Fujitsu Corporation

Hiroaki Takebe

Katsuhito Fujimoto

Akihiro Minagawa

Satoshi Naoi

Takayuki Baba

Yoshinobu Hotta

Yutaka Katsuyama

Masahiko Sugimura

Yusuke Uehara

Susumu Endo

Noriaki Ozawa

Hiroshi Tanaka

Yusaku Fujii

Yasutaka Moriwaki

Masaki Ishihara

Misako Suwa

Nobuhiro Miyazaki

Koji Kurokawa

Masayoshi Shimizu

Jun Sun

Hirohisa Naito

Seiji Takahashi

Kenji Nakajima

Akira Miyazaki

Nobuyuki Hara

Yoshio Fukasawa

Masahiko Shimada

Kiichiro Watanabe

Xu-Cheng Yin

Masaki Inami

Yusaku Fuji

Junichi Hirai

A non-transitory computer-readable storage medium storing a program that causes a computer to execute a process, the process includes identifying first filter processing applied to a first image that is training data used for machine learning; generating the first image from which characteristics of the identified first filter processing are removed; and generating, by applying second filter processing to the first image from which the characteristics are removed, a second image to be assigned a label identical to a label of the first image, the second image being used in the machine learning as the training data.

Computer-readable recording medium, training data generation method, and training data generation apparatus

A non-transitory computer-readable recording medium having stored an image processing program that causes a computer to execute a process, the process includes extracting a plurality of consecutive pixels corresponding to a first part or a second part of a body, from a pixel column in a predetermined direction of an image of the body, obtaining a statistical value of pixel values of the plurality of consecutive pixels, and identifying a part corresponding to the plurality of consecutive pixels, among the first part or the second part, based on the statistical value.

Image processing apparatus and image processing method

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

An apparatus for processing an image includes a memory and a processor coupled to the memory. The processor is configured to determine a base hue value based on a state of distribution of a first hue value of each pixel of at least one image. The processor is configured to convert the first hue value of each pixel of the at least one image into a second hue value based on the base hue value. The processor is configured to calculate a difference in the second hue value between pixels included in each of a plurality of pixel pairs set in at least one feature region in the at least one image. The processor is configured to calculate a local feature amount of each of the at least one feature region based on the difference in the second hue value.

Apparatus for processing image and method thereof

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

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

A color chart detection apparatus that detects a color chart from an image representing the color chart having a plurality of palettes arranged in a predefined order is provided. The color chart detection apparatus: extracts any pixel having a color component corresponding to each designated one of a predetermined number of palettes; selects at least three palettes to form a first group and, when a combination of pixels selected on a palette-by-palette basis from among the pixels extracted for the respective palettes included in the first group is arranged in the predefined order, detects each of the pixels included in the combination as being a candidate pixel; and based on the candidate pixel, computes color information representing color of the palette corresponding to the candidate pixel on the image and position information representing position of the corresponding palette on the image.

Color chart detection apparatus, color chart detection method, and color chart detection computer program

A subject tracking device includes a processor; and a memory which stores a plurality of instructions, which when executed by the processor, cause the processor to execute, detecting at least one subject candidate area in which it is probable that a tracking target subject appears on an image that is received from an imaging unit; calculating a degree of blur of the subject candidate area for each of the subject candidate areas; determining that the subject appears in a subject candidate area having a degree of blur in accordance with a moving speed of the subject, out of the subject candidate areas; and deciding movement of the subject depending on a movement direction from an area in which the subject appears on a previous image that is captured by the imaging unit before capturing the image, to the subject candidate area in which the subject appears on the image.

Subject tracking device and subject tracking method

A signal processing device includes: a memory; and a processor coupled to the memory and configured to: detect a second feature value relating to a first feature value recognized to satisfy a recognition condition, from a second time series prior to a first time series of the first feature value in a times series of a feature value corresponding to an input signal, and change the recognition condition so that the second feature value is recognized as a class for recognizing the first feature value.

Signal processing device and signal processing method

According to an aspect of an embodiment, a method of detecting boundary line information contained in image information comprising a plurality of pixels in either one of first and second states, comprising: detecting a first group of pixels in the first state disposed continuously in said image information to determine first line information and detecting a second group of pixels in the first state disposed adjacently with each other and surrounded by pixels in the second state to determine edge information based on the contour of the second group of pixels; and determining the boundary line information on the basis of the information of the relation of relative position of the line information and the edge information and the size of the first and second group of pixels.

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Kawasaki, Japan

Hiroaki Takebe