Nec Corporation

Kenji Okajima

Yoshiko Yamashita

Akira Saito

Tomoharu Kiyuna

Yoshiko Yoshihara

Masanobu Miyashita

Akira Sato

Keiichi Yoshida

Kenichi Kamijo

Hitoshi Imaoka

Masahiko Kuroda

Akira Saitou

Hiroaki Torii

Ken' Ichi Kamijo

Kiyoshi Mukai

Maki Ogura

Masanobu Miyasita

A breast cancer pathological image diagnosis support system for supporting a diagnosis of breast cancer based on a pathological image is provided. The breast cancer pathological image diagnosis support system includes an image obtaining unit which obtains an HE-stained image and an IHC image as pathological images to be diagnosed; an information obtaining unit which obtains information of a tumor area in the HE-stained image; a matching unit which calculates a matching position of the HE-stained image and the IHC image obtained by the image obtaining unit; a specifying unit which specifies a tumor area in the IHC image based on the information of the tumor area in the HE-stained image obtained by the information obtaining unit and information of the matching position calculated by the matching unit; and a calculating unit which calculates a staining positive cell content rate in the tumor area based on information of the tumor area in the IHC image specified by the specifying unit.

Breast cancer pathological image diagnosis support system, breast cancer pathological image diagnosis support method, and recording medium recording breast cancer pathological image diagnosis support program

A support system for histopathological diagnosis includes a cell nucleus uniformity evaluation unit evaluating a uniformity of a plurality of cell nuclei included in a ductal region in an image. With this configuration, there is provided a support system, a support method and a support program for histopathological diagnosis, which enables realization of highly accurate cancer differentiation in a pathological diagnosis.

Support system for histopathological diagnosis, support program for histopathological diagnosis and support method for histopathological diagnosis

This invention is directed to improvement of the accuracy of cancer differentiation in the pathologic diagnosis using a pathologic tissue image analysis. There are included a stroma area/duct area detecting module for detecting, from a biological tissue image, a duct area on the basis of duct characteristic information indicating the characteristic of ducts included in a tissue area in the biological tissue image; an intraduct cell-nucleus detecting module for extracting, based on a predetermined pigment reference, cell nucleuses included in the detected duct area; and a duct formation evaluating module for calculating a heterotypic degree of each duct on the basis of the distribution state of the cell nucleuses for a predetermined base areas in each duct.

Pathologic tissue image analyzing apparatus, pathologic tissue image analyzing method, and pathologic tissue image analyzing program

The detection accuracy of poorly differentiated cancers in adenocarcinoma is improved by restricting false detection. Cell nucleus detection meansreceives a digitized pathological image as an input and extracts the region of a cell nucleus therefrom. Gland duct detection meansdetects a gland duct structure in the image. Poorly differentiated cancer detection meansdetects poorly differentiated cancers only in the region other than the gland duct region. False detection rejection meanscompares the detection density of poorly differentiated cancer in the vicinity of a detection point with a threshold that is predetermined depending on gland duct density in the vicinity of the detection point, at each detection point detected by poorly differentiated cancer detection meansand rejects the detection point as a false detection if the detection density of a poorly differentiated cancer is smaller than the threshold.

Module for detecting poorly differentiated cancers and pathological image diagnosis support apparatus, program and recording medium including the same

An evaluation system includes a capture unit for capturing an image of a living tissue in which HER2 protein and cell nucleuses are dyed, a discrimination unit for identifying a cell membrane from the image of the living tissue based on dyed cell nucleuses within the image of the living tissue captured by the capture unit to discriminate a dyed state of the cell membrane, and an evaluation unit for evaluating development of the HER2 protein based on a discrimination result by the discrimination unit.

Evaluation system, evaluation method, and recording medium

A method of evaluating a cell state based on information of an image taken of a cell containing a chromosome territory is provided. This method includes extracting the chromosome territory from the image (S), standardizing a positioning state of the chromosome territory and then quantifying the positioning state (S), and evaluating the cell state based on the quantified positioning state of the chromosome territory (S).

Method of evaluating chromosome state and evaluation system

A pathological diagnosis support device, a pathological diagnosis support program, a pathological diagnosis support method, and a pathological diagnosis support system extract a pathological tissue for diagnosis from a pathological image and diagnose the pathological tissue. A tissue collected in a pathological inspection is stained using, for example, hematoxylin and eosin. In consideration of the state of the tissue in which a cell nucleus and its peripheral constituent items are stained in respective colors unique thereto, subimages such as a cell nucleus, a pore, cytoplasm, interstitium are extracted from the pathological image, and color information of the cell nucleus is also extracted. The subimages and the color information are stored as feature candidates so that presence or absence of a tumor and benignity or malignity of the tumor are determined.

Pathological diagnosis support device, program, method, and system

Feature decision means () decides a set of features appropriate for pattern identification from a plenty of feature candidates generated by feature candidate generation means () by using learning patterns stored in learning, pattern storage means (). The feature decision means () successively decides features according to a reference of information maximization under the condition that the decided feature is known while adding an effective noise to the learning pattern and performs information amount calculation approximately and at a high speed while merging the learning patterns into a set of N elements when required. As a result, it is possible to automatically create a feature set appropriate for pattern identification of a high performance without requiring enormous learning. Moreover, by using a transition table () containing transitions between sets, it is possible to perform pattern judgment with a high efficiency.

Pattern feature selection method, classification method, judgment method, program, and device

In a feature extracting device for pattern recognition which is hardly affected by a pattern variation, an input pattern received by a learning pattern input/store unit is normalized by a normalizing unit and projected on a subspace group by a feature vector extracting unit, so to calculate feature vectors from each projection length, and a parameter updating unit updates basis vectors of each subspace so as to increase the ratio (variation between classes/variation within a class) as for the feature vectors, and optimizes them in a way of absorbing the pattern variation into each subspace according as the learning by the update processing progresses, thereby realizing the high ratio of the variation between classes to the variation within a class at the time of completing the learning and enabling the feature extraction more suitable for pattern recognition.

Feature extracting device for pattern recognition

In a three-dimensional reference image segmenting method and device, a two-dimensional image of a reference object and a shape data of a pattern obtained by transforming the image is stored together with depth data of the reference object in a memory as a reference pattern. On the basis of a local Fourier transform image date of an input image supplied from an image transform unit and the reference data of the reference pattern read out from the memory, a deform amount estimating unit calculates the amount of deform (displacement vector) required to make both the images coincident with each other to a possible extent. An inverse Fourier transform unit generates a deformed reference image by an local inverse Fourier transform based on the displacement vector and the data of the local Fourier transform image data of the reference pattern. On the basis of the deformed reference image thus generated, an image segmentation unit extracts a reference object image from within the input image, and the reference object image thus extracted is outputted from an image output unit.

Three dimensional reference image segmenting method and device and

The images of an object equivalent to those obtained when as object is viewed respectively by the right and left eyes are subdivided into small areas with overlaps allowed therebetween and are Fourier transformed to achieve a local Fourier transform for the images. Phase difference of Fourier transforms is obtained according to the right and left local Fourier transform images of a reference image which is stored beforehand in a storage memory. Alternatively, a Fourier transform power spectrum is obtained. Disparity is represented in terms of a phase difference or in the form of a Fourier transform power spectral pattern. According to either one of the above results, and on the basis of a local Fourier transform of an input image including the reference image, an identical object image is estimated. For the image, a local inverse Fourier transform is effected such that geometric mean values are attained between the right and left images so as to segment an image matching the reference image and, also, with respect to disparity.

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Tokyo, Japan

Kenji Okajima