Informatics, digital & computational pathology

Spectral imaging

Spectral imaging

Last author update: 9 December 2022
Last staff update: 9 December 2022

Copyright: 2019-2023,, Inc.

PubMed Search: Spectral imaging pathology

Aisha Abdelhafez, M.Sc.
Anil Parwani, M.D., Ph.D., M.B.A.
Page views in 2022: 121
Page views in 2023 to date: 61
Cite this page: Abdelhafez A, Parwani A. Spectral imaging. website. Accessed March 30th, 2023.
Definition / general
  • Spectral imaging, commonly referred to as imaging spectroscopy, combines traditional imaging and spectroscopy techniques to gather both spatial and spectral data about an object (J Biomed Opt 2013;18:100901)
Essential features
  • Technique that generates a spectrum (with varying spectral resolution) for every pixel of an image (Stud Health Technol Inform 2013;185:43)
  • Analysis is based on well validated standard spectroscopy algorithms used for identifying, unmixing and quantifying components in mixtures; spectral imaging software essentially modifies such methods to produce pixel by pixel quantitative data
  • Compared to traditional approaches, spectral imaging provides better detection, validation, separation and quantification (Stud Health Technol Inform 2013;185:43)
  • Spectrum: a collection of light intensities at different wavelengths
  • Spectroscopy, the science of acquiring and explaining the spectral characteristics of matter, is a broad, well established and old science
  • Newton described the dispersion of white light into its constituent colors in 1666
  • By 1900 the spectrograph, a system for measuring a spectrum, was widely used
  • Spectroscopy provided the spectrum of hydrogen that was explained by Balmer in 1885, which led to the Böhr model of the atom in 1913 and finally to the development of quantum mechanics by Schrödinger in 1926
  • Reference: Cytometry A 2006;69:735
  • Spectral imaging classification is dependent on the spectral resolution, the number of bands, bandwidth and contiguousness of bands as multispectral, hyperspectral (HSI) and ultraspectral
  • In general, multispectral imaging systems collect data in a small number of relatively non-contiguous, wide spectral bands, which are often measured in micrometers or tens of micrometers
  • These spectral bands have been chosen to gather intensity in very specific regions of the spectrum and are optimized for particular types of information that are most prominent in those bands
  • HSI systems can collect hundreds of spectral bands
  • Ultraspectral imaging systems can gather even more spectral bands than HSI systems
  • Concept of the hypercube data obtained by a spectral imaging system is illustrated in diagram 1
  • Data of spectral imaging can be visualized as a 3 dimensional (3D) cube or a stack of multiple 2 dimensional (2D) images due to its intrinsic structure, in which the cube face is a function of the spatial coordinates and the depth is a function of wavelength
  • One of the key benefits of this technique is that it can obtain the reflectance, absorption or fluorescence spectrum for each pixel in the image; this allows for the detection of biochemical changes in objects that cannot be identified using conventional gray scale or color imaging techniques
  • Reference: J Biomed Opt 2013;18:100901
  • Hardware architecture of a typical biomedical spectrum imaging system is illustrated in diagram 2
  • It typically consists of 4 components:
    • Collection optics or instruments
    • Spectral dispersion element
    • Detector
    • System control and data gathering module
  • Reference: J Biomed Opt 2013;18:100901
Diagrams / tables

Images hosted on other servers:

Concept of spectral data cube

Typical biomedical spectral imaging system

Collection optics
  • The term collection optics (or instruments) refers to collecting and imaging optics
    • This includes microscopes, endoscopes, camera lenses and other devices that can project an image onto the spectral dispersion element
  • Quality of an image decides the amount of information that can be obtained from it; the following list outlines the most prevalent factors that define the acquired images:
    • Spatial resolution identifies the items' nearest recognizable features; it mostly depends on
      • Wavelength (λ)
      • Numerical aperture (NA) of the objective lens
      • Magnification and the pixel size of the array detector, which is often a charged coupled device (CCD) camera
    • Lowest detectable signal relies on
      • Quantum efficiency of the detector (the higher the better)
      • System noise level (the lower the better)
      • NA of the optics (the higher the better)
      • Optics quality
    • Dynamic range of the captured data determines the number of various intensity levels that can be identified in an image
      • It depends on the maximal possible number of electrons at each pixel and on the lowest detectable signal (basically it is the ratio of these 2 values)
      • However, if the measured signal is weak and only partially fills the CCD well associated with each pixel, the dynamic range will be constrained
    • Field of view (FOV) determines the maximal area that can be imaged
    • Other factors include the exposure time range, which is typically determined by the detector, and binning CCD pixels to increase sensitivity (by trading off spatial resolution)
  • In actual measurements, there are a variety of imperfections that decrease the image's quality, including autofluorescence, nonspecific staining, bleaching and others; however, these should be distinguished from the physical restrictions imposed by the electro-optical system itself and the properties of light (Cytometry A 2006;69:735)
Spectral dispersion element
  • The spectral dispersion element is the main component of the system that enables the separation of the light into different wavelengths
  • 2 types of frequently used wavelength dispersion tools are dispersive spectrometers and spectral filters
  • Dispersive spectrometers consist of many transmitting or reflecting components separated by a distance corresponding to the wavelength of the light under analysis
    • Frequently it refers to prisms, gratings, prism grating prism (PGP) or beam splitters, which can be used with whiskbroom, pushbroom and some snapshot imaging modes
  • Spectral filters have the characteristic of passing radiation through a very narrow bandpass or spectral bin and this can be spectrally tuned over a wide spectral range, usually in a very short time
    • The most widely used tunable filters in spectral scanning instruments are acousto-optical tunable filters (AOTF), liquid crystal tunable filters (LCTF), circular and linear variable filters (CVF and LVF)
  • Interferometer is another frequently used spectral dispersion component that can produce spectral images by adjusting the optical path length difference (OPD) between 2 beams, capturing the interference intensity as a function of the OPD (interferogram) and Fourier transforming the interferograms pixel by pixel (Opt Express 2011;19:1291)
  • Used to gather the intensity of light necessary to measure the spectrum at each pixel in the image
  • A variety of sensors, including the photomultiplier tube, linear CCD, area CCD and CMOS, focal plane array and electron multiplying charge coupled device (EMCCD), can be used as detectors depending to the imaging required
Biomedical applications
  • Spectrum imaging may be helpful in applications where subtle spectral discrepancies exist in chemical components within a scene of spectral images
  • It has enabled applications in a wide variety of biomedical engineering studies, ranging from specific biochemical species detected in in vitro samples to organs of living individuals (J Biomed Opt 2013;18:100901)
In vivo spectral imaging
  • In vivo optical imaging enables noninvasive monitoring of living organisms and aids in the comprehension of metabolic processes and changes in the body brought on by disease
  • Spectral imaging has recently been used for in situ tissue diagnosis as one of the optical diagnostic technologies
  • Skin is one of the best candidates for in vivo spectral imaging among openly accessible tissues
    • Multispectral imaging system is used to evaluate the burn depth of the skin
    • It was reported that the spectral imaging method was more accurate in predicting burn healing than the traditional method (IEEE Trans Biomed Eng 1988;35:842)
    • Noninvasive spectral reflectance methods can be used for mapping blood oxygen saturation in various types of wounds, ranging from burn injuries and diabetic related ulcers to skin grafts
    • Additional studies involve identifying skin ulcers and spectral imaging of cancer skin fluorescence and reflectance (IEEE Eng Med Biol Mag 2004;23:40)
    • Recent studies have focused on 3D HSI on small sections of human skin, quantifying skin oxygen saturation and how it relates to conventional wound healing measures, evaluating ischemic wounds and studying breast cancers (Opt Express 2010;18:3244, Biomed Opt Express 2012;3:1433, J Biomed Opt 2011;16:066007)
    • These studies suggest that noninvasive in vivo skin surface detection using spectral imaging technology may be useful in the early evaluation of burn injuries and skin malignancies
  • Another important in vivo application of spectral imaging is retinal detection
  • Some earlier studies concentrated on the development of the retinal spectral imaging system coupled with a standard ophthalmic fundus camera (Invest Ophthalmol Vis Sci 2004;45:1464)
    • Some researchers also discussed how to use spectral images to analyze retinal abnormalities and determine their extent
  • In vivo spectral imaging technology has also been used for tongue surface identification and oral cancer diagnosis (Appl Opt 2010;49:2006)
  • Use of a spectral imaging system in conjunction with a gastroscope, endoscope and colposcope allows for the in vivo detection of some intracorporeal organs in addition to the exterior organs and tissues
    • Multispectral imaging system based on liquid crystal tunable filter (LCTF) was developed for the in vivo detection, quantitative staging and mapping of cervical cancer and precancer (IEEE Trans Biomed Eng 2001;48:96)
  • Another promising use of in vivo spectrum imaging is the guidance of surgical intervention, treatment and real time assessment of tissue response to therapy (Opt Express 2010;18:8058)
    • Surgeons can use in vivo spectral imaging to define the tumor margins and find any micrometastases or residual tumor cells
    • Nanotechnology and optical instrumentation advancements can be used for surgical oncology applications such as tumor localization, tumor margins, identification of significant adjacent structures, mapping of sentinel lymph nodes and detection of residual tumor cells (Annu Rev Med 2010;61:359)
  • Furthermore, some scientists perform in vivo diagnostics using diffuse spectral imaging technology
    • This technology has been used for early diagnosis and screening of malignant alterations in the oral cavity
    • Some initial findings indicate the possible application of this technique in routine clinical practice for rapid screening for oral cancer (J Opt 2013;15:055301)
Histological analysis
  • Spectral imaging technology might provide a novel tool for histological analysis
  • It can utilize the spectral characteristics of different stains applied in histological sections to segment features of interest for later morphometric evaluation
  • This technique has been effectively used in recent years to identify a variety of histology stains and immunolabeling reaction products and has shown to be capable of finding any color stained object
  • Spectral analysis of stain combinations was described, including
    • Safranin O / hematoxylin for proteoglycans in articular cartilage
    • Oil red O in adipose tissue
    • Peroxidase diaminobenzidine immunostain with either a fast green or hematoxylin counterstain
    • Alkaline phosphatase (AP) red and Vector Red immunostain and counterstains in a variety of tissues
  • Additionally, the studies discussed the possibility of using the spectral imaging method for quantitative assessment of the uptake by histologic samples of stains used in pathology to label tissue features of diagnostic importance (IEEE Trans Biomed Eng 2003;50:207)
  • Using HSI technology, which combines spectroscopy and imaging, an objective method was developed to identify gastric cancer (J Biomed Opt 2013;18:26010)
  • A more recent study used an infrared (IR) spectral imaging system to diagnose colon cancer (Cytometry A 2013;83:294)
  • Additionally, IR spectral imaging system has been employed to perform the histopathology mapping of biochemical changes in myocardial infarction, live epithelial cancer cells, histologic and biochemical correlations in tissue engineered cartilage and histomorphological evaluation of oral squamous cell carcinoma of the oral cavity (J Clin Pathol 2013;66:312)
Fluorescence and cytogenetics
  • One of the most widespread applications of spectral imaging technology is spectral karyotyping (SKY), which is based on FISH and revolutionized mouse and human cytogenetics
  • Recent applications of SKY include assessing
    • Chromosomal rearrangements in post Chernobyl papillary thyroid carcinomas
    • Detecting constitutional chromosomal abnormalities
    • Describing giant marker and ring chromosomes in a pleomorphic leiomyosarcoma
  • As a result, it is an important technique for the cytogenetic analysis of a wide variety of chromosomal abnormalities linked to a lot of genetic diseases and cancers (J Vis Exp 2012;60:3887)

Spectral imaging laboratory

Medical applications of hyperspectral imaging

Board review style question #1

In spectral imaging, the detector is used to gather which of the following?

  1. Color of light
  2. Contrast of light
  3. Hardness of light
  4. Intensity of light
Board review style answer #1
D. Intensity of light

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Reference: Spectral imaging
Board review style question #2
Which of the following components of the spectral imaging system is responsible for the separation of light into different wavelengths?

  1. Collection optics
  2. Detector
  3. Spectral dispersion element
  4. System control and data gathering module
Board review style answer #2
C. Spectral dispersion element

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Reference: Spectral imaging
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