WO2002015788A1 - Systeme, procede et programme informatique pour l'analyse d'une image spectrale - Google Patents

Systeme, procede et programme informatique pour l'analyse d'une image spectrale Download PDF

Info

Publication number
WO2002015788A1
WO2002015788A1 PCT/US2001/026653 US0126653W WO0215788A1 WO 2002015788 A1 WO2002015788 A1 WO 2002015788A1 US 0126653 W US0126653 W US 0126653W WO 0215788 A1 WO0215788 A1 WO 0215788A1
Authority
WO
WIPO (PCT)
Prior art keywords
image
screening
spectral image
reflected
reflected spectral
Prior art date
Application number
PCT/US2001/026653
Other languages
English (en)
Other versions
WO2002015788A9 (fr
Inventor
Eric P. Krotkov
Yongping Wang
Zhang Zhong
Robert M. Danen
Original Assignee
Krotkov Eric P
Yongping Wang
Zhang Zhong
Danen Robert M
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Krotkov Eric P, Yongping Wang, Zhang Zhong, Danen Robert M filed Critical Krotkov Eric P
Priority to AU2001286794A priority Critical patent/AU2001286794A1/en
Priority to US10/362,643 priority patent/US20040024296A1/en
Publication of WO2002015788A1 publication Critical patent/WO2002015788A1/fr
Publication of WO2002015788A9 publication Critical patent/WO2002015788A9/fr

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/14535—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring haematocrit
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/41—Detecting, measuring or recording for evaluating the immune or lymphatic systems
    • A61B5/411—Detecting or monitoring allergy or intolerance reactions to an allergenic agent or substance
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/47—Scattering, i.e. diffuse reflection
    • G01N21/49—Scattering, i.e. diffuse reflection within a body or fluid
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7253—Details of waveform analysis characterised by using transforms
    • A61B5/7257—Details of waveform analysis characterised by using transforms using Fourier transforms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7253—Details of waveform analysis characterised by using transforms
    • A61B5/726—Details of waveform analysis characterised by using transforms using Wavelet transforms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems

Definitions

  • the present invention relates generally to reflected light analysis. More particularly, the invention relates to the use of reflected spectral imaging to analyze visualizable components of a fluid flowing in a tubular system. Still more particularly, the invention relates to screening reflected spectral images to analyze visualizable components of fluids in a vascular system.
  • CBC+Diff white blood cell differential
  • a conventional CBC+Diff test is done in an "invasive" manner in which a sample of venous blood is drawn from apatient through a needle, and submitted to a laboratory for analysis.
  • a phlebotomist an individual specially trained in drawing blood collects a sample of venous blood into a tube containing an anticoagulant to prevent the blood from clotting.
  • the sample is then sent to a hematology laboratory to be processed, typically on automated, multiparameter analytical instruments, such as those manufactured by Coulter Diagnostics of Miami, Florida.
  • the CBC+Diff test results are returned to the requesting physician, typically on the next day.
  • In medical diagnosis it is often necessary to measure other types of blood components, such as non-cellular constituents present in the plasma component of blood.
  • Such constituents can include, for example, blood gases and bilirubin.
  • Bilirubin is a reddish to yellow pigment produced in the metabolic breakdown of hemoglobin and other proteins. Bilirubin is removed from the blood by the liver and is excreted from the body. However, the livers of newborn children, especially premature babies, cannot process bilirubin effectively.
  • bilirubin may accumulate in the blood stream. If bilirubin levels rise high enough, it begins to be deposited in other body tissues causing jaundice.
  • the most common method for bilirubin analysis is through an in vitro process.
  • a blood sample is invasively drawn from the patient.
  • the formed elements red blood cells and other cells
  • the remaining fluid is reacted chemically and analyzed spectrophotometrically.
  • Blood bank use for transfusion of infants in neonatal intensive care units is second only to the usage for cardiothoracic surgery.
  • invasive techniques are also particularly stressful for, and/or difficult to carry out on, children, elderly patients, burn patients, and patients in special care units.
  • a hierarchical relationship exists between the laboratory findings and those obtained at the physical examination.
  • the demarcation between the physical findings of the patient and the laboratory findings are, in general, the result of technical limitations. For instance, in the diagnosis of anemia (defined as low hemoglobin concentration), it is frequently necessary to quantify the hemoglobin concentration or the hematocrit in order to verify the observation of pallor. Pallor is the lack of the pink color of skin which frequently signals the absence or reduced concentration of the heavily red pigmented hemoglobin.
  • a device which is able to rapidly and non-invasively quantitatively determine the hemoglobin concentration directly from an examination of one or more of the foregoing areas would eliminate the need to draw a venous blood sample to ascertain anemia. Such a device would also eliminate the delay in waiting for the laboratory results in the evaluation of the patient. Such a device also has the advantage of added patient comfort.
  • Soft tissue such as mucosal membranes or unpigmented skin, do not absorb light in the visible and near-infrared, t.e., they do not absorb light in the spectral region where hemoglobin absorbs light. This allows the vascularization to be differentiated by spectral absorption from surrounding soft tissue background.
  • the surface of soft tissue strongly reflects light and the soft tissue itself effectively scatters light after penetration of only 100 microns. Therefore, in vivo visualization of the circulation is difficult because of poor resolution, and generally impractical because of the complexities involved in compensating for multiple scattering and for specular reflection from the surface.
  • Spectrophotometry involves analysis based on the absorption or attenuation of electromagnetic radiation by matter at one or more wavelengths of light.
  • the instruments used in this analysis are referred to as spectrophotometers.
  • a simple spectrophotometer includes: a source of radiation, such as, e.g., a light bulb; a spectral selection means, such as a monochromator containing a prism or grating or colored filter; and one or more detectors, such as, e.g., photocells, which measure the amount of light transmitted and/or reflected by the sample in the selected spectral region.
  • the radiation reflected from the surface of the sample maybe measured and compared with the radiation reflected from a non-absorbing or white sample. If this reflectance intensity is plotted as a function of wavelength, it gives a reflectance spectrum.
  • Reflectance spectra are commonly used in matching colors of dyed fabrics or painted surfaces.
  • reflection spectrophotometry has been used primarily in qualitative rather than quantitative analysis.
  • transmission spectrophotometry is conventionally used for quantitative analysis because Beer's law (inversely relating the logarithm of measured intensity linearly to concentration) can be used.
  • Reflective spectrophotometry is conventionally avoided for quantitative analysis because specularly reflected light from a surface limits the available contrast (black to white or signal to noise ratio), and, consequently, the measurement range and linearity. Because of surface effects, measurements are usually made at an angle to the surface. However, only for the special case of a
  • Lambertian surface will the reflected intensity be independent of the angle of viewing. Light reflected from a Lambertian surface appears equally bright in all directions (cosine law). However, good Lambertian surfaces are difficult to obtain. Conventional reflection spectrophotometry presents an even more complicated relationship between reflected light intensity and concentration than exists for transmission spectrophotometry which follows Beer's law. Under the Kubelka-Munk theory applicable in reflection spectrophotometry, the intensity of reflected light can be related indirectly to concentration through the ratio of absorption to scattering. Some imaging studies have been done in the reflected light of the microcirculation of the nail beds on patients with Raynauds, diabetes, and sickle cell disease.
  • the Winkelman device uses image analysis and reflectance spectrophotometry to measure individual cell parameters such as cell size. Measurements are taken only within small vessels, such as capillaries where individual cells can be visualized. Because the Winkelman device takes measurements only in capillaries, measurements made by the Winkelman device will not accurately reflect measurements for larger vessels. This inaccuracy results from the constantly changing relationship of volume of cells to volume of blood in small capillaries resulting from the non-Newtonian viscosity characteristic of blood. Consequently, the Winkelman device is not capable of measuring the central or true hematocrit, or the total hemoglobin concentration, which depend upon the ratio of the volume of red blood cells to that of the whole blood in a large vessel such as a vein.
  • the Winkelman device measures the number of white blood cells relative to the number of red blood cells by counting individual cells as they flow through a micro-capillary.
  • the Winkelman device depends upon accumulating a statistically reliable number of white blood cells in order to estimate the concentration. However, blood flowing through a micro-capillary will contain approximately 1000 red cells for every white cell, making this an impractical method.
  • the Winkelman device does not provide any means by which platelets can be visualized and counted. Further, the Winkelman device does not provide any means by which the capillary plasma can be visualized, or the constituents of the capillary plasma quantified.
  • the Winkelman device also does not provide a means by which abnormal constituents of blood, such as tumor cells, can be detected.
  • the '120 patent Reflected Imaging Analysis
  • the '939 patent is incorporated herein by reference as though set forth in its entirety.
  • the device of the '120 patent or the '939 patent provides for complete non-invasive in vivo analysis of a vascular system.
  • This device provides for high resolution visualization of blood cell components (red blood cells, white blood cells, and platelets), blood rheology, blood vessels, and vascularization throughout the vascular system.
  • the device of the ' 120 patent or the '939 patent allows quantitative determinations to be made for blood cells, normal and abnormal contents of blood cells, as well as for normal and abnormal constituents of blood plasma.
  • the device of the '120 patent or the '939 patent captures a raw reflected image of a blood sample, and normalizes the image with respect to the background to form a corrected reflected image.
  • An analysis image is segmented from the corrected reflected image to include a scene of interest for analysis.
  • the method and apparatus disclosed in the '120 patent or the '939 patent employs Beer' s law to determine such characteristics as the hemoglobin concentration per unit volume of blood, the number of white blood cells per unit volume of blood, a mean cell volume, the number of platelets per unit volume of blood, and the hematocrit.
  • the images need to be screened to identify images having good measurable properties.
  • the measurements taken from the images also need to be screened, normalized and corrected to obtain better estimates of the true value of the blood characteristics.
  • the present invention is directed to screening reflected spectral images of a microcirculatory system to determine whether the spectral image is suitable for hemoglobin measurement and prediction.
  • the method and apparatus of the present invention analyze the spectral image (also referred to herein as "blood sample” or "image”) to identify images having good measurable properties.
  • multi-stage screening is performed on each input image to determine whether the image is suitable for hemoglobin measurement and prediction.
  • the following quantities are measured: image intensity, image background intensity variation, image focus condition, image motion blur condition, number of vessels in the image with certain range of diameters, average vessel edge contrast, number of bubble segments in the image and the similarity between two images.
  • the multi-stage screening cycle includes intensity screening, discrete wavelet transform (DWT) decomposition, focus measurement, motion blur measurement, vessel and bubble detection, and repetition detection.
  • DWT discrete wavelet transform
  • the image is classified into one of three categories: excellent, mediocre and poor. Only the images having an excellent rating is used for further Hb estimation. But both excellent and mediocre images would launch an autofocusing process provided by focus measurement.
  • a Fast Fourier transform is used to develop a spectral ratio to detect excellent images.
  • the Fast Fourier transform is used to compute a power spectrum from the image.
  • the spectral ratio is derived by computing a ratio from the slopes of the power spectrum at low and intermediate spatial frequencies. If the spectral ratio falls within a predetermined range, the image is judged as having good measurable properties.
  • the present invention provides real time hemoglobin prediction on the subject under investigation.
  • the method is used to perform in vivo analysis of blood in large vessels, and in vivo analysis of blood in small vessels to determine blood parameters such as concentrations and blood cell counts .
  • the method of the present invention can also be used to conduct non-invasive in vivo analysis of non-cellular characteristics of capillary plasma.
  • the method of the present invention can also be used to perform in vitro analyses by imaging blood in, for example, a tube or flow cell.
  • the method of the present invention can also be used to analyze other types of fluids containing visualizable components.
  • the reflected spectral imaging system can be used to analyze fluids for particulate impurities. It is only necessary that the walls of the fluid path be sufficiently transparent to permit-light to pass through the walls of the fluid path to image the fluid and any impurities flowing in the path.
  • FIG. 1 shows a flow chart representing the general operational flow for screening spectral images according to an embodiment of the present invention
  • FIG. 2 shows a flow chart illustrating step 104 shown in FIG. 1
  • FIG. 3 shows a flow chart illustrating step 110 shown in FIG. 1;
  • FIG. 4 shows a flow chart illustrating step 112 shown in FIG. 1;
  • FIG. 5 shows a flow chart illustrating step 114 shown in FIG. 1 ;
  • FIG. 6 shows a flow chart illustrating step 116 shown in FIG. 1;
  • FIG. 7 shows a flow chart illustrating step 118 shown in FIG. 1
  • FIG. 8 shows a flow chart illustrating step 120 shown in FIG. 1;
  • FIG. 9 shows a flow chart illustrating step 122 shown in FIG. 1;
  • FIG. 10 shows a flow chart illustrating step 124 shown in FIG. 1
  • FIG.11 shows a flow chart illustrating steps 1026 and steps 1028 shown in FIG. 10;
  • FIG. 12 shows a flow chart illustrating step 1128 shown in FIG. 11 ;
  • FIG. 13 shows a flow chart illustrating step 126 shown in FIG. 1;
  • FIG. 14 shows a flow chart representing the general operational flow for screening spectral images according to another embodiment of the present invention.
  • FIG. 15 is a block diagram of an example computer system useful for implementing the present invention.
  • the present invention is directed to a method and apparatus for analysis, particularly non-invasive, in vivo analysis of a subject's vascular system.
  • the in vivo measurements discussed herein can also be performed in vitro by imaging blood in, for example, a tube or flow cell, as would be apparent to a person skilled in the relevant art(s).
  • the in vivo method is carried out by imaging a portion of the subject's vascular system.
  • the image can be created from a sub-surface region of a subject's tissues or organs.
  • the tissue covering the imaged portion must be traversed by light without multiple scattering to obtain a reflected image.
  • two criteria must be met.
  • image contrast resulting from a difference in the optical properties, such as absorption, index of refraction, or scattering characteristics, between the subject to be imaged and its surroundings or background.
  • the light that is collected from the subject must reach an image capturing means without substantial scattering, i. e., the reflected image must be captured from a depth that is less than the multiple scattering length.
  • image refers to any image that satisfies the foregoing two criteria.
  • reflected image refers to the image of a subject in reflected light. The resolution required for capturing the image is dictated by the spatial homogeneity of the imaged portion. For example, a reflected image of individual cells requires high resolution.
  • a reflected image of large vessels can be done with low resolution.
  • a reflected image suitable for making a determination based on pallor requires very low resolution.
  • the tissue covering the imaged portion is thus preferably transparent to light, and relatively thin, such as the mucosal membrane on the inside of the lip of a human subject.
  • light refers generally to electromagnetic radiation of any wavelength, including the infrared, visible, and ultraviolet portions of the spectrum.
  • a particularly preferred portion of the spectrum is that portion where there is relative transparency of tissue, such as in the visible and near-infrared wavelengths. It is to be understood that for the present invention, light can be coherent light or incoherent light, and illumination may be steady or in pulses of light.
  • the reflected image is corrected to form a corrected reflected image.
  • the correction to the reflected image is done, for example, to isolate particular wavelengths of interest, or to extract a moving portion of the image from a stationary portion of the image.
  • a scene is segmented from the corrected reflected image to form an analysis image.
  • the analysis image is then analyzed for the desired characteristic of the subject's vascular system.
  • the method of the present invention can be used for analysis in large and small vessels, including capillary plasma.
  • "large vessel” refers to a vessel in the vascular system of sufficient size so that a plurality of red blood cells flow side-by-side through it.
  • “Small vessel” refers to a vessel in the vascular system of a size so that red blood cells flow substantially “single file” through it.
  • the present invention uses reflectance, not transmission, for the images that are analyzed. That is, the image is made by "looking at” the vascular system, rather than by "looking through” the vascular system.
  • the imaging system used in the present invention as described in detail in the above-referenced '120 patent or the '939 patent, the image appears to be of the transmission type. For this reason, Beer's law can be applied to quantitatively measure the images. Per unit volume or concentration measurements can be made directly from the images. Therefore, although the present invention uses reflectance, it would be apparent to a person skilled in the relevant art(s) that the method of the present invention can be used on both transmitted and reflected images.
  • the hemoglobin (Hb), hematocrit (Hct), and white blood cell count (WBC) parameters can be directly determined.
  • mean cell volume (MC V), mean cell hemoglobin concentration (MCHC), and platelet count (Pit) can be directly determined.
  • red blood cells erythrocytes
  • white blood cells leukocytes
  • platelets platelets.
  • red blood cells contain hemoglobin that carries oxygen from the lungs to the tissues of the body.
  • White blood cells are of approximately the same size as red blood cells, but do not contain hemoglobin.
  • a normal healthy individual will have approximately 5,000,000 red blood cells per cubic millimeter of blood, and approximately 7,500 white blood cells per cubic millimeter of blood. Therefore, a normal healthy individual will have approximately one white blood cell for every 670 red blood cells circulating in the vascular system.
  • the first six parameters are referred to herein as RBC parameters.
  • Concentration measurements (measurements per unit volume of blood) are necessary for producing values for Hb, Hct, RBC, WBC, and Pit.
  • Hb is the hemoglobin concentration per unit volume of blood.
  • Hct is the volume of cells per unit volume of blood. Hct can be expressed as a percentage, i.e.,:
  • RBC is the number of red blood cells per unit volume of blood.
  • WBC is the number of white blood cells per unit volume of blood.
  • Pit is the number of platelets per unit volume of blood.
  • Red cell indices are cellular parameters that depict the volume, hemoglobin content, and hemoglobin concentration, respectively, of the average red cell.
  • the red cell indices may be determined by making measurements on individual cells, and averaging the individual cell measurements. Red cells do not change volume or lose hemoglobin as they move through the vascular system. Therefore, red cell indices are constant throughout the circulation, and can be reliably measured in small vessels.
  • the three red cell indices are related by the equation:
  • values for the six RBC parameters listed above the following two criteria must be met. First, three of the parameters must be independently measured or determined. That is, three of the parameters must be measured or determined without reference to any of the other of the six parameters. Second, at least one of the three independently measured or determined parameters must be a concentration parameter (per unit volume of blood). Therefore, values forthe six key parameters canbe determined by making three independent measurements, at least one of which is a concentration measurement which cannot be made in a small vessel. As disclosed in the '120 patent or the '939 patent, Hb and Hct can be directly measured by reflected spectral imaging of large vessels, and MCV can be directly measured by reflected spectral imaging of small vessels. In this manner, three parameters are independently measured, and two of the parameters (Hb and Hct) are concentration parameters measured per unit volume of blood. As such, the six RBC parameters listed above can be determined in the following manner:
  • Hb can be directly measured by reflected spectral imaging of large vessels
  • MCN and MCHC can be directly measured by reflected spectral imaging of small vessels.
  • three parameters are independently measured, and one of the parameters (Hb) is a concentration parameter measured per unit volume of blood.
  • the six RBC parameters listed above can be determined in the following manner:
  • MCHC Directly measured Concentration measurements are measurements per unit volume. As discussed above, a measurement made per unit area is proportional to a measurement made per unit volume (volume measurement with constant depth) when the depth of penetration is constant. The depth of penetration is a function of wavelength, the size of the particles with which it interacts, and refractive index. For blood, the particle size and index of refraction are essentially constant. Consequently, the depth of penetration will be constant for a particular wavelength.
  • Hemoglobin is the main component of red blood cells. Hemoglobin is a protein that serves as a vehicle for the transportation of oxygen and carbon dioxide throughout the vascular system. Hemoglobin absorbs light at particular absorbing wavelengths, such as 550 nm, and does not absorb light at other non- absorbing wavelengths, such as 650 nm. Under Beer's law, the negative logarithm of the measured transmitted light intensity is linearly related to concentration. As explained more fully in the '120 patent or the '939 patent, a spectral imaging apparatus can be configured so that reflected light intensity follows Beer's law.
  • the concentration of hemoglobin in a particular sample of blood is linearly related to the negative logarithm of light reflected by the hemoglobin.
  • the more 550 nm light absorbed by a blood sample the lower the reflected light intensity at 550 nm, and the higher the concentration of hemoglobin in that blood sample.
  • the concentration of hemoglobin can be computed by taking the negative logarithm of the measured reflected light intensity at an absorbing wavelength such as 550 nm. Therefore, if the reflected light intensity from a particular sample of blood is measured, the concentration in the blood of such components as hemoglobin can be directly determined.
  • the method of the present invention can also be used to determine the hematocrit (Hct).
  • Hct hematocrit
  • the difference between hemoglobin (which is the grams of hemoglobin per volume of blood) and hematocrit (which is the volume of blood cells per volume of blood) is determined by the concentration of hemoglobin within the cells which determines the index of refraction of the cells.
  • measurements in which the image contrast between the circulation and the background is achieved principally by the scattering properties of the circulation will be related to the hematocrit and those obtained principally by the absorbing properties will be related primarily to the hemoglobin.
  • the microvascular system beneath the mucosal membrane on the inside of the lip of a human subject can be imaged to produce a raw reflected image whose contrast is determined by a difference in the scattering properties of the blood cells.
  • a spectral imaging apparatus includes a light source that is used to illuminate the portion of the subject's vascular system to be imaged.
  • the reflected light is captured by an image capturing means.
  • Suitable image capturing means include, but are not limited to, a camera, a film medium, a photocell, a photodiode, or a charge coupled device camera.
  • An image correcting and analyzing means such as a computer, is coupled to the image capturing means for carrying out image correction, image screening, scene segmentation, and blood characteristic analysis.
  • FIG. 1 illustrates a general operational flow of an embodiment of the present invention. More specifically, flowchart 100 shows an example of a process for screening a spectral image of a blood or tissue sample. Still more particularly, flowchart 100 describes a process for collecting information about the spectral image to better judge the quality of the image and perform vessel measurements to determine its characteristics.
  • FIG 1 is described in referenced to predicting hemoglobin, other blood characteristics can also be measured from the images selected by the present method.
  • the spectral image can be obtained from a spectral imaging apparatus preferably, but not necessarily, ofthe type described in the '120 patent or the '939 patent. Nonetheless, the spectral image can be obtained from any type of imaging apparatus designed for tissue or blood analysis, as would be apparent to a person skilled in the relevant art(s).
  • FIG. 1 starts at step 101 and passes immediately to step 102, where spectral images are retrieved from a memory source or image directory.
  • the images can be retrieved from an input file stored in a temporary or permanent memory location on a hard disk drive or removable storage device, such as a floppy diskette, magnetic tape, optical disks, or the like.
  • the input file also includes the subject number or other data used to identify the subject or patient.
  • an output file is also created to store relevant test results, as described below in further detail.
  • screening parameters are downloaded for use during the subsequent screening steps.
  • the screening parameters include the width and height for an original image.
  • 8-bit images are used for the screening process according to the present invention. Using 8-bit images maximizes performance requirements while maintaining accuracy. Higher dynamic range images can be used but may degrade the processing speed.
  • Other screening parameters include the preset size for cropped images and position within the original image; Discrete Wavelet Transform (DWT) decomposition level; mask size for use during repetit on detection (as described below); search range for use during repetition detection; number of sampled rows and columns for screening and the sampling positions (as described below); and various threshold values.
  • the threshold values include SCREEN_IM_MIN,
  • the threshold values SCREEN_IM_MIN and SCREEN M_MAX setthe minimum and maximum intensity levels, respectively. If an image's mean intensity exceeds this range, the image would not pass the screening steps as discussed below.
  • Another threshold value is SCREEN_IN_MAX, which determines the maximum allowed background intensity variation value. If an image has a background intensity variation larger than this value, the image would not pass screening.
  • the SCREE ⁇ _MB_THRESH threshold value sets the motion blur threshold. Images with motion blur measurement larger than this value would not pass screening.
  • the threshold values SCREEN_VE_MIN_D and SCREEN_NE_MAX_D represent the minimum and maximum vessel diameters, respectively. If a vessel has a diameter beyond this range, the vessel measurements would not be counted.
  • the threshold value SCREE ⁇ _V ⁇ _THRESH is the number of vessel segment with an appropriate size, meaning that images having a number of vessel segments less than this value would not pass the screening.
  • the threshold value SCREEN_BN_THRESH sets the number of bubble segments. The images having a number of bubble segments larger than this value would not pass screening.
  • SCREEN_RP_THRESH is the repetition threshold. Two images having a repetition parameter larger than this value would be considered from the same site.
  • SCREEN_FO_THRESH is the minimum vessel edge contrast threshold. The edge contrast threshold is set adaptively. However during the initial screening cycle, this value is set as the threshold because historical values would not be in existence.
  • the screening process is initialized for the subject and the subject's images are selected.
  • the first image if any, is selected and at step 108, the image is loaded for screening.
  • the screening process is initialized for the loaded image.
  • the image is subsampled for real time screening. Subsampling increases the processing speed by uniformly extracting a predetermined number of rows and columns from the image, hi an embodiment, sixteen rows and columns are subsampled from the image.
  • the present invention can be practiced with any number up to the total number of pixel rows and columns in the image. All screening information is obtained from this data.
  • Step 114 is the first screening step.
  • the subsampled images are evaluated to determine the mean image intensity value and intensity variation value.
  • Statistical information (such as, minimum, maximum and mean values) is computed for the rows and columns within each subsampled image.
  • the mean intensity of all the samples approximates the mean intensity of the whole image.
  • the standard deviation among the mean intensities of the sampled rows and columns is used to approximate the intensity variation of the image.
  • These calculations are used to determine whether the intensity of the image under inspection is within a linearity range of the illumination and whether no intensity background shadowing is present. If the images are saturated, Beer's law may not hold. Thus, too bright or too dark images are not considered as good images.
  • These calculations are also used to ensure that no big background intensity variation is present in the selected images. In most cases, the shadow from deeper vessels would cause significant intensity variation which would influence the hemoglobin prediction. Thus, images with large background intensity variation should not be selected.
  • a binary vessel mask is generated for the current image.
  • This mask would be used for repetition detection (discussed in detail below) to determine whether the current image is from the same site as a controlled image.
  • the controlled image typically, is the previously processed image.
  • the controlled image can be determined from an initialization parameter entered at step 102.
  • two image masks (row mask and column mask) are generated. When generating these masks, the cropped image is divided into 3 x 3 blocks. Each block would have its own threshold for background and vessel segmentation. Using two image masks reduces the influence of bowl-shape background illumination pattern.
  • DWT discrete wavelet transform
  • focus measurement is computed from the high frequency components of the images in an appropriate scale.
  • the focus measurement is used to compare the focus conditions of two images from the same scene.
  • the focus measurement is used to exclude those images which are out of focus.
  • This automatic focusing routine is implemented by analyzing the average vessel edge contrast of each image. If the average edge contrast of an image is much lower than the controlled image (or, alternatively, the running average of previous selected images), the current image would be discarded even if it had passed other screening criteria.
  • a motion blur measurement is computed for the current image.
  • the measurement is compared against the motion blur threshold entered at step 102. Images having motion blur measurement that exceeds this threshold would not be used for Hb measurements .
  • Motion blur usually reduces the contrast of the vessels and also causes inaccurate vessel diameters measurement.
  • vessel and bubble detection is performed. If bubbles are detected in the image or if no good sized vessels can be located, the image would not be used for further processing.
  • the bubbles typically, caused by liquids, such as water or saliva
  • the bubbles would usually produce intensity distortion on the vessels.
  • vessel detection the diameter of each vessel segment is measured for each detected vessel. Only the vessels whose diameter is within a specified range would be counted as good-sized vessels.
  • the focus measurement and vessel masks are analyzed for repetition detection to determine if the current image and controlled image are from the same site.
  • Repetition detection ensure that only one image is kept for each distinct site. Therefore, repetition detection ensures that Hb measurements are based on each site, not each image. It is presumed that each distinct site has the same contribution towards the final hemoglobin prediction. If the currently selected image has been detected to be similar enough with the controlled image (typically, the previously selected image), only the one with higher focus measurement would be saved for further measurement.
  • Step 126 is the final screening step.
  • an overall screening summary is prepared and/or updated for the current subject.
  • the summary also includes a running average of all information for the current subject, and site information for the current subject.
  • adaptive thresholds are also generated for further screening.
  • distinct sites are determined from the screening information. For instance, a distinct site would exist if there was a big motion, a sequence of bad images (i.e., images not passing the screening steps 114-126) or the size of the current site exceeds a certain threshold.
  • the screening information or statistics are reported to an operator.
  • the information can be displayed on a display unit, presented in a paper report, or the like.
  • the screening information allows the operator to determine if the spectral imaging apparatus, or its light source or probe, is being moved in an appropriate way.
  • the screening results of the last fifty images are evaluated. If too much repetition is determined at step 126, this is an indication that the probe is moving too slow. If too much motion blur is detected at step 122, the probe is moving too fast. If too many images fail bubble screening at step 124, there are too many bubbles in the scene, which must be eliminated.
  • the screening information is stored in the output file, and at step 106 the next image is selected to repeat the screening and processing steps
  • step 134 the images passing the screening thresholds are selected for each site. In an embodiment, only one image is selected from each site for further processing. The selected images should be distortion free and contain good-sized vessels for hemoglobin prediction.
  • OD/D is computed for each image at step 128. In another embodiment, OD/D is computed only for the selected images at step 134.
  • FIG. 2 illustrates an operational flow of an embodiment for screening initialization for a current subject. More specifically, FIG.2 is an embodiment of a more detailed description of process step 104 from FIG. 1.
  • the screening initialization process is started by allocating memory space for processing.
  • all global variables are set or reset to zero, depending on whether it is the initial or subsequent screening cycle.
  • the global variables are the screening parameters determined at screening steps 114-126 and are used to evaluate images for selection.
  • a lookup table is generated to increase the ability to quickly process the masks generated during the screening steps.
  • a lookup table is generated for the number of ones from 1 to 255 bitwise.
  • a histogram file is opened for plotting Hb measurements at step 136. Then the control flow passes to step 106, as previously described.
  • FIG. 3 illustrates an operational flow of an embodiment for screening initialization for a currently loaded image. More specifically, FIG. 3 is an embodiment of a more detailed description of process step 110 from FIG. 1.
  • buffers are setup to analyze the current image.
  • the global variables are set or reset to zero, depending on whether the current image is the first image to be processed for the current subject.
  • the image masks are also set or reset to zero. Then the control flow passes to step 112, as previously described.
  • FIG.4 illustrates an operational flow of an embodiment for subsampling a current image. More specifically, FIG. 4 is an embodiment of a more detailed description of process step 112 from FIG. 1.
  • the current image and the input parameters are received. The input parameters include the subsample step and position, and the number of subsampled horizontal and vertical lines.
  • FIG. 5 illustrates an operational flow of an embodiment for intensity screening a current image. More specifically, FIG.5 is an embodiment of a more detailed description of process step 114 from FIG. 1.
  • each subsampled row is scanned to compute a maximum, minimum and sum of the intensity.
  • each subsampled column is scanned to compute a maximum, minimum and sum of the intensity.
  • a mean intensity for the whole image is calculated from all subsampled mean intensities.
  • a row variance and/or standard deviation for the whole image is calculated from the mean intensities of the subsampled rows.
  • a column variance and/or standard deviation for the whole image is calculated from the mean intensities of the subsampled columns.
  • mean intensity, row variance (or standard deviation) and column variance (or standard deviation) are compared to the minimum and maximum intensity threshold values (SCREEN_IM_MIN and SCREEN_ ⁇ M_MAX) entered at step 102. If the calculated values fall within the range set by the threshold values, a mean or variance flag is set to "1" at step 528 or step 534, as appropriate. If the calculated values fall outside of the range, the mean or variance flag is set to "0" at step 530 or step 536, as appropriate.
  • the mean image intensity value, intensity variation value, and intensity screening status (i.e., mean or variance) flag(s) are outputted, and the control flow passes to step 116, as previously described.
  • FIG. 6 illustrates an operational flow of an embodiment for generating a binary vessel mask for a current image. More specifically, FIG. 6 is an embodiment of a more detailed description of process step 116 from FIG. 1.
  • the current image and the screening parameter for the size of cropped image and its position are received.
  • the mask of a controlled image is also received.
  • the controlled image can be the previously screened image, or it can be a screening parameter inputted at step 102.
  • the cropped image is divided into nine blocks (i.e., three-by-three). The size of each block is l60 by l28.
  • the mean intensity is computed for each image block.
  • a vessel segmentation threshold is established for each block based on the mean
  • each block is subsampled into an eight-by-eight binary mask.
  • the value for each mask pixel is determined by the segmentation results. The value would be set to "1" if the pixel corresponds to a vessel and "0" if otherwise.
  • a row mask and column mask is generated simultaneously. The column mask is determined as the transpose of the row mask.
  • the process is repeated for all nine image blocks.
  • the mask for the current image is compare to the mask for the controlled image. In an embodiment, only the column masks are used.
  • FIG. 7 illustrates an operational flow of an embodiment for performing a DWT transform on a current image.
  • FIG.7 is an embodiment of a more detailed description of process step 118 from FIG. 1.
  • the following global variables are received for processing: the subsampled rows and columns and their lengths (from step 112), and the DWT decomposition level (from step 102).
  • a one-dimensional circular DWT is computed on each of the sixteen subsampled rows, with each row being 512 pixels in length.
  • a one-dimensional circular DWT is computed on each of the sixteen subsampled columns, with each column being 400 pixels in length.
  • the output of the DWT computations is a coefficient vector with the same length as the inputted data.
  • the DWT coefficients have a multi-scale structure.
  • FIG. 8 illustrates an operational flow of an embodiment for determining the focus measurement from a current image. More specifically, FIG. 8 is an embodiment of a more detailed description of process step 120 from FIG. 1.
  • the following global variables are received: the DWT coefficients for the subsampled rows and columns and their lengths (from step 118), mean intensity of each sampled row and column (from step 114), and DWT decomposition level (from step 102).
  • the high frequency coefficients at the third decomposition level is used as the focus measurement.
  • the measurement is normalized by using the average mean intensity for the current image.
  • the DWT coefficients at the third level reflect the edge sharpness of larger vessels, at the same time, they are less sensitive to the noise than the coefficients at finer scales, i.e. "1" and "2.”
  • FIG. 9 illustrates an operational flow of an embodiment for measuring motion blur for a current image. More specifically, FIG. 9 is an embodiment of a more detailed description of process step 122 from FIG. 1.
  • the global parameters are received, namely the DWT coefficients for the subsampled rows and columns and their lengths (from step 118), and the DWT decomposition level (from step 102).
  • a calculation is made of the sum of the magnitude of the DWT coefficients of a subsampled column at scale 1 and scale 2, respectively. The calculated values are denoted as Msl and Ms2.
  • a ratio of Msl to Ms2 is computed for high frequency components in the first and second scale for the current column.
  • the process defined in steps 924-926 is repeated for all sixteen subsampled columns.
  • the maximum ratio is determined and compared to the motion blur threshold value from step 102. If the maximum ratio is less than the threshold value, at step 932, a motion flag is set to "1" to designate the image has passed. Otherwise, at step 934, the motion flag is set to "0" to designate the image has failed the motion blur screening.
  • This embodiment for motion blur screening is premised on the assumption that motion generally produces a shift between two fields in an image.
  • the shift would generate a large amount of high frequency components for the subsampled columns, and the high frequency components would disappear in the coarser scale because of the subsampling. Therefore, the ratio of Msl to Ms2 is a good indicator of motion blur.
  • the motion blur measurements and motion blur screening status flag are outputted as global variables, and the control flow passes to step 124, as previously described.
  • FIG. 10 illustrates an operational flow of an embodiment for performing vessel and bubble detection on a current image. More specifically, FIG. 10 is an embodiment of a more detailed description of process step 124 from FIG. 1.
  • the global parameters are received for processing. The parameters include the DWT coefficients of subsampled rows and columns, the DWT decomposition level, the original input images and the sizes of the input data.
  • horizontal edge scanning is performed to detect vessel and bubble segments in the image.
  • vertical edge scanning is performed to detect vessel and bubble segments in the image.
  • the detected vessels are analyzed to determine if the vessel size is sufficient.
  • a vessel flag is set to "1" if a predetermined number of good-sized vessels are detected.
  • the vessel flag is set to "0.”
  • the bubble segments if any, are evaluated.
  • a bubble flag is set to "1" if the number of detected bubbles exceed a preset threshold. Otherwise, at step 1040, the bubble flag is set to "0.”
  • the output data for the subsequent steps include the number of vessel segments, number of bubble segments, vessel segment information, average edge contrast and the vessel and bubble screening status flags. Afterwards, the control flow passes to step 126, as previously described.
  • FIG. 11 illustrates an operational flow of an embodiment for the edge scanning process for a current subject. More specifically, FIG. 11 is an embodiment of a more detailed description of step 1026 and step 1028 from FIG. 10.
  • the appropriate global parameters are obtained at step 1126.
  • step 1128 subsampled rows (for step 1026) or columns (for step 1028) are loaded for processing.
  • edge detection is performed on each individual subsampled row or column, as appropriate, to obtain edge information, including edge position, direction and intensity or contrast.
  • the edge information is recorded.
  • the edge information is used to detect bubble segments in the image.
  • the edge information is used to detect vessel segments in the image. Then the control flow passes to step 1028 or step 1030, as appropriate, as previously described.
  • FIG. 12 illustrates an operational flow of an embodiment for performing edge detection on a current image. More specifically, FIG. 12 is an embodiment of a more detailed description of process step 1130 from FIG. 11.
  • the data is received from the loaded DWT rows or columns (from step 1128).
  • Edge detection is based on gradient information and uses the low frequency band of the DWT coefficients.
  • the absolute values are calculated for the differences between all adj acent data in the loaded DWT rows or columns.
  • the mean value of all differences are calculated.
  • a threshold value is derived from the mean value. In an embodiment, the threshold value is calculated as the product of 1.75 and the mean value.
  • the difference data is checked from left to right to determine the edge location, direction and edge strength.
  • the difference data is compared to its neighbor. If the difference data exceeds the mean value and its neighbor's mean value, the control flow passes to step 1242.
  • the edge location is determined, a mark is set at this position, and the edge direction is recorded. The control flow then passes to step 1244. If, on the other hand, the difference data does not exceed the mean value and it neighbor's mean value, the control flow passes to step 1244.
  • FIG. 13 illustrates an operational flow of an embodiment for measuring repetition detection for a current image. More specifically, FIG. 13 is an embodiment of a more detailed description of process step 126 from FIG. 1.
  • the parameters include the vessel masks (row and column masks) of the current and controlled image, the matching range which is the searching range for maximum correlation between the two masks, the focus measurements of the two images, and the repetition threshold.
  • the matching range is evaluated to determine if repetition detection has been implemented over the specified range.
  • the row and column masks for the current and controlled images are analyzed to detect similarities.
  • the current similarity measurement is compared to the previous similarity measurement, and the larger value is recorded at step 1334. For the initial pass or cycle, the current similarity value would be recorded.
  • the similarity measurement is compared to the repetition threshold. If the repetition threshold is exceeded, at step 1340, a repetition flag is set to "0" to designate the image as failed because it is located at the same site as the controlled image. Otherwise, at step 1338, the repetition flag is set to "1" to designate the image represents a new and distinct site.
  • the maximum similarity measurement and repetition screening flag is outputted, and the control flow passes to step 128, as previously described.
  • FIG.14 is a general operational flow of another embodiment for screening spectral images.
  • This embodiment utilizes a Fourier spectrum to select a subset of images that contain vascular structure.
  • the control flow starts at step 108, where an image is loaded as described in reference to FIG. 1.
  • the current image is cropped and subsampled according to a predetermined parameter, and the image size set at step 1412 as 220 by 220 pixels.
  • the current image is resized to 256 by 256 pixels according to the image size set in step 1416. The resized image allows a Fast Fourier transform to be used to significantly reduce the time to calculate the Fourier spectrum.
  • the Fast Fourier transform is applied and the power spectrum is computed at step 1420.
  • slopes of the Fourier spectrum at low and intermediate spatial frequencies are determined.
  • the slope at low frequencies is determined by fitting a line over the region from the peak to where the spectrum has a value midway between its maximum and minimum values.
  • the slope at intermediate frequencies is determined by fitting a line over the region between the spatial frequencies of 23 and 46 (pixel/2) "1 .
  • the lower frequency region corresponds to spatial scales above fifty microns
  • the intermediate frequency region corresponds to spatial scales between nineteen and thirty-eight microns.
  • a single parameter is derived from the Fourier spectrum.
  • the parameter is the spectral ratio, S, which is the ratio of the slopes of the
  • the spectral ratio parameter is sensitive to the average size of vessels as well as the number of vessels in an image.
  • S is evaluated to determine if it falls within a predetermined range. If the spectral ratio S lies within the interval between five and ten (i.e., 5 ⁇ S ⁇ 10), the image is considered to contain measurable vessels. Thus, at step 1428, the image is designated as passing the screening process.
  • Images with S ⁇ 5 generally contain predominately small vessels (e.g., diameters less than twenty microns) or few vessels. Images with S > 10 generally contain large vessels (e.g., diameters greater than 100 microns) or highly uneven backgrounds with no discernable vessels.
  • the present invention was developed primarily to analyze blood components in a non-invasive manner.
  • the invention has application outside the medical area and can be used generally to analyze visualizable components in a fluid flowing in any vascular system, such as a tube, the walls of which are transparent to transmitted and reflected light.
  • the present invention can be implemented using hardware, software or a combination thereof and can be implemented in one or more computer systems or other processing systems. In fact, in one embodiment, the invention is directed toward one or more computer systems capable of carrying out the functionality described herein.
  • the present invention can be programmed in C, C++ and the like. In the preferred embodiment, C programming language is used.
  • Computer system 1500 includes one or more processors, such as processor 1504.
  • the processor 1504 is connected to a communication infrastructure 1506 (e.g., a communications bus, cross-over bar, or network).
  • a communication infrastructure 1506 e.g., a communications bus, cross-over bar, or network.
  • Computer system 1500 can include a display interface 1502 that forwards graphics, text, and other data from the communication infrastructure 1506 (or from a frame buffer not shown) for display on the display unit 1530.
  • Computer system 1500 also includes a main memory 1508, preferably random access memory (RAM), and can also include a secondary memory 1510.
  • the secondary memory 1510 can include, for example, a hard disk drive 1512 and/or a removable storage drive 1514, representing a floppy disk drive, a magnetic tape drive, an optical disk drive, etc.
  • the removable storage drive 1514 reads from and/or writes to a removable storage unit 1518 in a well-known manner.
  • Removable storage unit 1518 represents a floppy disk, magnetic tape, optical disk, etc. which is read by and written to removable storage drive 1514.
  • the removable storage unit 1518 includes a computer usable storage medium having stored therein computer software and/or data.
  • secondary memory 1510 can include other similar means for allowing computer programs or other instructions to be loaded into computer system 1500.
  • Such means can include, for example, a removable storage unit 1522 and an interface 1520. Examples of such can include aprogram cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an EPROM, or PROM) and associated socket, and other removable storage units 1522 and interfaces 1520 which allow software and data to be transferred from the removable storage unit 1522 to computer system 1500.
  • Computer system 1500 can also include a communications interface 1524.
  • Communications interface 1524 allows software and data to be transferred between computer system 1500 and external devices.
  • Examples of communications interface 1524 can include a modem, a network interface (such as an Ethernet card), a communications port, a PCMCIA slot and card, etc.
  • S oftware and data transferred via communications interface 1524 are in the form of signals 1528 which canbe electronic, electromagnetic, optical or other signals capable of being received by communications interface 1524.
  • These signals 1528 are provided to communications interface 1524 via a communications path (i.e., channel) 1526.
  • This channel 1526 carries signals 1528 and can be implemented using wire or cable, fiber optics, a phone line, a cellular phone link, an RF link and other communications channels.
  • computer program medium and “computer usable medium” are used to generally refer to media such as removable storage drive 1514, a hard disk installed in hard disk drive 1512, and signals 1528. These computer program products are means for providing software to computer system
  • the invention is directed to such computer program products.
  • Computer programs are stored in main memory 1508 and/or secondary memory 1510. Computer programs can also be received via communications interface 1524. Such computer programs, when executed, enable the computer system 1500 to perform the features of the present invention as discussed herein. In particular, the computer programs, when executed, enable the processor 1504 to perform the features of the present invention. Accordingly, such computer programs represent controllers of the computer system 1500.
  • the software can be stored in a computer program product and loaded into computer system 1500 using removable storage drive 1514, hard drive 1512 or communications interface 1524.
  • the control logic when executed by the processor 1504, causes the processor 1504 to perform the functions of the invention as described herein.
  • the invention is implemented primarily in hardware using, for example, hardware components such as application specific integrated circuits (ASICs).
  • ASICs application specific integrated circuits
  • the invention is implemented using a combination of both hardware and software.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Pathology (AREA)
  • Animal Behavior & Ethology (AREA)
  • Molecular Biology (AREA)
  • Veterinary Medicine (AREA)
  • Public Health (AREA)
  • Immunology (AREA)
  • Surgery (AREA)
  • Biophysics (AREA)
  • Engineering & Computer Science (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Medical Informatics (AREA)
  • Chemical & Material Sciences (AREA)
  • Biochemistry (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • General Physics & Mathematics (AREA)
  • Analytical Chemistry (AREA)
  • Optics & Photonics (AREA)
  • Vascular Medicine (AREA)
  • Investigating Or Analysing Materials By Optical Means (AREA)

Abstract

Cette invention concerne un système, un procédé et un programme informatique permettant de sélectionner et d'analyser des images spectrales d'un circuit micro-circulatoire afin de déterminer des caractéristiques sanguines, telles que la concentration d'hémoglobine. Selon un mode de réalisation, on procède a une analyse à étapes multiples de chaque image d'entrée afin de déterminer si l'image en question convient pour des mesures d'hémoglobine et des prévisions. Pendant l'analyse sont mesurées les quantités suivantes: intensité de l'image, variation d'intensité de l'arrière plan de l'image, degré de mise au point de l'image, degré de flou du aux mouvements, nombre de vaisseaux dans l'image dans certaines plages de diamètres, contraste de bord moyen des vaisseaux, nombre de segments de bulle dans l'image et similitude entre deux images. Ce cycle d'analyse à étapes multiples porte sur les aspects suivants : analyse d'intensité, décomposition par transformation en ondelettes discrètes, mesure de mise au point, mesure du flou de mouvement, détection de vaisseaux et de bulles, et détection de répétitions. Pendant ces étapes, la qualité de l'image est classée en : excellente, médiocre, mauvaise. Seules les images jugées excellentes sont soumises à une estimation de l'hémoglobine. Toutefois, les images jugées aussi bien excellentes que médiocres déclenchent un processus de mise au point automatique par mesure de la mise au point. Selon un autre mode de réalisation, on utilise une transformée rapide de Fourier pour déterminer un rapport spectral permettant de détecter les images d'excellente qualité. La présente invention permet d'établir des prévisions d'hémoglobine en temps réel pour le sujet étudié.
PCT/US2001/026653 2000-08-25 2001-08-27 Systeme, procede et programme informatique pour l'analyse d'une image spectrale WO2002015788A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
AU2001286794A AU2001286794A1 (en) 2000-08-25 2001-08-27 System, method and computer program product for screening a spectral image
US10/362,643 US20040024296A1 (en) 2001-08-27 2001-08-27 System, method and computer program product for screening a spectral image

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US22757600P 2000-08-25 2000-08-25
US60/227,576 2000-08-25

Publications (2)

Publication Number Publication Date
WO2002015788A1 true WO2002015788A1 (fr) 2002-02-28
WO2002015788A9 WO2002015788A9 (fr) 2003-03-20

Family

ID=22853639

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2001/026653 WO2002015788A1 (fr) 2000-08-25 2001-08-27 Systeme, procede et programme informatique pour l'analyse d'une image spectrale

Country Status (2)

Country Link
AU (1) AU2001286794A1 (fr)
WO (1) WO2002015788A1 (fr)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8064976B2 (en) 2003-10-03 2011-11-22 Can Ince Systems and methods for sidesstream dark field imaging
WO2015043340A1 (fr) * 2013-09-30 2015-04-02 香港纺织及成衣研发中心有限公司 Procédé et dispositif de mise au point rapide pour imagerie multispectrale
US9131861B2 (en) 2004-11-30 2015-09-15 Academisch Medisch Centrum Pulsed lighting imaging systems and methods

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1997015229A1 (fr) * 1995-10-23 1997-05-01 Cytometrics, Inc. Procede et appareil d'analyse par imagerie reflechie
WO1999016353A1 (fr) * 1997-10-01 1999-04-08 Applied Spectral Imaging Ltd. Bio-imagerie spectrale de l'oeil
US5974338A (en) * 1997-04-15 1999-10-26 Toa Medical Electronics Co., Ltd. Non-invasive blood analyzer

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1997015229A1 (fr) * 1995-10-23 1997-05-01 Cytometrics, Inc. Procede et appareil d'analyse par imagerie reflechie
US5974338A (en) * 1997-04-15 1999-10-26 Toa Medical Electronics Co., Ltd. Non-invasive blood analyzer
WO1999016353A1 (fr) * 1997-10-01 1999-04-08 Applied Spectral Imaging Ltd. Bio-imagerie spectrale de l'oeil

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8064976B2 (en) 2003-10-03 2011-11-22 Can Ince Systems and methods for sidesstream dark field imaging
US8452384B2 (en) 2003-10-03 2013-05-28 MicroVision Medical Holdings B.V. Systems and methods for sidesstream dark field imaging
US9131861B2 (en) 2004-11-30 2015-09-15 Academisch Medisch Centrum Pulsed lighting imaging systems and methods
WO2015043340A1 (fr) * 2013-09-30 2015-04-02 香港纺织及成衣研发中心有限公司 Procédé et dispositif de mise au point rapide pour imagerie multispectrale

Also Published As

Publication number Publication date
WO2002015788A9 (fr) 2003-03-20
AU2001286794A1 (en) 2002-03-04

Similar Documents

Publication Publication Date Title
AP931A (en) Method and apparatus for reflected imaging analysis.
US4998533A (en) Apparatus and method for in vivo analysis of red and white blood cell indices
US4768516A (en) Method and apparatus for in vivo evaluation of tissue composition
EP1130998B1 (fr) Dispositif pour la determination non invasive du metabolisme de l'oxygene dans des tissus
DE69430791T2 (de) Lichtsensor mit mehreren Lichtquellen
US20040024296A1 (en) System, method and computer program product for screening a spectral image
US20040024295A1 (en) System, method and computer program product for measuring blood properties form a spectral image
CA2149392A1 (fr) Appareil permettant l'analyse des parametres sanguins sans effraction
JPH07308312A (ja) 非侵襲血液分析装置とその方法
EP3417276B1 (fr) Procédé, appareil et programme informatique pour l'estimation d'une caractéristique d'un milieux optiquement diffus
WO2002043561A9 (fr) Procede, systeme et produit de programme informatique pour mesurer les hematocrites dans les vaisseaux sanguins
WO2002015788A1 (fr) Systeme, procede et programme informatique pour l'analyse d'une image spectrale
US20120093389A1 (en) Temporal oximeter
WO2002015786A1 (fr) Systeme, procede et programme informatique permettant de mesurer des proprietes du sang a partir d'une image spectrale
WO1988000447A1 (fr) Analyse in vivo d'indices erythrocytaires
AU738223B2 (en) Method and apparatus for reflected imaging analysis
RU77144U1 (ru) Устройство для неинвазивного определения параметров крови
Winkelman Noninvasive blood cell measurements by imaging of the microcirculation
CN114742772B (zh) 一种人体血氧饱和度的测量方法、系统、装置和存储介质
Khurshid et al. Performance Evaluation of the Pallor in the Diagnosis of Anemia.
JPH09173300A (ja) 血液分析装置
Barlow et al. Breast biopsy analysis by spectroscopic imaging
CN117274162A (zh) 一种基于可见光图像的无创胆红素检测方法
Shelke et al. GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES

Legal Events

Date Code Title Description
AK Designated states

Kind code of ref document: A1

Designated state(s): AE AG AL AM AT AU AZ BA BB BG BR BY BZ CA CH CN CO CR CU CZ DE DK DM DZ EC EE ES FI GB GD GE GH GM HR HU ID IL IN IS JP KE KG KP KR KZ LC LK LR LS LT LU LV MA MD MG MK MN MW MX MZ NO NZ PH PL PT RO RU SD SE SG SI SK SL TJ TM TR TT TZ UA UG US UZ VN YU ZA ZW

AL Designated countries for regional patents

Kind code of ref document: A1

Designated state(s): GH GM KE LS MW MZ SD SL SZ TZ UG ZW AM AZ BY KG KZ MD RU TJ TM AT BE CH CY DE DK ES FI FR GB GR IE IT LU MC NL PT SE TR BF BJ CF CG CI CM GA GN GQ GW ML MR NE SN TD TG

121 Ep: the epo has been informed by wipo that ep was designated in this application
DFPE Request for preliminary examination filed prior to expiration of 19th month from priority date (pct application filed before 20040101)
COP Corrected version of pamphlet

Free format text: PAGES 1/11-11/11, DRAWINGS, REPLACED BY NEW PAGES 1/12-12/12; DUE TO LATE TRANSMITTAL BY THE RECEIVING OFFICE

REG Reference to national code

Ref country code: DE

Ref legal event code: 8642

WWE Wipo information: entry into national phase

Ref document number: 10362643

Country of ref document: US

122 Ep: pct application non-entry in european phase
NENP Non-entry into the national phase

Ref country code: JP