field area into a central area and a peripheral area based on a shape of the lung field area; counting the number of pixels indicating lesions in each of the divided central area and peripheral area; and identifying a similar case image corresponding to similarity level of the number of pixels indicating lesions by referring to a storage unit that stores the number of pixels indicating lesions in each of the areas.

Similar case image search program, similar case image search apparatus, and similar case image search method

A non-transitory recording medium stores a program causing a computer to execute a process including determining a reference value on a decision surface classifying a group of image data represented by a plurality of types of feature quantities into two classes in a feature quantity space, based on a feature quantity of given image data that is closest to the decision surface and is classified into one of the classes of the group of image data in the feature quantity space, plotting and displaying pieces of image data of the group of image data along at least one axis indicating a degree of separation of each of the pieces of image data relative to the reference value, modifying, upon receiving a single piece of image data identified from the plotted and displayed pieces of image data, the decision surface based on the identified single piece of image data.

Recording medium, case data generation support system, and case data generation support method

An image retrieval apparatus that retrieves a candidate medical image in diagnosis of diffuse lung disease based on a position of an abnormal shadow in an organ region in a target medical image, the apparatus includes a memory, and a processor coupled to the memory and configured to map the organ region in the target medical image to an image having a predetermined shape to make it identifiable whether the abnormal shadow is distributed over a first portion in the organ region or a second portion in the organ region, occurrence portions of the abnormal shadow within the first portion and the second portion are organizationally different, and calculate a position of the abnormal shadow after the mapping in the image having the predetermined shape.

Image retrieval apparatus and image retrieval method

A non-transitory computer-readable recording medium storing an image interpretation support program that causes a computer to execute a process, the process including generating first data indicating a first internal structure of a predetermined object, based on a first cross-sectional image group acquired with respect to the predetermined object; detecting a structural change of the first internal structure from a second internal structure of the predetermined object, based on second data indicating the second internal structure and the generated first data, the second data being generated based on a second cross-sectional image group acquired at a past time; identifying a new cross-section with respect to the predetermined object based on the structural change; generating a cross-sectional image of the predetermined object with respect to the new cross-section, based on the first cross-sectional image group; and displaying the generated cross-sectional image together with first information indicating the detected structural change.

Cross-sectional image generating apparatus, cross-sectional image generating method, and recording medium

There is provided a distance measuring apparatus including a storing unit configured to store a first image and a second image to be compared with one another; and a calculating unit configured to extract a plurality of feature points from each of the first image and the second image, extract a combination of feature points with high stability of feature values amongst the plurality of feature points in the first image and a combination of feature points with high stability of feature values amongst the plurality of feature points in the second image, and output, as bit strings, the feature values associated with the combination extracted from the first image and the feature values associated with the combination extracted from the second image.

Method and apparatus for distance measurement

An image similarity determination apparatus includes a memory and a processor configured to acquire a first image and a second image, perform selection of a first group and a second group from a plurality of feature points included in the first image and perform selection of a third group and a fourth group from a plurality of feature points included in the second image, calculate feature quantity for each feature point included in the first group and the third group on the basis of luminance and calculate feature quantity for each feature point included in the second group and the fourth group on the basis of hue, and determine similarity between the first image and the second image on the basis of both first comparison of the first group with the third group and second comparison of the second group with the fourth group.

Image similarity determination apparatus and image similarity determination method

A computation unit calculates luminance differences of individual pixel pairs in a feature area and calculates, based thereon, a local feature value formed from bit values respectively corresponding to the pixel pairs. Specifically, the computation unit calculates a specific luminance difference for a specific pixel pair corresponding to a specific bit value and then compares the result with a specified range including a zero point of luminance difference. Then a first value is assigned to the specific bit value when the specific luminance difference is greater than the upper bound of the specified range. A second value is assigned to the same when the specific luminance difference is smaller than the lower bound of the specified range. A predetermined one of the first and second values is assigned to the same when the specific luminance difference falls in the specified range.

Computer-readable storage medium storing image processing program and image processing apparatus

This invention discloses an image retrieval apparatus. The image retrieval apparatus comprises an unlabelled image selector for selecting one or more unlabelled image(s) from an image database; and a main learner for training in each feedback round of the image retrieval, estimating relevance of images in the image database and a user's intention, and determining retrieval results, wherein the main learner makes use of the unlabelled image(s) selected by the unlabelled image selector in the estimation. In addition, the image retrieval apparatus may also include an active selector for selecting, in each feedback round and according to estimation results of the main learner, one or more unlabelled image(s) from the image database for the user to label.

Image retrieval apparatus

An image processor includes: a memory; and a processor, wherein the processor is configured to: extract an arc area in an image captured by an image sensor; and determine whether a portion that protrudes from an arc in the arc area satisfies a first reference regarding a shape and detect the portion as a colony candidate when it is determined that the portion satisfies the first reference.

Image apparatus, image processing method, and computer readable, non-transitory medium

An extraction device includes a processor that executes a procedure. The procedure includes: from captured images obtained by capturing a sports game having a match style of attack toward a goal provided in territory of an opposing team, identifying a portion of captured images in which the captured images change over time toward a direction of one or other goal; and extracting a scene in which one or other team is attacking based on the portion of captured images.

Method of extracting attack scene from sports footage

An image processing apparatus receives an input specifying a target in a first captured image group that is obtained by slicing a three-dimensional body into captured images at a first point in time, extracts feature points surrounding the specified target from the captured images of the first captured image group, specifies slice positions of each of the feature points that are extracted in captured images of a second captured image group that is obtained by slicing the three-dimensional body into the captured images at a second point in time, and outputs an image corresponding to a slice position range specified based on the specified slice positions of each of the extracted feature points, and corresponding to a range on a first plane specified based on positions of each of the extracted feature points on a second plane.

Image processing apparatus and image processing method

An image display apparatus includes a display device and a computer configured to execute a program to cause the computer to serve as a display controlling part. The display part displays a first CT image and a second CT image simultaneously. The first CT image and the second CT image represent the same object but taken in different time periods. The display controlling part performs: displaying the first and second CT images simultaneously on the display device according to a first display mode; determining a target portion designated in the first CT image; and changing a display mode of a corresponding portion of the second CT image from the first display mode into a second display mode, the corresponding portion corresponding to the target portion designated in the first CT image.

Image display apparatus, image display method and storage medium storing image display program

A binarization processing unit generates a binary image by comparing the visual feature of each pixel of an input image with a predetermined binarization threshold and binarizing the value of each pixel of the input image based on the comparison result. The binarization processing unit performs this process while changing the binarization threshold, thereby generating a plurality of binary images. A contour detecting unit calculates the area of a closed region having an overlap between the plurality of binary images, and determines the binarization threshold to be used for detecting a boundary of the corresponding closed region as a contour of an object, based on the area change rate of the corresponding closed region caused by changing the binarization threshold.

An extraction device includes a processor that executes a procedure. The procedure includes identifying, as a video data portion including a scene of interest in a sports game, a portion of, or all of, captured images of the sports game that correspond to a period in which audio data obtained by collecting sound from the sports game exhibits relatively loud sound, or a period in which the audio data exhibits sound louder than a specific loudness.

Extraction method and device

A computer-readable recording medium storing a program for causing a computer to execute a procedure for managing a system that retrieves a image corresponding to a first type specified by a retrieval request received from a terminal device and another image corresponding to a second type associated with the first type, from an image database storing a plurality of images associated with at least a number of a plurality of types, the procedure includes: referring to a learning state representing whether feature values of images corresponding to each of the plurality of the types have been obtained; retrieving an unlearned type in which the feature value has not been obtained, among the plurality of types; specifying an association type associated with the unlearned type; and outputting the unlearned type in accordance with the learning state of the association type.

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Kawasaki, Japan

Susumu Endo