Digital Staining of Unstained Pathological Tissue Samples through Spectral Transmittance Classification

Bautista, Pinky A.; Abe, Tokiya; Yamaguchi, Masahiro; Yagi, Yukako; Ohyama, Nagaaki

doi:10.1007/s10043-005-0007-0

Cited by 23 publications

(32 citation statements)

References 15 publications

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“…The N-band spectra of the objects' original and target colors, which could either be calculated or measured, are also provided by the user at this phase. In this work the relation between the N-band spectra of the original and target colors is considered linear such that we can derive an NxN matrix describing such relation by least mean-square pseudoinverse method [5]. In the object detection phase, the spectral residual-error configurations of the different objects in the sample images are investigated.…”

Section: Proposed Multispectral Enhancement Methodsmentioning

confidence: 99%

“…At some bands the error of the object could be small while at some bands it could be large. In previous works [4,5] the spectral residual-error characteristics of the various objects in the image were investigated to gain knowledge on the spectral bands at which the spectral errors' magnitudes of the object of interest are larger compared to the background objects. These bands were considered as the bands for enhancement since at these bands the object of interest can be fairly detected.…”

Section: Detection Of Object Of Interestmentioning

confidence: 99%

“…When abnormal changes in the ratio between the cytoplasm and nuclei areas are observed other staining techniques, i.e., special stains or immunohistochemical stain, are employed to further probe the cause of the abnormality. Due to the popularity of the H&E staining, a number of investigations have been done to digitally simulate its staining effect and to investigate the discrimination of H&E stained tissue structures which are ordinarily differentiated with the use of other types of stain [2][3][4][5][6][7][8]. The success of these research endeavors would eventually allow pathologists to quantify the distribution of tissue structures in the absence of special stains.…”

Section: Introductionmentioning

confidence: 99%

“…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%

See 4 more Smart Citations

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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Section: Proposed Multispectral Enhancement Methodsmentioning

confidence: 99%

Section: Detection Of Object Of Interestmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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Investigation on the potential of Mueller matrix imaging for digital staining

Wang

Lim

Srivastava

et al. 2015

Journal of Biophotonics

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Digital staining based on Mueller matrix measurements and their derivatives was investigated. Mueller matrix imaging was performed at the microscopic level on gastric tissue sections. Full Mueller matrices (4 × 4) were reconstructed using recorded images, followed by the extraction of polarization parameters. The most effective parameters and their combinations were extracted from Mueller matrix elements, principal component scores and polarization parameters respectively to classify samples into three categories - i.e. cancer, dysplasia and intestinal metaplasia/normal glands for various regions of interest sizes. It was observed that two-step classification yielded higher classification accuracy than the traditional one-step classification and that pixel classification based on Mueller matrix elements yielded higher accuracy than that based on polarization parameters and derived principal components. Moreover, Mueller matrix images with a lower spatial resolution generated higher classification accuracy but those with a higher spatial resolution revealed more morphological details.ns. The original stained image (top) and the digital staining image (bottom).

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Digital staining for multispectral images of pathological tissue specimens based on combined classification of spectral transmittance

Bautista

Abe

Yamaguchi

et al. 2005

Computerized Medical Imaging and Graphics

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Digital Staining of Unstained Pathological Tissue Samples through Spectral Transmittance Classification

Cited by 23 publications

References 15 publications

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

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

Investigation on the potential of Mueller matrix imaging for digital staining

Digital staining for multispectral images of pathological tissue specimens based on combined classification of spectral transmittance

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