US20070091109A1 - Image quality - Google Patents

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Publication number
US20070091109A1
US20070091109A1 US11/520,444 US52044406A US2007091109A1 US 20070091109 A1 US20070091109 A1 US 20070091109A1 US 52044406 A US52044406 A US 52044406A US 2007091109 A1 US2007091109 A1 US 2007091109A1
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Prior art keywords
image
color space
method
device
microscope
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Abandoned
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US11/520,444
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Roscoe Atkinson
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Carl Zeiss Microscopy GmbH
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ChromaVision Medical Systems Inc
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Priority to US71688705P priority Critical
Application filed by ChromaVision Medical Systems Inc filed Critical ChromaVision Medical Systems Inc
Priority to US11/520,444 priority patent/US20070091109A1/en
Assigned to CLARIENT, INC. reassignment CLARIENT, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: ATKINSON, ROSCOE
Publication of US20070091109A1 publication Critical patent/US20070091109A1/en
Assigned to CARL ZEISS MICROIMAGING AIS, INC. reassignment CARL ZEISS MICROIMAGING AIS, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: CLARIENT, INC.
Application status is Abandoned legal-status Critical

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    • GPHYSICS
    • G09EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09GARRANGEMENTS OR CIRCUITS FOR CONTROL OF INDICATING DEVICES USING STATIC MEANS TO PRESENT VARIABLE INFORMATION
    • G09G5/00Control arrangements or circuits for visual indicators common to cathode-ray tube indicators and other visual indicators
    • G09G5/02Control arrangements or circuits for visual indicators common to cathode-ray tube indicators and other visual indicators characterised by the way in which colour is displayed
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/0059Detecting, measuring or recording for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01JMEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRA-RED, VISIBLE OR ULTRA-VIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/46Measurement of colour; Colour measuring devices, e.g. colorimeters
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01JMEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRA-RED, VISIBLE OR ULTRA-VIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/46Measurement of colour; Colour measuring devices, e.g. colorimeters
    • G01J3/462Computing operations in or between colour spaces; Colour management systems
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01JMEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRA-RED, VISIBLE OR ULTRA-VIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/46Measurement of colour; Colour measuring devices, e.g. colorimeters
    • G01J3/463Colour matching
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00Image enhancement or restoration
    • G06T5/50Image enhancement or restoration by the use of more than one image, e.g. averaging, subtraction
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01JMEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRA-RED, VISIBLE OR ULTRA-VIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/46Measurement of colour; Colour measuring devices, e.g. colorimeters
    • G01J2003/467Colour computing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10032Satellite or aerial image; Remote sensing
    • G06T2207/10036Multispectral image; Hyperspectral image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10056Microscopic image

Abstract

A magnified image is improved by integrating the wavelength specific component into that image. A magnified images obtained, and at least one wavelength specific component images also obtained. The different images are converted in color space, and different channels, indicative of the different parts of the image shows, are also obtained. For example, the image may be converted to and L*a*b* color space, and the luminance channel of the wavelength specific component may be used to enhance or replace the luminance channel of the magnified image.

Description

    CROSS-REFERENCE TO RELATED APPLICATIONS
  • This application claims priority to U.S. Provisional Application Ser. No. 60/716,887, filed on Sep. 13, 2005. The disclosure of the prior application is considered part of (and is incorporated by reference in) the disclosure of this application.
  • BACKGROUND
  • Pathology often requires viewing microscope images. The resolution of the microscope images from the imaging system. This is often limited by different parameters of obtaining the image. For example, the resolution may be limited by the time it takes to scan a tissue section and by the resulting image file size.
  • DESCRIPTION OF THE DRAWINGS
  • FIG. 1 shows a flowchart;
  • FIG. 2 illustrates the progression of the different image;
  • FIG. 3 illustrates an exemplary hardware setup which can be used;
  • FIG. 4 a and 4 b show examples of the different images for colon cancer; and
  • FIG. 5 a through 5 d show examples of the different images for a breast cancer cell.
  • DETAILED DESCRIPTION
  • The general structure and techniques, and more specific embodiments which can be used to effect different ways of carrying out the more general goals, are described herein.
  • The number of image elements within an obtained image from a tissue section increases exponentially between different microscope objectives. For example, the image at 10× may require exponentially more storage than the image at 4×. The time that is required to scan the tissue section at 60× may be excessive. Therefore, many believe that capturing a large image at 60× is not practical. The time required to scan a tissue section at 60× is extremely large, and the amount of digital storage space required for such a scan is also large. This may limit the number of scans that can be obtained and reviewed.
  • An embodiment describes use of a multi spectral imaging system, such as the Nuance Multispectral Imaging System available from Cambridge Research & Instrumentation (“Nuance”) in combination with an automated microscope such as the Automated Cellular Imaging System (“ACIS”) provided by Clarient Inc. The processing provides an effective augmentation of images at lower magnifications, to attempt to obtain additional information from those images at lower magnifications. In an embodiment, image augmentation is carried out by extracting images at specific color wavelengths, converting color spaces, and carrying out channel mixing in a converted color space.
  • FIG. 3 illustrates an exemplary hardware setup which can be used. The sample 300 is on a sample table 305 as conventional. The ACIS or other automated microscope 310 obtains image information from the sample 300. A single spectrum camera 315 also obtains information. All of the information is coupled to a computer 320 which operates as described herein and specifically according to the flowcharts of FIGS. 1 and 2.
  • A color space is a model for representing color in terms of intensity values; a color space specifies how color information is represented. It defines a one, two, three, or four-dimensional space whose dimensions, or components, represent intensity values. A color component is also referred to as a color channel. For example, RGB space is a three-dimensional color space whose components are the red, green, and blue intensities that make up a given color. Visually, these spaces are often represented by various solid shapes, such as cubes, cones, or polyhedral.
  • Different kinds of color spaces are known.
  • Gray spaces typically have a single component, ranging from black to white. The RGB space is a three-dimensional color space whose components are the red, green, and blue intensities that make up a given color. For example, scanners read the amounts of red, green, and blue light that are reflected from an image and then convert those amounts into digital values. Displays receive the digital values and convert them into red, green, and blue light seen on a screen.
  • RGB-based color spaces are the most commonly used color spaces in computer graphics, primarily because they are directly supported by most color displays and scanners. RGB color spaces are device dependent and additive. The groups of color spaces within the RGB base family include HSV (hue, saturation, value) and HLS (hue, lightness, saturation) spaces. The saturation component in both color spaces describes color intensity. A saturation value of 0 (in the middle of a hexagon) means that the color is “colorless” (gray); a saturation value at the maximum (at the outer edge of a hexagon) means that the color is at maximum “colorfulness” for that hue angle and brightness. The value component (in HSV space) and the lightness component (in HLS space) describe brightness or luminance. In both color spaces, a value of 0 represents black. In HSV space, a maximum value means that the color is at its brightest. In HLS space, a maximum value for lightness means that the color is white, regardless of the current values of the hue and saturation components. The brightest, most intense color in HLS space occurs at a lightness value of exactly half the maximum.
  • CMY color spaces are like the above, but define the colors additively.
  • Any color expressed in RGB space is some mixture of three primary colors: red, green, and blue. Most RGB-based color spaces can be visualized as a cube, with corners of black, the three primaries (red, green, and blue), the three secondaries (cyan, magenta, and yellow), and white.
  • Some color spaces can express color in a device-independent way. Whereas RGB colors vary with display and scanner characteristics, and CMYK colors vary with printer, ink, and paper characteristics, device-independent colors are meant to be true representations of colors as perceived by the human eye. These color representations, called device-independent color spaces, result from work carried out in 1931 by the Commission Internationale d'Eclairage (CIE), and for that reason are also also called CIE-based color spaces.
  • In the L*a*b color space, the L*a*b* space consists of a luminosity ‘L*’ or brightness layer, chromaticity layer ‘a*’, indicating where color falls along the red-green axis, and chromaticity layer ‘b*’ indicating where the color falls along the blue-yellow axis.
  • An embodiment is described herein. The embodiment can be carried out automatically using a robotic or computer-controlled system. Alternatively, some parts of the embodiment, such as the staining or the input of data into machines, can be carried out manually.
  • At 100, the system obtains a number of different images, including a first microscopic image, and at least one single spectrum image. Preferably, a plurality of different single spectrum images are obtained. FIG. 2 also illustrates the different images, including the color image 200 from the microscope or from the spectral camera, and a single spectrum image 205 from the spectral camera.
  • Colon cancer tissue sections may be examined in this embodiment. These sections are in fixed paraffin and stained with HER2 stain. A multispectral camera, which in the embodiment can be the Nuance camera, is used to examine the tissue sections. The Nuance camera is mounted on an ACIS microscope.
  • For purposes of the embodiment, color RGB images are obtained at any magnification, e.g., 4×, 10×, 20× and/or 60×. Grayscale images of the exact same fields are also captured at near ultraviolet (420 nm) and near infrared (720 nm). Physics dictates that resolution is inversely proportional to wavelength. One would therefore predict that the 420 nm image would have better resolution than any of the RGB channels of the original color images.
  • The images obtained from the cameras are in an RGB based color space. At 110, the images are converted into a device independent color space which includes a luminance component. More specifically, in the embodiment, the devices are converted into the L*a*b* color space. FIG. 2 shows the image 200 being converted into the new color space image 210, and the image 205 being converted into the new color space image 215. This color space conversion may use commercially available software or modules.
  • At 120, the channels of the new images are separated. In FIG. 2, image 210 is divided into separated channels, the L* channel 220, the a* channel 221 and the b* channel 222. Similarly, the image 215 is converted into its separate channels representing separate image parts, 225, 226 and 227.
  • In the embodiment, only the luminance information from the single spectrum image 205 is used. Accordingly, at 130, the luminance channels from the image 200 are replaced by the luminance channel from their corresponding 420 nm image 205. The channels are then premixed at 140 to create the image 240, and then are transformed back to another color space transformation at 150 such as RGB, HSI, or any other color space of a type that may facilitate the display.
  • According to the embodiment, it was found that the new image provided more detailed than the original. In order to test the importance of the 420 nm image, the same process was done using a 720 nm spectral image in place of the 420 nm image. The resulting images were of poor quality.
  • Another embodiment tested immunohistochemical stained tissue. This tissue test was a breast-cancer test tissue stained with Her2/neu, using diainobenzidine (“DAB”) secondary, and a hematoxylin counterstain. Surprisingly, this process increased the detail of the hematoxylin stained counterstain tissue but greatly reduced the information carried by the stained cancer tissue, which became less interpretable.
  • The inventor believes that the brown DAB based secondary stain contains a great deal of red color. Therefore, the 720 nm process was applied with very good results. The DAB stained tissue showed an increase detail at ends the background of slightly decreased background detail.
  • Therefore, the different convergences between different kinds of color are important. FIGS. 4 a and 4 b show examples of the different images for colon cancer. FIGS. 5 a through 5 d show examples of the different images for a breast cancer cell.
  • The embodiment describes only two different single spectrum images, but other embodiments may use a different luminance convert step 130 which uses a plurality of different single spectrum images, or some kind of combined single spectrum image which is combined by transforming and weighting the number of different images together.
  • The general structure and techniques, and more specific embodiments which can be used to effect different ways of carrying out the more general goals are described herein.
  • Although only a few embodiments have been disclosed in detail above, other embodiments are possible and the inventor (s) intend these to be encompassed within this specification. The specification describes specific examples to accomplish a more general goal that may be accomplished in another way. This disclosure is intended to be exemplary, and the claims are intended to cover any modification or alternative which might be predictable to a person having ordinary skill in the art. For example, other stains and colors may be used. Other single spectrum images, or images that are multispectrum or narrow spectrum can also be used. Moreover, when specific values, such as 420 nm, are given herein, those specific values are intended to be center values within a range of 10-20%, for example.
  • Also, the inventor intends that only those claims which use the words “means for” are intended to be interpreted under 35 USC 112, sixth paragraph. Moreover, no limitations from the specification are intended to be read into any claims, unless those limitations are expressly included in the claims. The computers described herein may be any kind of computer, either general purpose, or some specific purpose computer such as a workstation. The computer may be a Pentium class computer, running Windows XP or Linux, or may be a Macintosh computer. The computer may also be a handheld computer, such as a PDA, cellphone, or laptop.
  • The programs may be written in C, or Java, Brew or any other programming language. The programs may be resident on a storage medium, e.g., magnetic or optical, e.g. the computer hard drive, a removable disk or media such as a memory stick or SD media, or other removable medium, the programs may also be run over a network, for example, with a server or other machine sending signals to the local machine, which allows the local machine to carry out the operations described herein.

Claims (20)

1. A method, comprising:
obtaining a first image comprising a microscope image indicative of a sample;
obtaining a second image indicative of the same sample but which covers substantially only a single spectrum;
dividing said second image into component parts indicative of the second image; and
using at least one of said component parts to enhance said first image.
2. A method as in claim 1, wherein said information from said second image is a luminance component from the second image which is used to modify a luminance component in the first image.
3. A method as in claim 1, further comprising converting said microscope image in said second image to a second color space, separating channels in said separate color space, and using at least one of said channels from said second image to replace at least one of said channels in said microscope image.
4. A method as in claim 3, wherein said second color space is a device independent color space.
5. A method as in claim 3, wherein said second color space is an L*a*b* color space.
6. A method as in claim 3, after said using, combining said channels to form a new image, and transforming said new image to another color space.
7. A method as in claim 1, wherein said second image is at substantially 420 nm.
8. A method as in claim 1, wherein said second image is at 720 nm.
9. A device comprising:
a computer, obtaining an image of a microscope, and obtaining another image having a relationship to said microscope, but over substantially only a single spectrum, said computer operating to provide information from the another image into component parts indicative of the another image and to use information from at least one of said component parts, but not all of said component parts to enhance said image from said microscope.
10. A device as in claim 9, further comprising a microscope, producing said image.
11. A device as in claim 9, further comprising a single spectrum camera, producing at least one output indicative of a single spectrum image.
12. A device as in claim 10, further comprising a multi-spectrum camera, which produces a number of output images, each representative of a single spectrum image.
13. A device as in claim 9, wherein said at least one component part is a component part indicative of luminance within the image.
14. A device as in claim 9, wherein said computer further operates to convert said microscope image and said another image, into a device independent color space.
15. A device as in claim 14, wherein said second color space is an L*a*b* color space.
16. A device as in claim 9, wherein said computer operates to combine said component parts to form a new image, and to transform said new image to another color space.
17. A method as in claim 11, wherein said single spectrum image is at substantially 420 nm.
18. A method as in claim 11, wherein said single spectrum image is at 720 nm.
19. A method, comprising:
obtaining a first image from a first camera indicative of full color image of a magnified sample;
obtaining a second image indicative of a single spectrum image of the magnified sample;
converting said first and second images into a device independent color space;
separating channels of the first and second images in said device independent color space;
using at least one of said channels of the second image to enhance a quality of said first image; and
creating a new image based on said first image, and said at least one of said channels.
20. A method as in claim 19, wherein said using comprises using said one channel from said a second image to replace a corresponding channel in said first image.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090185267A1 (en) * 2005-09-22 2009-07-23 Nikon Corporation Microscope and virtual slide forming system
US20100021077A1 (en) * 2005-09-13 2010-01-28 Roscoe Atkinson Image quality

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102011075809A1 (en) * 2011-05-13 2012-11-15 Carl Zeiss Microimaging Gmbh A method and apparatus for determining a z-region in a sample in which record a z-stack of the sample by a microscope
JP6234970B2 (en) * 2015-07-22 2017-11-22 日東電工株式会社 Transparent conductive layer with cover member having a transparent pressure-sensitive adhesive layer
JP6060223B1 (en) * 2015-07-22 2017-01-11 日東電工株式会社 A cover member having a transparent pressure-sensitive adhesive layer
JP2017143759A (en) * 2016-02-16 2017-08-24 株式会社Screenホールディングス Cell observation apparatus and cell observation method

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5241468A (en) * 1989-04-13 1993-08-31 Vanguard Imaging Ltd. Apparatus and method for spectral enhancement of body-surface images to improve sensitivity of detecting subtle color features
US20010036304A1 (en) * 2000-01-22 2001-11-01 Yang Mary M. Visualization and processing of multidimensional data using prefiltering and sorting criteria
US20020141640A1 (en) * 2001-02-09 2002-10-03 Walter Kraft Local digital image property control with masks
US20030053690A1 (en) * 2001-07-06 2003-03-20 Jasc Software, Inc. Automatic contrast enhancement
US20040017586A1 (en) * 2002-04-23 2004-01-29 Mutsuko Nichogi Apparatus and method for editing colors
US20040209178A1 (en) * 2003-03-07 2004-10-21 Takayuki Itakura Cyan toner and method for forming an image
US20050100189A1 (en) * 2002-09-24 2005-05-12 Hideaki Yamada Image processing apparatus
US20050111017A1 (en) * 2002-07-30 2005-05-26 Canon Kabushiki Kaisha Image processing system, apparatus, and method, and color reproduction method
US20050180628A1 (en) * 2004-02-12 2005-08-18 Xerox Corporation Systems and methods for identifying regions within an image having similar continuity values
US20070092135A1 (en) * 2004-06-17 2007-04-26 Softcolor Oy Method and device for colour calibrating a camera and/or a display device and for correcting colour defects from digital images

Family Cites Families (122)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3824393A (en) * 1971-08-25 1974-07-16 American Express Invest System for differential particle counting
US4125828A (en) 1972-08-04 1978-11-14 Med-El Inc. Method and apparatus for automated classification and analysis of cells
US3851972A (en) 1973-10-18 1974-12-03 Coulter Electronics Automatic method and system for analysis and review of a plurality of stored slides
US4011004A (en) * 1975-02-06 1977-03-08 Geometric Data Corporation Pivotable stage for microscope system
JPS5811562B2 (en) * 1975-05-08 1983-03-03 Matsushita Electric Ind Co Ltd
US4249825A (en) * 1979-05-14 1981-02-10 The Trustees Of Boston University Method and apparatus suitable for ocular blood flow analysis
US4393466A (en) * 1980-09-12 1983-07-12 International Remote Imaging Systems Method of analyzing particles in a dilute fluid sample
US4513438A (en) * 1982-04-15 1985-04-23 Coulter Electronics, Inc. Automated microscopy system and method for locating and re-locating objects in an image
JPH0214748B2 (en) * 1983-12-08 1990-04-09 Kubota Ltd
US4700298A (en) 1984-09-14 1987-10-13 Branko Palcic Dynamic microscope image processing scanner
US4673973A (en) * 1985-02-04 1987-06-16 National Biomedical Research Foundation Split-image, multi-power microscopic image display system and method
US5202931A (en) * 1987-10-06 1993-04-13 Cell Analysis Systems, Inc. Methods and apparatus for the quantitation of nuclear protein
US5281517A (en) * 1985-11-04 1994-01-25 Cell Analysis Systems, Inc. Methods for immunoploidy analysis
US4740270A (en) * 1986-06-19 1988-04-26 Universite Laval Vacuum pyrolysis of scrap tires
JPH0774856B2 (en) * 1986-10-16 1995-08-09 オリンパス光学工業株式会社 Automatic focus adjustment method
US5121436A (en) * 1987-08-14 1992-06-09 International Remote Imaging Systems, Inc. Method and apparatus for generating a plurality of parameters of an object in a field of view
US5085325A (en) * 1988-03-08 1992-02-04 Simco/Ramic Corporation Color sorting system and method
US4965725B1 (en) * 1988-04-08 1996-05-07 Neuromedical Systems Inc Neural network based automated cytological specimen classification system and method
US4991223A (en) * 1988-06-30 1991-02-05 American Innovision, Inc. Apparatus and method for recognizing image features using color elements
FR2638240B1 (en) * 1988-10-21 1991-10-18 Biocom Sa Platinum for the rapid analysis and indexed search under microscope and other holders of multiple sample holders and method for analyzing these samples using the platinum
CA2003131C (en) * 1988-11-25 1998-06-23 Seigo Igaki Biological object detection apparatus
US5016173A (en) * 1989-04-13 1991-05-14 Vanguard Imaging Ltd. Apparatus and method for monitoring visually accessible surfaces of the body
US5087965A (en) 1989-06-28 1992-02-11 American Innovision, Inc. Recognition of image colors using arbitrary shapes in color space
US5233684A (en) * 1990-06-26 1993-08-03 Digital Equipment Corporation Method and apparatus for mapping a digital color image from a first color space to a second color space
US5235522A (en) * 1990-10-10 1993-08-10 Cell Analysis Systems, Inc. Method and apparatus for automated analysis of biological specimens
US5257182B1 (en) 1991-01-29 1996-05-07 Neuromedical Systems Inc Morphological classification system and method
US5231580A (en) * 1991-04-01 1993-07-27 The United States Of America As Represented By The Secretary Of The Department Of Health And Human Services Automated method and apparatus for determining characteristics of nerve fibers
JP3324780B2 (en) * 1991-05-16 2002-09-17 オリンパス光学工業株式会社 Ultraviolet microscope
CA2077781A1 (en) 1991-09-23 1993-03-24 James W. Bacus Method and apparatus for automated assay of biological specimens
US5428690A (en) 1991-09-23 1995-06-27 Becton Dickinson And Company Method and apparatus for automated assay of biological specimens
US5375177A (en) 1991-09-27 1994-12-20 E. I. Du Pont De Nemours And Company Method of identifying and characterizing a valid object by color
US5463470A (en) 1991-10-09 1995-10-31 Fuji Photo Film Co., Ltd. Methods of collecting photometric image data and determining light exposure by extracting feature image data from an original image
US5635402A (en) * 1992-03-05 1997-06-03 Alfano; Robert R. Technique for determining whether a cell is malignant as opposed to non-malignant using extrinsic fluorescence spectroscopy
US5533628A (en) * 1992-03-06 1996-07-09 Agri Tech Incorporated Method and apparatus for sorting objects by color including stable color transformation
DE4221807C2 (en) * 1992-07-03 1994-07-14 Boehringer Mannheim Gmbh A method for the analytical determination of the concentration of a component of a medical sample
US5317140A (en) * 1992-11-24 1994-05-31 Dunthorn David I Diffusion-assisted position location particularly for visual pen detection
JP3124407B2 (en) * 1993-02-09 2001-01-15 セイコーインスツルメンツ株式会社 Fluorescent x-ray film thickness meter
DE69420874T2 (en) 1993-02-12 2000-05-04 Eastman Kodak Co A process for cross-device Color Calibration and improvement
JP3129015B2 (en) 1993-02-16 2001-01-29 株式会社日立製作所 Inspection method and apparatus of the stained particles
US5907315A (en) * 1993-03-17 1999-05-25 Ultimatte Corporation Method and apparatus for adjusting parameters used by compositing devices
US6288703B1 (en) 1996-11-25 2001-09-11 Ultimatte Corporation Method for removing from an image the background surrounding a selected subject by generating candidate mattes
US5602941A (en) * 1993-05-21 1997-02-11 Digital Equipment Corporation Input modification system for multilevel dithering
US5515172A (en) * 1993-07-19 1996-05-07 Xerox Corporation Apparatus and method for enhanced color to color conversion
US5784162A (en) 1993-08-18 1998-07-21 Applied Spectral Imaging Ltd. Spectral bio-imaging methods for biological research, medical diagnostics and therapy
US5432871A (en) * 1993-08-04 1995-07-11 Universal Systems & Technology, Inc. Systems and methods for interactive image data acquisition and compression
US6165734A (en) 1995-12-12 2000-12-26 Applied Spectral Imaging Ltd. In-situ method of analyzing cells
JP3039594B2 (en) 1993-10-08 2000-05-08 株式会社日立製作所 Staining reagents and methods of use thereof
US5409007A (en) * 1993-11-26 1995-04-25 General Electric Company Filter to reduce speckle artifact in ultrasound imaging
US5483055A (en) * 1994-01-18 1996-01-09 Thompson; Timothy V. Method and apparatus for performing an automatic focus operation for a microscope
US5400081A (en) * 1994-02-15 1995-03-21 The Grass Valley Group, Inc. Chroma keyer with correction for background defects
JP3134660B2 (en) 1994-04-14 2001-02-13 松下電器産業株式会社 A color conversion method and a color conversion device
US5795723A (en) * 1994-05-06 1998-08-18 Fred Hutchinson Cancer Research Center Expression of neurogenic bHLH genes in primitive neuroectodermal tumors
US5734498A (en) * 1994-05-09 1998-03-31 The Regents Of The University Of California Illuminator elements for conventional light microscopes
US5602937A (en) * 1994-06-01 1997-02-11 Cognex Corporation Methods and apparatus for machine vision high accuracy searching
US5625705A (en) * 1994-06-03 1997-04-29 Neuromedical Systems, Inc. Intensity texture based classification system and method
US5499097A (en) * 1994-09-19 1996-03-12 Neopath, Inc. Method and apparatus for checking automated optical system performance repeatability
US5647025A (en) * 1994-09-20 1997-07-08 Neopath, Inc. Automatic focusing of biomedical specimens apparatus
US5846749A (en) 1994-10-12 1998-12-08 The Regents Of The University Of California Quantitative measurement of tissue protein identified by immunohistochemistry and standardized protein determination
EP0713086B1 (en) 1994-11-17 1999-04-14 Chemunex Apparatus and process for the detection and counting of rarely occurring mammalian cells
US5671288A (en) 1995-05-31 1997-09-23 Neopath, Inc. Method and apparatus for assessing slide and specimen preparation quality
US5701172A (en) 1995-06-07 1997-12-23 Gas Research Institute Optical flowmeter
DK71495A (en) * 1995-06-22 1996-12-23 Purup Prepress As A method and apparatus for correcting color cast of the digital image
US5732150A (en) * 1995-09-19 1998-03-24 Ihc Health Services, Inc. Method and system for multiple wavelength microscopy image analysis
EP1426381A1 (en) * 1995-10-02 2004-06-09 Johns Hopkins University An epithelial protein and DNA thereof for use in early cancer detection
US6104839A (en) * 1995-10-16 2000-08-15 Eastman Kodak Company Method and apparatus for correcting pixel values in a digital image
US5981279A (en) 1995-11-09 1999-11-09 Allegheny University Of The Health Sciences Compositions and methods to regulate calmodulin gene expression, and uses thereof for influencing cell growth and differentiation
US6151405A (en) 1996-11-27 2000-11-21 Chromavision Medical Systems, Inc. System and method for cellular specimen grading
AT236386T (en) * 1995-11-30 2003-04-15 Chromavision Med Sys Inc sampling method for automatic image analysis of biological
JP3744989B2 (en) * 1995-12-05 2006-02-15 オリンパス株式会社 Color classification system and the area correction method
US6005964A (en) 1996-01-24 1999-12-21 The Board Of Trustees Of The University Of Illinois Automatic machine vision microscope slide inspection system and method
US5911003A (en) * 1996-04-26 1999-06-08 Pressco Technology Inc. Color pattern evaluation system for randomly oriented articles
US6103479A (en) * 1996-05-30 2000-08-15 Cellomics, Inc. Miniaturized cell array methods and apparatus for cell-based screening
US6716974B1 (en) * 1996-05-31 2004-04-06 Maine Medical Center Research Institute Therapeutic and diagnostic methods and compositions based on jagged/notch proteins and nucleic acids
JP3809226B2 (en) * 1996-06-06 2006-08-16 富士写真フイルム株式会社 Correction method of an output image signal of the linear image sensor
US6301382B1 (en) 1996-06-07 2001-10-09 Microsoft Corporation Extracting a matte of a foreground object from multiple backgrounds by triangulation
US5717518A (en) * 1996-07-22 1998-02-10 Kla Instruments Corporation Broad spectrum ultraviolet catadioptric imaging system
US6272235B1 (en) * 1997-03-03 2001-08-07 Bacus Research Laboratories, Inc. Method and apparatus for creating a virtual microscope slide
US6031930A (en) * 1996-08-23 2000-02-29 Bacus Research Laboratories, Inc. Method and apparatus for testing a progression of neoplasia including cancer chemoprevention testing
US6404906B2 (en) * 1997-03-03 2002-06-11 Bacus Research Laboratories,Inc. Method and apparatus for acquiring and reconstructing magnified specimen images from a computer-controlled microscope
US5911327A (en) * 1996-10-02 1999-06-15 Nippon Steel Corporation Method of automatically discriminating and separating scraps containing copper from iron scraps
JP2001511247A (en) * 1996-12-23 2001-08-07 ルプレヒト−カールス−ウニヴェルジテート ハイデルベルク Distance measuring method and distance measuring device between the object structure
JPH10319517A (en) * 1997-05-21 1998-12-04 Konica Corp Image forming device and manufacture of film unit with lens
WO1998055026A1 (en) 1997-06-05 1998-12-10 Kairos Scientific Inc. Calibration of fluorescence resonance energy transfer in microscopy
US6058322A (en) * 1997-07-25 2000-05-02 Arch Development Corporation Methods for improving the accuracy in differential diagnosis on radiologic examinations
US6011595A (en) * 1997-09-19 2000-01-04 Eastman Kodak Company Method for segmenting a digital image into a foreground region and a key color region
US5937104A (en) * 1997-09-19 1999-08-10 Eastman Kodak Company Combining a first digital image and a second background digital image using a key color control signal and a spatial control signal
US6122400A (en) 1997-09-26 2000-09-19 Sarnoff Corporation Compression encoder bit allocation utilizing colormetric-adaptive weighting as in flesh-tone weighting
US6208753B1 (en) * 1998-02-27 2001-03-27 International Business Machines Corporation Quality of digitized images through post-scanning reregistration of their color planes
US6183981B1 (en) * 1998-03-05 2001-02-06 Board Of Regents, The University Of Texas System Diagnostic assay for late-onset Alzheimer's disease
US6603885B1 (en) * 1998-04-30 2003-08-05 Fuji Photo Film Co., Ltd. Image processing method and apparatus
EP1080444A4 (en) * 1998-05-18 2002-02-13 Datacube Inc Image recognition and correlation system
US6313452B1 (en) 1998-06-10 2001-11-06 Sarnoff Corporation Microscopy system utilizing a plurality of images for enhanced image processing capabilities
US6542187B1 (en) * 1998-07-09 2003-04-01 Eastman Kodak Company Correcting for chrominance interpolation artifacts
JP3758377B2 (en) * 1998-09-14 2006-03-22 コニカミノルタビジネステクノロジーズ株式会社 Image reading apparatus and the chromatic aberration correcting method
DE19842572B4 (en) * 1998-09-17 2005-03-24 Heidelberger Druckmaschinen Ag Method for the automatic removal of image defects
US6611289B1 (en) * 1999-01-15 2003-08-26 Yanbin Yu Digital cameras using multiple sensors with multiple lenses
US7339699B1 (en) * 1999-02-03 2008-03-04 Minolta Co., Ltd. Image processing apparatus
JP3053620B1 (en) 1999-02-25 2000-06-19 株式会社コミュータヘリコプタ先進技術研究所 Russia - Tablet - de of the flap drive unit
US6103518A (en) * 1999-03-05 2000-08-15 Beecher Instruments Instrument for constructing tissue arrays
US6418236B1 (en) * 1999-06-24 2002-07-09 Chromavision Medical Systems, Inc. Histological reconstruction and automated image analysis
US6453060B1 (en) 1999-06-29 2002-09-17 Tri Path Imaging, Inc. Method and apparatus for deriving separate images from multiple chromogens in a branched image analysis system
WO2001015452A1 (en) * 1999-07-13 2001-03-01 Chromavision Medical Systems, Inc. Device for applying accurate color thresholds in real time
US6404916B1 (en) * 1999-08-04 2002-06-11 Chromavision Medical Systems, Inc. Method and apparatus for applying color thresholds in light microscopy
JP3777906B2 (en) * 1999-09-29 2006-05-24 コニカミノルタビジネステクノロジーズ株式会社 Image processing apparatus
EP1218547A4 (en) 1999-10-15 2005-04-20 Ventana Med Syst Inc Method of detecting single gene copies in-situ
US6716588B2 (en) * 1999-12-09 2004-04-06 Cellomics, Inc. System for cell-based screening
US6727225B2 (en) * 1999-12-20 2004-04-27 Immunex Corporation TWEAK receptor
GB9930156D0 (en) 1999-12-22 2000-02-09 Secr Defence Optical system interface
US6962789B2 (en) 2000-01-12 2005-11-08 Ventana Medical Systems, Inc. Method for quantitating a protein by image analysis
US6942970B2 (en) 2000-09-14 2005-09-13 Zymed Laboratories, Inc. Identifying subjects suitable for topoisomerase II inhibitor treatment
US20030017491A1 (en) * 2000-09-14 2003-01-23 Zuo-Rong Shi Chromogenic in situ hybridization methods, kits, and compositions
AUPR376101A0 (en) * 2001-03-15 2001-04-12 Vision Biosystems Limited Slide holder for an automated slide loader
US7180642B2 (en) * 2001-05-25 2007-02-20 Ricoh Company, Ltd. Imaging lens, image reader using the lens, and imaging apparatus using the image reader
US7065236B2 (en) * 2001-09-19 2006-06-20 Tripath Imaging, Inc. Method for quantitative video-microscopy and associated system and computer software program product
US6697509B2 (en) * 2001-10-04 2004-02-24 Chromavision Medical Systems, Inc. Method and apparatus for scoring the uptake of markers in cells
US7133547B2 (en) * 2002-01-24 2006-11-07 Tripath Imaging, Inc. Method for quantitative video-microscopy and associated system and computer software program product
WO2005036877A1 (en) 2003-09-12 2005-04-21 Nielsen Media Research, Inc. Digital video signature apparatus and methods for use with video program identification systems
US20060041385A1 (en) * 2004-08-18 2006-02-23 Bauer Kenneth D Method of quantitating proteins and genes in cells using a combination of immunohistochemistry and in situ hybridization
US20060178833A1 (en) 2005-02-04 2006-08-10 Bauer Kenneth D System for and method of providing diagnostic information through microscopic imaging
US8467583B2 (en) * 2005-04-04 2013-06-18 Yissum Research Development Company Of The Hebrew University Of Jerusalem Ltd. Medical imaging method and system
US7627153B2 (en) * 2005-08-04 2009-12-01 Carl Zeiss Microimaging Gmbh Repositioning inaccuracies in an automated imaging system
US20070091109A1 (en) * 2005-09-13 2007-04-26 Roscoe Atkinson Image quality

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5241468A (en) * 1989-04-13 1993-08-31 Vanguard Imaging Ltd. Apparatus and method for spectral enhancement of body-surface images to improve sensitivity of detecting subtle color features
US20010036304A1 (en) * 2000-01-22 2001-11-01 Yang Mary M. Visualization and processing of multidimensional data using prefiltering and sorting criteria
US20050114801A1 (en) * 2000-01-22 2005-05-26 Yang Mary M. Visualization and processing of multidimensional data using prefiltering and sorting criteria
US20020141640A1 (en) * 2001-02-09 2002-10-03 Walter Kraft Local digital image property control with masks
US20030053690A1 (en) * 2001-07-06 2003-03-20 Jasc Software, Inc. Automatic contrast enhancement
US20040017586A1 (en) * 2002-04-23 2004-01-29 Mutsuko Nichogi Apparatus and method for editing colors
US20050111017A1 (en) * 2002-07-30 2005-05-26 Canon Kabushiki Kaisha Image processing system, apparatus, and method, and color reproduction method
US20050100189A1 (en) * 2002-09-24 2005-05-12 Hideaki Yamada Image processing apparatus
US20040209178A1 (en) * 2003-03-07 2004-10-21 Takayuki Itakura Cyan toner and method for forming an image
US20050180628A1 (en) * 2004-02-12 2005-08-18 Xerox Corporation Systems and methods for identifying regions within an image having similar continuity values
US20070092135A1 (en) * 2004-06-17 2007-04-26 Softcolor Oy Method and device for colour calibrating a camera and/or a display device and for correcting colour defects from digital images

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100021077A1 (en) * 2005-09-13 2010-01-28 Roscoe Atkinson Image quality
US8817040B2 (en) 2005-09-13 2014-08-26 Carl Zeiss Microscopy Gmbh Methods for enhancing image quality
US20090185267A1 (en) * 2005-09-22 2009-07-23 Nikon Corporation Microscope and virtual slide forming system

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