Classifications

• • 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
  • A61B5/0082—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence adapted for particular medical purposes
  • A61B5/0088—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence adapted for particular medical purposes for oral or dental tissue

Definitions

• This invention generally relates to a method and apparatus for dental imaging and more particularly to an improved method for early detection of caries using fluorescence and scattering of light.
• QLF quantitative light-induced fluorescence
• U.S. Patent No. 4,515,476 discloses use of a laser for providing excitation energy that generates fluorescence at some other wavelength for locating carious areas.
• U.S. Patent No. 6,231,338 discloses an imaging apparatus for identifying dental caries using fluorescence detection.
• U.S. Patent Application Publication No. 2004/0240716 discloses methods for improved image analysis for images obtained from fluorescing tissue.
• U.S. Patent No. 4,479,499 describes a method for using transillumination to detect caries based on the translucent properties of tooth structure.
• U.S. Patent Application Publication No. 2004/0202356 (Stookey et al.) describes mathematical processing of spectral changes in fluorescence in order to detect caries in different stages with improved accuracy. Acknowledging the difficulty of early detection when using spectral fluorescence measurements, the '2356 Stookey et al. disclosure describes approaches for enhancing the spectral values obtained, effecting a transformation of the spectral data that is adapted to the spectral response of the camera that obtains the fluorescent image.
• a method for processing images to detect dental caries comprising: (a) directing incident light toward a tooth, wherein the incident light excites a fluorescent emission from the tooth tissue;
• Figure 1 is a schematic block diagram of an imaging apparatus for caries detection according to one embodiment
• Figure 2 is a view showing the process for image processing that combines the fluorescence and back-scattered image data to generate a diagnostic image according to the present invention
• Figure 3 is a schematic block diagram showing the image processing steps for the removal of spectral reflectance image components from the reflectance image data
• Figure 4 is a schematic block diagram showing the steps for the spatial registration of the modified reflectance image data, and fluorescence image data arrays.
• Figure 5 is a view showing the process for combining dental image data to generate a diagnostic image according to the present invention.
• fluorescence can be used to detect dental caries using either of two characteristic responses: First, excitation by a blue light source causes healthy tooth tissue to fluoresce in the green spectrum. Secondly, excitation by a red light source can cause bacterial by-products, such as those indicating caries, to fluoresce in the red spectrum.
• reflectance In order for an understanding of how light is used in the present invention, it is important to give more precise definition to the terms “reflectance” and “back-scattering” as they are used in biomedical applications in general and, more particularly, in the method and apparatus of the present invention.
• reflectance generally denotes the sum total of both specular reflectance and scattered reflectance. (Specular reflection is that component of the excitation light that is reflected by the tooth surface at the same angle as the incident angle.)
• specular component of reflectance is of no interest and is, instead, generally detrimental to obtaining an image or measurement from a sample.
• the component of reflectance that is of interest for the present application is from back-scattered light only. Specular reflectance must be blocked or otherwise removed from the imaging path.
• back-scattered reflectance is used in the present application to denote the component of reflectance that is of interest.
• Back- scattered reflectance is defined as that component of the excitation light that is elastically back-scattered over a wide range of angles by the illuminated tooth structure.
• Reflectance image data refers to image data obtained from back-scattered reflectance and specular reflectance optical components, hi the scientific literature, back-scattered reflectance may also be referred to as back-reflectance or simply as back- scattering. Back-scattered reflectance is at the same wavelength as the excitation light.
• back-scattered reflectance of light from an illuminated area can be at measurably different levels for normal versus carious areas.
• This change in reflectance, taken alone, may not be sufficiently pronounced to be of diagnostic value when considered by itself, since this effect is very slight, although detectable.
• back-scattered reflectance may be less effective an indicator than at earlier stages.
• a light source 11 directs an incident light 16, at a blue wavelength range or other suitable wavelength range, toward tooth 20 through an optional lens 14 or other light beam conditioning component.
• the tooth 20 may be illuminated at a proximal surface or at an occlusal surface.
• Two components of light are then detected by a digital color camera 32 through a lens 22; a back-scattered light component 18 having the same wavelength as the incident light and having measurable reflectance; and a fluorescent light component 19 that has been excited due to the incident light.
• a second light source 12 directs an incident light 16, at a second wavelength range, toward tooth 20 through an optional lens 15 or other light beam-conditioning component.
• two components of light are then detected by a digital color camera 32 through a lens 22 corresponding to the response to each of the two light sources.
• the reflectance image component could be generated from the light from the first source; and a fluorescent light component could be generated from the light from the second source.
• the two corresponding digital color images generated by the digital camera are the reflectance image 34 and fluorescence image 35. These two images are combined by image processing in step 38.
• the resulting two images, the back-scattered reflectance image, 50, and the diagnostic image, 52, shown in Figure 5, can then displayed on a computer monitor 40, printed or otherwise presented for interpretation, hi Figure 1 , only the back-scattered reflectance image and diagnostic image are shown displayed.
• the two sets of input data to step 38 are the two sets of image array data.
• the reflectance image, 34 contains both back-scattered reflectance and specular reflectance components.
• the second image contains the fluorescent image data. Since the reflectance image will be used both as a component of the final diagnostic image, and for direct viewing, this color image array is first color corrected to compensate for any variation in exposure to the tooth in step 80.
• a simple embodiment of this step calls for each of the red, green and blue components to be multiplied by a color-specific constant, e.g., 1.05, 1.0, 0.95.
• the three constants can be chosen such that the resulting color image has a natural color balance when displayed later on a computer monitor. In this case, for example, a gray object near the tooth, would appear gray, rather than with a color cast.
• the resulting image array is the color balanced reflectance image 82.
• Step 91 indicates the receiving of the color corrected reflectance image which has dimensions, (n lines x m pixels x 3; red, green and blue color records).
• step 92 several image (pixel) locations of said color corrected reflectance image are identified by selecting those whose values are above a signal threshold, e.g. 90% of the maximum image signal level. This can be done for each color record of the color-corrected reflectance image array, or a single color-record array.
• the identified pixel locations form a set, or mask array, ml.
• the thus-identified pixels may form groups of pixels, associated with a spectral reflectance region, or be the result of spurious signals.
• Small regions i.e., small objects, are eliminated by the morphological operations of erosion, followed by dilation 94.
• Erosion and dilation are techniques from the field of morphological image processing (E. R. Dougherty, An Introduction to Morphological Image Processing. SPIE Optical Engineering Press, Bellingham Washington, USA, 1992, Ch. 1).
• the result is a logical image array with several contiguous groups of pixels, i.e., spectral reflectance regions, identified.
• the input to this operation is the logical image array mask, ml ', from step 94.
• the operations of morphological dilation 96 and logical pixel-by-pixel subtraction 97 are applied. These result in a logical image array, m2, with only the areas surrounding the spectral reflectance regions identified.
• each surround region location in m2 the average pixel value of the corresponding reflectance image is computed in operation 98. This value is then added to the output of a random number generator to generate a series of signal value for each spectral reflectance region.
• the number of random numbers is the same as the number of pixels for each region, ml .
• the pixel values for each spectral reflectance region are then replaced by the set of values for each surrounding region. Thus, each region will have an average value of its surrounding area with the addition of a random texture.
• step 90 is an image with the specular reflectance reduced or removed.
• This image data is the spectral reflectance compensated color corrected reflectance image, or simply called the back-scattered reflectance image, since it has the spectral reflectance component removed.
• Step 110 shows the procedure. Referring to Figure 4, first calculate the cross-correlation matrix for the two image data arrays 111,
• c ⁇ l,m -, J ;Y N ⁇ 'Y M ⁇ m MiJ)Ki - IJ -m)- wb
• w is the back-scattered reflectance image array and b is the fluorescence image array
• N and M can be either the dimensions of the image data arrays, in pixels, or of corresponding cropped sections thereof.
• the number of pixels in each direction that two image data array are offset by is found from the location of the maximum of the array c 112.
• Array b is shifted in the opposite direction in step 114.
• the result is a shifted fluorescence image data array, F, 115 that is registered with the back-scattered reflectance image data array R.
• the processing of the modified image data uses both the reflectance and fluorescence image data to generate a final image that can be used to identify carious areas of the tooth.
• this image processing performs the following operation to form the diagnostic image, D, for each pixel:
• the modified image data could be combined using one-dimensional look-up table (LUT) operations, common in image processing.
• LUT look-up table
• a LUT operation invokes a discrete signal mapping using a vector. For each pixel location in an input image, the signal value is used as the index into the LUT vector. The value of the LUT vector at that index value is then stored into a modified image, corresponding to the current location in the input image array.
• equation (1) could written as,
• LUT P and LUT R are vectors, typically of length K, where K is the number of possible discrete levels that F va ⁇ ue and Rvalue can take on. If the LUT arrays include the transformation due to the values o and p, then Equation (2) is,
• Equations (1) and (2) are special cases
• T T [ ⁇ F value -o, R va tue-p] (4)
• T is a transformation, for example a polynomial
• T A F va ⁇ ue + B Rvalue + C F value Rvalue + D r value ⁇ • • (5)
• a fifth way that the modified image data can be combined is in the form of a multidimensional look-up table.
• Image processing to sharpen the appearance of the diagnostic image can be performed 60, for example, by the application of an image sharpening, or high-pass filtering operation via discrete convolution (G. A. Baxes, Digital Image Processing Principles and Applications, John Wiley, New York, 1994, pp. 91-95).

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• Health & Medical Sciences (AREA)
• Life Sciences & Earth Sciences (AREA)
• Heart & Thoracic Surgery (AREA)
• Medical Informatics (AREA)
• Physics & Mathematics (AREA)
• Dentistry (AREA)
• Biophysics (AREA)
• Pathology (AREA)
• Engineering & Computer Science (AREA)
• Biomedical Technology (AREA)
• Audiology, Speech & Language Pathology (AREA)
• Oral & Maxillofacial Surgery (AREA)
• Molecular Biology (AREA)
• Surgery (AREA)
• Animal Behavior & Ethology (AREA)
• General Health & Medical Sciences (AREA)
• Public Health (AREA)
• Veterinary Medicine (AREA)
• Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)
• Dental Tools And Instruments Or Auxiliary Dental Instruments (AREA)
• Endoscopes (AREA)

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• 2007
  • 2007-01-17 US US11/623,804 patent/US8224045B2/en not_active Expired - Fee Related
• 2008
  • 2008-01-03 JP JP2009546393A patent/JP2010516332A/ja active Pending
  • 2008-01-03 WO PCT/US2008/000039 patent/WO2008088672A1/en not_active Ceased
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