Hitachi, Ltd.

Hitachi-high-technologies Corporation

Norio Oowada

Hideki Asai

Hideyuki Horiuchi

Satoshi Mitsuyama

Ryohei Yabe

Yasuaki Kojima

Kazuhiro Sano

Miki Taki

Chihiro Manri

Jun Motoike

Hatsue Katahira

At each of mutually different multiple focal positions, focal adjustment parameter values are obtained from images of standard particles made of the same substance. Each focal adjustment parameter value is figured out as any one of the ratio between the density value around the center of the standard particle image and the density value around the outline, the difference therebetween, and the density value around the center. The in-focus position is adjusted on the basis of the relationship between the obtained focal adjustment parameter values and the focal positions. Moreover, on the basis of the relationship between the focal adjustment parameter values and the focal positions, the parameter values are converted into focal positions, and the focal positions and dispersion thereof are used to check the displacement of the in-focus position and the thickness of the sample liquid.

Flow type particle image analysis method and device

A particle image analyzing method is adapted so that while raising image-reviewing efficiency at a cropped image level of particle components in a sample, the entire sample can be observed without significantly changing a related apparatus configuration. Prior to image reviewing of an imaging region, cropped images thereof are reviewed and, with reference to the images arranged for each kind of particle component, if the operator judges any particles to have been falsely identified, the operator uses an operating unit to modify positions of the particles to those of correct component items. An overall image of the imaging region is displayed and if any components to be added (overlooked components) appear, the kinds of these components are identified and quantitative data on each kind of component is registered. Upon completion of the registration, the concentration of the sample is recalculated and a comment is entered in a comment field.

Particle image analysis method and apparatus

A particle image analyzing apparatus of high classifying accuracy and high efficiency in which a criterion for determining whether fine classification is required or not can be set for every facility such as hospital, or the like. A rough classification unit executes rough classification of particles and supplies results of the rough classification to a fine classification necessity determination unit. The necessity determination unit compares the results of the rough classification with reference values stored in a reference value storage unit and determines from the comparison whether every sample is normal or abnormal to thereby determine whether fine classification is required or not. The reference values stored in the storage unit can be changed in accordance with the subject of examination through an input/output control unit from a keyboard. When the determination unit makes a decision that fine classification is required, the determination unit gives an instruction to an image processing control circuit to execute fine classification. Characteristic parameters for fine classification are supplied to a fine classification unit on the basis of the control instruction of the control circuit. Then, fine classification is executed, so that results of the fine classification are supplied to the determination unit.

Particle image analyzing apparatus

In a pattern recognition system provided with a pattern recognition processing unit having a network structure constructed of an input layer for inputting a feature parameter of a subject under recognition as input information an intermediate layer for processing the input information and an output layer for outputting a processed result output values of respective output nodes for constituting the output layer, corresponding to the input information, are compared with each other by the pattern recognition processing unit, and a classification item corresponding to the output node whose output value is maximum is stored into a storage unit as a recognized result with respect to the input information. The pattern recognition system is also provided with a reliability evaluating unit for setting a threshold with respect to an output value of each of the output nodes and for evaluating reliability of the recognized result based upon the output values of the respective output nodes. When the reliability evaluating unit judges that the reliability is high, the recognized result is stored into the storage unit.

Pattern recognition system

A flow type particle image analytical method for feeding a particles-suspended sample by surrounding it by a cleaning solution, imaging particles in the sample by irradiating light to it, and automatically classifying the particles in the sample by analyzing the picked-up particle images, wherein the method comprises a means for designating the kind of particles to be reviewed beforehand among the classified particles, a means for storing only particles which are automatically classified to the designated particle kind in a review image memory, a means for displaying particles of the same kind on a CRT display in batch so as to review particle images, and a means for classifying each of the displayed particles finally by an operator or for changing the particle name.

Flow type particle image analyzing method and apparatus for displaying

A particle image in a sample is formed at an imaging position by an objective lens of a microscope, projected on the image picking up plane of a TV camera via a projection lens and is subjected to photo-electric conversion. Image signals from the TV camera are supplied to an image memory via an A/D converter as well as to an image processing control unit. Image signals outputted from the image memory are supplied to a characteristic picking out unit and there a plurality of characteristics of the particle concerned are picked out. The picked-out characteristics are supplied to the classification unit and there classification of the sediment components is perfumed via a neural network with a learning capability. Accordingly, the classification unit performs provisionally an automatic classification of the objective sediment components by making use of the inputted characteristic parameters. The device allows accurate and fast automatic component particle analysis even for patient specimens containing a variety of components in high concentration.

Device for the classification and examination of particles in fluid

A method and an apparatus for flow type particle image analysis generate a pulse light emission in any one of the odd and the even field image reading-out periods, and multiple exposure is prevented by prohibiting the next pulse light emission in the predetermined period after the emission. The method includes the steps of flowing a sample liquid in a flow cell, detecting particles passing through the flow cell by a laser beam, making a flash lamp emit light after the predetermined period from the particle detection, picking up a particle still image by a TV camera, and further preventing the multiple exposure by providing emission-prohibiting periods which are established by controlling the timings of flash lamp emission and image transfer with the TV camera, a flash lamp emission control circuit, and an image processing control circuit which are controlled by a central control part.

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Hitachinaka, Japan

Norio Oowada