Multispectral/hyperspectral image enhancement for biological cell analysis

Nuffer, Lisa L.; Medvick, Patricia A.; Foote, H.P.; Solinsky, J.C.

doi:10.1002/cyto.a.20294

Cited by 17 publications

(14 citation statements)

References 7 publications

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“…Spectral images, however, typically contain tens or hundreds of individual color channels, providing detailed color information. Furthermore, spectral image enhancement can be applied to spectral images to enhance the contrast of the wavelength-dependent properties of the object in the images [6,7,8,9,10]. Enhanced spectral images of the retina could be used for early detection of DR.…”

Section: Introductionmentioning

confidence: 99%

Multichannel Spectral Image Enhancement for Visualizing Diabetic Retinopathy Lesions

Fält

Yamaguchi

Murakami

et al. 2014

Lecture Notes in Computer Science

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Abstract. Spectral imaging is a useful tool in many fields of scientific research and industry. Spectral images contain both spatial and spectral information of the scene. Spectral information can be used for effective visualization of the features-of-interest. One approach is to use spectral image enhancement techniques to improve the diagnostic accuracy of medical image technologies like retinal imaging. In this paper, two multichannel spectral image enhancement methods and a technique to further improve the visualization are presented. The methods are tested on four multispectral retinal images which contain diabetic retinopathy lesions. Both of the methods improved the detectability and quantitative contrast of the diabetic lesions when compared to standard color images and are potentially valuable for clinicians and automated image analyses.

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Section: Introductionmentioning

confidence: 99%

Multichannel Spectral Image Enhancement for Visualizing Diabetic Retinopathy Lesions

Fält

Yamaguchi

Murakami

et al. 2014

Lecture Notes in Computer Science

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“…[1][2][3][4][5][6][7] In pathology, particular interest is focused on the possible application of this imaging modality to improve tissue detection, classification and visualization. [8][9][10][11][12][13][14][15] In contrast to the conventional RGB-color image acquisitions systems, which use wideband filters, multispectral imaging uses N narrowband filters to capture images across wide spectral range. The utilization of narrowband filters enables a multispectral image acquisition device to capture spectral features that are not normally captured with RGB-color image acquisition devices.…”

Section: Introductionmentioning

confidence: 99%

Digital simulation of staining in histopathology multispectral images: enhancement and linear transformation of spectral transmittance

Bautista

Yagi

2012

J. Biomed. Opt.

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Abstract. Hematoxylin and eosin (H&E) stain is currently the most popular for routine histopathology staining. Special and/or immuno-histochemical (IHC) staining is often requested to further corroborate the initial diagnosis on H&E stained tissue sections. Digital simulation of staining (or digital staining) can be a very valuable tool to produce the desired stained images from the H&E stained tissue sections instantaneously. We present an approach to digital staining of histopathology multispectral images by combining the effects of spectral enhancement and spectral transformation. Spectral enhancement is accomplished by shifting the N-band original spectrum of the multispectral pixel with the weighted difference between the pixel's original and estimated spectrum; the spectrum is estimated using M < N principal component (PC) vectors. The pixel's enhanced spectrum is transformed to the spectral configuration associated to its reaction to a specific stain by utilizing an N × N transformation matrix, which is derived through application of least mean squares method to the enhanced and target spectral transmittance samples of the different tissue components found in the image. Results of our experiments on the digital conversion of an H&E stained multispectral image to its Masson's trichrome stained equivalent show the viability of the method.

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“…Results on several pilot studies on the utility of multispectral imaging to improve the visualization of tissue structures and accuracy of quantitative histopathology image analysis showed promise [2][3][4][5][6][7][8][9][10][11][12][13][14][15]. A multispectral image acquisition system captures the same scene at different spectral wavelength using narrowband spectral filters producing a stack of N grey-level spectral images, where N corresponds to the number of narrowband spectral filters employed by the imaging system.…”

Section: Introductionmentioning

confidence: 99%

“…It is typically employed to pre-process images to improve the accuracy of the quantitative image analyses [16][17][18][19][20]. Multispectral enhancements are implemented to either improve our visual perception of the objects in a multispectral image or to particularly detect an object which is not visually perceptible [2][3][4][5][6][7][8][14][15]. When the objective of the enhancement is to emphasize the spectral-color difference between the object of interest and its background the visual clarity of the different objects in the background are not generally given attention.…”

Section: Introductionmentioning

confidence: 99%

Multispectral Enhancement Method to Increase the Visual Differences of Tissue Structures in Stained Histopathology Images

Bautista

Yagi

2012

Analytical Cellular Pathology

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Abstract. In this paper we proposed a multispectral enhancement scheme in which the spectral colors of the stained tissuestructure of interest and its background can be independently modified by the user to further improve their visualization and color discrimination. The colors of the background objects are modified by transforming their N-band spectra through an NxN transformation matrix, which is derived by mapping the representative samples of their original spectra to the spectra of their target colors using least mean square method. On the other hand, the color of the tissue structure of interest is modified by modulating the transformed spectra with the sum of the pixel's spectral residual-errors at specific bands weighted through an NxN weighting matrix; the spectral error is derived by taking the difference between the pixel's original spectrum and its reconstructed spectrum using the first M dominant principal component vectors in principal component analysis. Promising results were obtained on the visualization of the collagen fiber and the non-collagen tissue structures, e.g., nuclei, cytoplasm and red blood cells (RBC), in a hematoxylin and eosin (H&E) stained image.

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Multispectral/hyperspectral image enhancement for biological cell analysis

Cited by 17 publications

References 7 publications

Multichannel Spectral Image Enhancement for Visualizing Diabetic Retinopathy Lesions

Multichannel Spectral Image Enhancement for Visualizing Diabetic Retinopathy Lesions

Digital simulation of staining in histopathology multispectral images: enhancement and linear transformation of spectral transmittance

Multispectral Enhancement Method to Increase the Visual Differences of Tissue Structures in Stained Histopathology Images

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