Multispectral and hyperspectral imaging: applications for medical and surgical diagnostics

Freeman, Jonathan D.; Downs, Florence S.; Marcucci, L.; Lewis, E. Neil; Blume, Bradley T.; Rish, Jeff W.

doi:10.1109/iembs.1997.757727

Cited by 19 publications

(12 citation statements)

References 2 publications

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“…178 Fundamental to the concept is the ability to both spatially and spectrally resolve a heterogeneous surface topography for the purpose of detecting and classifying otherwise indiscernible features or targets, natural and manmade, by exploiting known spectral reflectance characteristics. 252 Extension of this technology to biomedical engineering applications will allow novel exploration of anatomy, physiology, biochemistry, and pathology. In this review, we have assessed major efforts and advances of spectral imaging techniques in biomedical and biological scientific research spanning from microscopy to clinical in vivo imaging.…”

Section: Discussionmentioning

confidence: 99%

“…This review shows that although the development of spectral imaging sensors and data analysis techniques are time-consuming and arduous, the types of information obtained by the spectral imaging techniques can enable us to clearly see characteristics of various tissues and organs in healthy and diseased subjects, which previously have not been able to be investigated so directly. 252 With the refinement of current technologies and the development of new techniques, additional information will be available in helping dissect biomedical analysis in the future.…”

Section: Discussionmentioning

confidence: 99%

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Review of spectral imaging technology in biomedical engineering: achievements and challenges

et al. 2013

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Spectral imaging is a technology that integrates conventional imaging and spectroscopy to get both spatial and spectral information from an object. Although this technology was originally developed for remote sensing, it has been extended to the biomedical engineering field as a powerful analytical tool for biological and biomedical research. This review introduces the basics of spectral imaging, imaging methods, current equipment, and recent advances in biomedical applications. The performance and analytical capabilities of spectral imaging systems for biological and biomedical imaging are discussed. In particular, the current achievements and limitations of this technology in biomedical engineering are presented. The benefits and development trends of biomedical spectral imaging are highlighted to provide the reader with an insight into the current technological advances and its potential for biomedical research.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Review of spectral imaging technology in biomedical engineering: achievements and challenges

et al. 2013

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“…Hyperspectral systems, HSI) are in use for more than three decades for various applications such as remote sensing [4][5][6][7] , agriculture and geology [8][9][10] , food industry 11,12 , bio-medical imaging and noninvasive tissue analysis and for cancer detection [13][14][15] , and many more. The overall performance of common classical hyperspectral (HS) imagers is always limited, setting tradeoffs between the signal-to-noise ratio (SNR), acquisition speed, spectral and spatial resolution, field of view, spectral window and physical dimensions of the imager.…”

Section: Introductionmentioning

confidence: 99%

Compressive and classical hyperspectral systems: a fundamental comparison

2015

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Hyperspectral imagery involves capturing and processing a tremendous amount of data, which sets severe system resource requirements. This has motivated the application of compressive sensing for different spectroscopic and spectroscopic imager systems. Several new compressive hyperspectral architectures have been designed to stretch the common limitations of classical systems. However, the application of the compressive sensing framework involves design of system architectures that differ significantly from the conventional ones. Since compressive sensing differs essentially from conventional sensing, it cannot be implemented for hyperspectral imaging by simply modifying one of the components of a conventional hyperspectral system, rather it requires a complete new design. In this work we present a comparison between four compressive hyperspectral architectures to conventional architectures. The compressive hyperspectral sensing compared are: Coded Aperture Snapshot Spectral Imaging (CASSI), Compressive HS Imaging by Separable Spatial And Spectral Operators (CHISSS), (Liquid-crystal Compressive spectral Imager) LiCSI and (Spectral Single-Pixel) SSP systems. Those methods are compared to conventional spatial/spectral scanning hyperspectral such as pushbroom, whiskbroom and color filter techniques. A fundamental comparison between these architectures is presented in terms of optical system volume and radiometric efficiency.

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“…The energy is captured in a narrow slice of wavebands of approximately [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] nm. Each recorded pixel in each waveband contains the spatial and spectral information that can be extracted as target reflectance or signature as a function of wavelength.…”

Section: Introductionmentioning

confidence: 99%

“…The radiometric properties can be used for target classification or identification on hyperspectral imageries (1). HSI has been employed extensively in all applications, such as agriculture (2,3), surveillance (4), remote sensing (5,6), medical (7,8) and military (9).…”

Section: Introductionmentioning

confidence: 99%

Illumination invariance and shadow compensation via spectro-polarimetry technique

et al. 2012

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A major problem for obtaining target reflectance via hyperspectral imaging systems is the presence of illumination and shadow effects. These factors are common artefacts, especially when dealing with a hyperspectral imaging system that has sensors in the visible to near infrared region. This region is known to have highly scattered and diffuse radiance which can modify the energy recorded by the imaging system. Shadow effect will lower the target reflectance values due to the small radiant energy impinging on the target surface. Combined with illumination artefacts, such as diffuse scattering from the surrounding targets, background or environment, the shape of the shadowed target reflectance will be altered. In this study we propose a new method to compensate for illumination and shadow effects on hyperspectral imageries by using a polarization technique. This technique, called spectropolarimetry, estimates the direct and diffuse irradiance based on two images, taken with and without a polarizer. The method is evaluated using a spectral 2 similarity measure, angle and distance metric. The results of indoor and outdoor tests have shown that using the spectro-polarimetry technique can improve the spectral constancy between shadow and full illumination spectra.

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Multispectral and hyperspectral imaging: applications for medical and surgical diagnostics

Cited by 19 publications

References 2 publications

Review of spectral imaging technology in biomedical engineering: achievements and challenges

Review of spectral imaging technology in biomedical engineering: achievements and challenges

Compressive and classical hyperspectral systems: a fundamental comparison

Illumination invariance and shadow compensation via spectro-polarimetry technique

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