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WO2000073977A1 - Technique d'imagerie spectrale $i(in situ) multicouche a fusion focale et analyse d'echantillons particulaires - Google Patents

Technique d'imagerie spectrale $i(in situ) multicouche a fusion focale et analyse d'echantillons particulaires Download PDF

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Publication number
WO2000073977A1
WO2000073977A1 PCT/US2000/014312 US0014312W WO0073977A1 WO 2000073977 A1 WO2000073977 A1 WO 2000073977A1 US 0014312 W US0014312 W US 0014312W WO 0073977 A1 WO0073977 A1 WO 0073977A1
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blob
sample
biological
particles
image
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Inventor
Danny S. Moshe
Michael Khazanski
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GreenVision Systems Ltd
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GreenVision Systems Ltd
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Priority to AU51608/00A priority Critical patent/AU5160800A/en
Priority to EP00936267A priority patent/EP1190372A1/fr
Publication of WO2000073977A1 publication Critical patent/WO2000073977A1/fr
Anticipated expiration legal-status Critical
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Optical investigation techniques, e.g. flow cytometry
    • G01N15/1429—Signal processing
    • G01N15/1433—Signal processing using image recognition

Definitions

  • the present invention relates to methods of imaging and analysis of particles and, in particular, to a method for in-situ focus-fusion multi-layer spectral imaging and analysis of particulate samples.
  • an image of the sample exhibits a layer dependent or spatially varying degree of sharpness.
  • This is referred to as a defocused image of the sample or scene, where some of the objects of the scene are in focus, while other objects of the scene are out of focus.
  • Defocused images contain information potentially useful for scene analysis.
  • the analysis of scenes from defocused images is of general interest in machine vision applications, for example, in active vision or robot vision where a camera actively explores a scene by continuously changing its position, i.e., field of view, relative to scene features.
  • scene analysis is of significant practical importance in chemical, pharmaceutical, biomedical, and biological imaging, and general microscopy image analysis, where layer or depth variations of imaged samples of chemicals, powders, frozen suspensions of powders, biological specimens, or other multi-layered particulate samples are typically large compared to imaging distances.
  • Scene analysis is of particular applicability to depth dependent particulate samples, where, tor instance, one or more layers ot microorganisms such as bacterial or fungal growth, exhibiting fluorescent emission properties in addition to the fluorescent emission properties of the particles themselves, is present on the particles, and there is a need for separation of imaging and analysis of the microorganisms from imaging and analysis of the host particles.
  • an auto-focus module is coupled with a computer controlled mechanism that automatically changes the focal position, by moving along an axis parallel to the optical axis of the imaging or focusing sensor, thereby enabling identification of a good focal position.
  • a good focal position is not guaranteed to exist and further image processing via focus-fusion is required.
  • a focused representation of the scene can be constructed by combining or fusing several defocused images of the same scene. This process is referred to as focus-fusion imaging, and the resulting image of such processing is referred to as a focus-fusion image.
  • Defocused images for example, those acquired during auto-focusing, are fused together such that each target in a given scene is in correct focus.
  • Scene targets are detected by analyzing either the focused image, if it exists, or the focus-fusion image.
  • a current technique of imaging particles, for example, featuring chemical and/or biological species is based on spectral imaging.
  • spectral imaging a particulate sample is affected in a way, for example, excitation by incident ultraviolet light upon the sample, which causes the sample to emit light featuring an emission spectra. Emitted light is recorded by an instrument such as a scanning interferometer that generates a set of interferogram images, which in turn are used to produce a spectral image, or image cube, of the sample.
  • a spectral image, or image cube is a three dimensional data set (a volume) of voxels in which two dimensions are the spatial dimensions of the sample and the third dimension is the wavelength of the imaged light, such that coordinates of a voxel in a spectral image or image cube may be represented as (x,y, ⁇ ).
  • a particulate sample is imaged in two dimensions, so that voxels corresponding to that wavelength constitute the pixels of a monochromatic image of the sample at that wavelength.
  • the spectral image is analyzed to produce a two dimensional map of the chemical or biological composition, or of some other physicochemical property of the sample, for example, particle size distribution.
  • An example of a method and system for real-time, on-line chemical analysis of particulate samples, for example, polycyclic aromatic hydrocarbon (PAH) particles in aerosols, in which the PAH sample is excited to emit light, for example fluorescence, is that of U.S. Patent No. 5,880,830, issued to Schechter, and manufactured by GreenNision Systems Ltd. of Tel Aviv, Israel, and is incorporated by reference for all purposes as if fully set forth herein.
  • spectral imaging techniques are implemented to acquire an image and analyze the properties of fixed position PAH particles.
  • PAH particles are first collected by drawing a large volume of air containing PAHs through a filter, followed by on-line scene analysis of the stationary particles.
  • a method of calibration and real-time analysis of particles is described in U.S.
  • Patent Application No. 09/146,361 filed September 03, 1998, and is incorporated by reference for all purposes as if fully set forth herein.
  • the method described is based on using essentially the same system of U.S. Pat. No. 5,880,830, for acquiring spectral images of static particles on a filter.
  • Targets are identified in static particle images and are classified according to morphology type and spectrum type. Each target is assigned a value of an extensive property.
  • a descriptor vector is formed, where each element of the descriptor vector is the sum of the extensive property values for one target class.
  • the descriptor vector is transformed, for example, to a vector of mass concentrations of chemical species of interest, or of number concentrations of biological species of interest, using a relationship determined in a calibration procedure.
  • spectral images of calibration samples of static particles having known composition are acquired, and empirical morphology types and spectrum types are inferred from the spectral images.
  • Targets are identified in the calibration spectral images, classified according to morphology type and spectrum type, and assigned values of an extensive property.
  • a calibration descriptor vector and a calibration concentration vector is formed. A collective relationship between the calibration descriptor vectors and the calibration concentration vectors is found using chemometric methods.
  • Spectral imaging of spatially varying, depth dependent, or multi-layered samples of particles is not described in the above referenced methods and systems. Imaging and image analysis of a random single layer of a sample including particles are ordinarily straightforward. However, multi-layer imaging and image analysis of depth dependent particulate samples, for example, multi-layered dry particles, or particles in a frozen or immobilized suspension, are substantially more complex, for the reasons stated above. More often than not, images obtained of such particulate samples are defocused, and require special image processing techniques, such as focus-fusion, for obtaining useful information about the samples. Nevertheless, there are instances where it is necessary to obtain property and classification information of depth dependent particulate samples, in-situ, for example, as part of sampling an industrial process.
  • a sample of dispersed or multi-layered particles is amenable to three-dimensional imaging and scene analysis.
  • spectral imaging as presently practiced would involve tedious methodologies and system manipulations, making acquisition of high resolution images impossible or at best impracticable.
  • Scene analysis via focus-fusion of defocused images, acquired by multi-layer spectral imaging of depth dependent particulate samples would be quite useful for detecting and classifying in-situ physicochemical information of the particles, such as particle size distribution, morphological features, including structure, form, and shape characteristics, and, chemical and biological composition, which ideally involve multi-layer three-dimensional image analysis.
  • in-situ physicochemical information of the particles such as particle size distribution, morphological features, including structure, form, and shape characteristics, and, chemical and biological composition, which ideally involve multi-layer three-dimensional image analysis.
  • current focus-fusion procedures and algorithms typically involve information and parameters relating only to the extent to which acquired images are either focused or defocused, without inclusion of additional information and parameters relating to particular properties or characteristics of the imaged object or sample.
  • Characteristic sample physicochemical and spectral information and parameters can be quite relevant to imaging particulate samples, and ought to be included in a method of focus-fusion of acquired images of such samples. This is especially the case for images of chemical and/or biological particulate samples featuring layer dependent or spatially varying degree of sharpness.
  • the present invention relates to a method for in-situ focus-fusion multi-layer spectral imaging and analysis of depth dependent particulate samples, where a given sample features chemical and/or biological species.
  • a unique method of focus-fusion is applied to focused and defocused images acquired from multi-layer spectral imaging of a depth dependent particulate sample, in order to construct focused fused cube spectral image representations of the imaged particles, thereby generating a focused image of essentially each particle in the sample.
  • the method of the present invention features the use of a uniquely defined and calculated focus-fusion factor parameter which combines empirically determined particle morphological characteristics with empirically determined particle spectral characteristics, and is used in critical steps of image detection and image analysis classification.
  • the method includes collecting and analyzing physicochemical and multi-layer spectral data relating to the particles in the sample, including mapping of three-dimensional positions of particles, particle sizes, and characteristics of particle emission spectra.
  • Scene information, in the form of spectral fingerprints, derived from analysis of focus-fusion of the multi-layer spectral images is further processed in order to generate usable in-situ physicochemical information of the particles, such as particle size distribution, mo ⁇ hological features, including structure, form, and shape characteristics, and, chemical and biological composition.
  • the focus-fusion multi-layer spectral image analysis includes a sophisticated classification procedure for extracting, on-line, useful information relating to particle properties and characteristics needed for generating a report applicable to monitoring or controlling an industrial process.
  • the method of the present invention enables multi-layer spectral imaging, multi-layer scene analysis, and multi-layer physicochemical characterization of particulate samples featuring depth dependency, which until now has not been described.
  • the present invention is of significant practical importance in chemical, pharmaceutical, biomedical, and biological imaging, and general microscopy image analysis, where layer or depth variations of imaged samples of chemicals, powders, frozen suspensions of powders, biological specimens, or other multi-layered particulate samples are typically large compared to imaging distances.
  • a method for multi-layer imaging and analyzing a sample featuring particles, imaged particles exhibiting a spatially varying degree of sha ⁇ ness the method composing the steps of: (a) providing a spectroscopic imaging system, including a sample holder movable by a three dimensional translation stage; (b) defining imaging scenario parameters; (c) adjusting and setting the imaging system for imaging at a selected field of view, FONrada having central x, y coordinates; (d) focusing the imaging system by moving the translation stage an increment ⁇ z, until receiving a sha ⁇ gray level image of the sample at a selected focal distance ⁇ z,; (e) at the selected FON, and at the selected ⁇ z consumer acquiring a cube image of the sample, the cube image featuring a plurality of pixels, each of the plurality of pixels having at least one common visual property, each of the plurality of pixels having a location in the cube image; (f) detecting a plurality of targets for each of the
  • a method for multi-layer imaging and analyzing a sample featuring particles, imaged particles exhibiting a spatially varying degree of sha ⁇ ness comprising the steps of: (a) at a selected field of view, FON strictly and at a selected focal distance, ⁇ ztron acquiring a cube image of the sample, the cube image featuring a plurality of pixels, each of the plurality of pixels having at least one common visual property, each of the plurality of pixels having a location in the cube image; (b) detecting a plurality of targets for each of the plurality of pixels, each of the plurality of targets created by a plurality of pixels having a pre-defined measured intensity above the imaged background intensity, each target defined as a Blob k ; (c) calculating a set of empirically determined mo ⁇ hological and biological parameters and a set of spectral parameters for each Blob k ; (d) calculating a focus-fusion factor parameter, F, from the set of mo ⁇ hological
  • FIG. 1 is a flow diagram of an exemplary preferred embodiment of the method for in-situ focus-fusion multi-layer spectral imaging and analysis of particulate samples, in accordance with the present invention.
  • FIG. 2 is a schematic diagram illustrating implementation of selected steps of the preferred embodiment of the method for in-situ focus-fusion multi-layer spectral imaging and analysis of particulate samples, in accordance with the present invention.
  • the present invention is of a method for in-situ focus-fusion multi-layer spectral imaging and analysis of particulate samples. Steps and implementation of the method according to the present invention are better understood with reference to the drawings and the accompanying description. It is to be noted that illustrations of the present invention shown here are for illustrative pu ⁇ oses only and are not meant to be limiting.
  • FIG. 1 is a flow diagram of an exemplary preferred embodiment of the method for in-situ focus-fusion multi-layer spectral imaging and analysis of particulate samples.
  • each generally applicable, principle step of the method of the present invention is numbered and enclosed inside a frame.
  • a sub-step representing further of an indicated principle step of the method are indicated by a letter in parentheses.
  • FIG. 2 is a schematic diagram illustrating implementation of selected steps of the preferred embodiment of the method for in-situ focus-fusion multi-layer spectral imaging and analysis of particulate samples. Referenced items shown in FIG. 2 relevant to understanding the method of FIG. 1 are referred to and described in the description of FIG. 1.
  • a sample 10 (FIG. 2) featuring particles is provided, and prepared for multi-layer spectral imaging and analysis.
  • Sample 10 could be, for example, a pure powder or a powder mixture, a frozen suspension of a powder, a biological specimen, or some other multi-layered particulate sample, and features a three dimensional topography using coordinate system 12 as a reference, whereby there are layer or depth variations along sample height 14 which are relatively large compared to imaging distances.
  • Sample 10 is placed on a sample holder 16, where sample 10 and sample holder 16 are either exposed to ambient conditions, for example, a powdered sample resting on a glass slide without controlled environmental containment, or, are contained in a controlled environment, for example, a frozen suspension maintained at or below the freezing point temperature of such a frozen suspension.
  • a spectroscopic imaging system 18, including a three dimensional translation stage 20 is provided. Examples of a spectroscopic imaging system 18, including peripheral apparatus, and control / data links, appropriate for implementation of the method of the present invention are fully described in U.S. Patent No. 5,880,830, issued to Schechter, and references cited therein, which are inco ⁇ orated by reference for all pu ⁇ oses as if fully set forth herein.
  • Spectroscopic imaging system 18 includes, among other components, an ultraviolet light illumination source, an optical system, a spectroscopic imaging device, a CCD camera having suitable sensitivity and dynamic range, a central control system, and control / data links.
  • the light source illuminates particles of sample 10 homogeneously via the optical system, or by directly without inclusion of the optical system.
  • the control system is based on a personal computer, and includes a frame grabber, for acquiring images from the CCD camera, as well as other hardware interface boards for controlling translation stage 20 and the other components of spectroscopic imaging system 18.
  • the software of the control system includes a database of empirically determined mo ⁇ hology types and spectrum types and codes for implementing the image processing and quantification algorithms described below.
  • Spectroscopic imaging system 18 includes a three dimensional translation stage 20 used for synchronized electronic three dimensional movement and positioning of sample holder 16, and therefore, sample 10.
  • Translation stage 20 is in electronic communication with spectroscopic imaging system control devices via control / data links 22. Usage of translation stage 20 enables spectroscopic imaging system 18 to automatically focus and image sample 10 in a plurality of pre-selected fields of view 24, FONconstru and along a plurality of pre-selected focal planes or focal distances, ⁇ z,, potentially spanning entire sample height 14.
  • sub-step (a) of Step (2) imaging scenario parameters to be used for image acquisition and analysis are defined.
  • sample physicochemical and biological parameters relating to particle chemical composition and biological composition such as microorganism count per particle, and related chemistry, and particle mo ⁇ hology relating to particle sizes and shapes
  • imaging system scanning parameters including selected viewing or imaging range of the sample relating to fields of view 24, FONnell and depth dimension or focal distances, ⁇ z )J5 pixel threshold intensity, Blob neighborhood distance, ⁇ D, and imaging time interval, ⁇ t.
  • Step (2) calibrations are performed on standard samples with known physicochemical, biological, and spectral imaging characteristics, according to methodology described in pending U.S. Patent Application No. 09/146,361, cited above, which is inco ⁇ orated by reference for all pu ⁇ oses as if fully set forth herein.
  • Step (3) sample 10 is scanned by adjusting and setting spectroscopic imaging system 18 for spectral imaging at a selected field of view 24, FOVallow over sample 10, having central x, y coordinates, by moving translation stage 20 an increment of ⁇ x and ⁇ y.
  • Step (4) imaging system 18 is focused by moving translation stage 20 an increment ⁇ z, until receiving a sha ⁇ gray level image of sample 10, at a selected focal distance ⁇ z, r
  • Step (5) a plurality of cube (spectral) images, featuring pixels, of sample 10 and particles therein, are acquired.
  • the pixels have at least one common visual property, and each pixel has a location.
  • Blobs if present in sample 10, are detected, where a Blob k 26 or 28 is defined as a detected target created by a plurality of pixels having a pre-defined intensity above the imaged background or threshold intensity (defined in Step (2), sub-step (a)). Ordinarily, it is desired that detection of a Blob k 26 or 28 be indicative of either a focused or defocused image of a particle of sample 10.
  • One of the primary tasks of the unique image acquisition procedure of the present invention is to distinguish meaningful or high content Blobs featuring useful information relating to particle characteristics from Blobs featuring non-particle information, such as sample or imaging system contamination.
  • sub-step (b) there is calculating a set of mo ⁇ hological and biological parameters, and a set of spectral parameters, for each Blob k 26 or 28, empirically determined from the Blob image data.
  • Mo ⁇ hological and biological parameters relate to, for example, the size, area, shape, microorganism count per particle, and x, y position coordinates of central gravity point of a given Blob k , which in turn, relate to particle characteristics in sample 10.
  • Spectral parameters relate to emission characteristics of an imaged particle in sample 10.
  • sub-step (c) there is calculating a focus-fusion factor parameter, F exclude from the set of mo ⁇ hological and biological, and set of spectral parameters, for each Blob k 26 or 28.
  • the focus-fusion factor parameter uniquely combines particle mo ⁇ hological and biological information with spectral information, to be used in a decision step for discriminating Blobs from each other. This uniquely determined parameter enables achievement of high levels of accuracy and precision in detection and classification of the sample, in general, and of the featured particles, in particular.
  • sub-step (d) all detected Blobs are grouped into a single Blob neighborhood, according to the neighborhood distance parameter, ⁇ D, defined in Step (2), sub-step (a).
  • sub-step (e) there is calculating a set of inter-Blob distances 30, ⁇ d kb for all the detected Blobs in the Blob neighborhood, where ⁇ d k] is measured between each grouped Blob k and its neighboring grouped Blobs, in the Blob neighborhood.
  • sub-step (f) there is selecting high content Blobs 28 from all the Blobs k 26 and 28 in the Blob neighborhood, according to decisions made by using the focus-fusion factor parameter, F Cincinnati ⁇ D, and the set of ⁇ d k] .
  • These high content Blobs are to be used in construction of a fused cube image of sample 10, ultimately, providing useful image content relating to particle characteristics.
  • sub-step (g) cube image data is saved in a cube image database, for use in construction of a fused cube image of sample 10.
  • Step (6) Step (4) through Step (5) are repeated, in the same field of view,
  • This step enables the acquisition of multi-layer spectral image data of sample 10.
  • multi-layer spectral image data of sample 10 For example, in FIG. 2, three cube images 30, 32, and 34, all in same FON, 24, are separately acquired using focal distances ⁇ z,, 36, ⁇ z, 2 38, and ⁇ z, 3 40, respectively.
  • This procedure illustrates the multi-layer spectral imaging of sample
  • Step (7) a single fused cube image 42, is constructed, using the cube image database of Step (5), sub-step (g), and using empirically determined background parameters, B median for selecting the background of fused cube image 42.
  • fused cube image 42 preferably, only high content Blobs 28 are retained and featured.
  • fused cube image data is saved in a fused cube image database, for use in image analysis algorithms (Step (9)).
  • Step (8) there is acquiring and constructing additional fused cube (spectral) images of sample 10 in other fields of view, FON j , by repeating Step (3) through Step (7), until the selected sample viewing / imaging range is imaged. This is, in part, accomplished by programmed movement of translation stage 20 to other fields of view over sample 10, and in each field of view, incremental movement of translation stage
  • Step (9) one or more image analysis algorithms are applied to the database of fused cube images.
  • the plurality of fused cube images are analyzed for spectral fmge ⁇ rints, whereby spectral data is related to applicable physicochemical and biological characteristics of sample 10.
  • detection, classification, and/or decision algorithms are used for image analysis of the fused cube image data.
  • Examples of specific detection, classification, and/or decision algorithms suitable for image analysis in the method of the present invention are fully described in U.S. Patent No. 5,880,830, issued to Schechter, and in pending U.S. Patent Application No. 09/146,361 , and references cited therein, which are inco ⁇ orated by reference for all pu ⁇ oses as if fully set forth herein.
  • Calibration data of standard samples with known physicochemical, biological, and spectral imaging characteristics are used as part of the image analysis.
  • image analysis is based on uniquely combining physicochemical, for example, mo ⁇ hological, and, chemical and biological composition data, with multi-layer spectral imaging data of sample 10 featuring particles. This unique combination enables achievement of high levels of accuracy and precision in detection and classification of the sample, in general, and of the featured particles, in particular.
  • sub-step (b) a statistical analysis report of the image analysis results is generated.
  • Step (10) Step (3) through Step (9) are repeated for each pre-defined time interval, ⁇ t.
  • Step (a) a report relating to time variation of the physicochemical, biological, and spectral imaging characteristics of sample 10 featuring particles is generated. This step further enables achievement of high levels of accuracy and precision in detection and classification of the sample.

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Abstract

Cette invention a trait à une technique d'imagerie spectrale in situ multicouche à fusion focale (2) ainsi qu'à une analyse d'échantillons particulaires tributaires de la profondeur. On utilise, pour construire une image spectrale cubique focalisée de quasiment chaque particule dans l'échantillon, une seule technique de fusion focale appliquée à des images focalisées et défocalisées acquises par imagerie spectrale multicouche (2) d'un échantillon particulaire tributaire de la profondeur. Cette technique repose sur l'utilisation d'un paramètre (5c) de facteur de fusion focale, défini et calculé de manière unique, qui associe des caractéristiques (5b) morphologiques et biologiques de particule, déterminées de façon empirique, à des caractéristiques spectrales de particule déterminées de façon empirique, lequel paramètre est utilisé dans des étapes critiques de détection d'image et de classement d'analyse d'image (9). Ce paramètre, déterminé de manière unique, permet d'obtenir des niveaux élevés d'exactitude et de précision en matière de détection et de classement de l'échantillon en général et des particules caractérisées en particulier.
PCT/US2000/014312 1999-06-01 2000-05-25 Technique d'imagerie spectrale $i(in situ) multicouche a fusion focale et analyse d'echantillons particulaires Ceased WO2000073977A1 (fr)

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AU51608/00A AU5160800A (en) 1999-06-01 2000-05-25 Method for in-situ focus-fusion multi-layer spectral imaging and analysis of particulate samples
EP00936267A EP1190372A1 (fr) 1999-06-01 2000-05-25 Technique d'imagerie spectrale $i(in situ) multicouche a fusion focale et analyse d'echantillons particulaires

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US32297599A 1999-06-01 1999-06-01
US09/322,975 1999-06-01

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US9816940B2 (en) 2015-01-21 2017-11-14 Kla-Tencor Corporation Wafer inspection with focus volumetric method
CN114166700A (zh) * 2021-11-26 2022-03-11 哈尔滨工程大学 一种颗粒群间液桥融合现象观测装置及方法

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Publication number Priority date Publication date Assignee Title
CN103632160A (zh) * 2012-08-24 2014-03-12 孙琤 一种融合多尺度形态学特征的组合核函数rvm高光谱分类方法
CN103632160B (zh) * 2012-08-24 2017-01-18 孙琤 一种融合多尺度形态学特征的组合核函数rvm高光谱分类方法
US9816940B2 (en) 2015-01-21 2017-11-14 Kla-Tencor Corporation Wafer inspection with focus volumetric method
CN114166700A (zh) * 2021-11-26 2022-03-11 哈尔滨工程大学 一种颗粒群间液桥融合现象观测装置及方法
CN114166700B (zh) * 2021-11-26 2023-07-21 哈尔滨工程大学 一种颗粒群间液桥融合现象观测装置及方法

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