Digital Staining of Unstained Pathological Tissue Samples through Spectral Transmittance Classification

University of Pittsburgh, Pittsburgh, Pennsylvania, United States
Optical Review (Impact Factor: 0.66). 01/2005; 12(1):7-14. DOI: 10.1007/s10043-005-0007-0


Histological structures of a pathological tissue sample convey information relevant to the diagnosis of the disease that might have afficted the person. To reveal the morphology of these structures clearly, pathological tissues are stained. In this paper, a digital staining methodology for pathological tissue samples is introduced. Digital staining implies the application of digital processing techniques to transform the image of an unstained sample to its stained image counterpart. In the method, the transmittance spectra of the unstained and Hematoxylin and Eosin (H&E) stained multispectral images (16 bands) of specific tissue components are utilized. Two experiments were conducted to probe the possibility of the digital staining framework: the linear mapping of spectral transmittances, and the classification of spectral transmittances in conjunction with the linear mapping of specific transmittance data sets. The method classified the four tissue components, e.g. nucleus, cytoplasm, red blood cells, and the white region (region devoid of tissue structures), while the misclassifications between components with spectral transmittances that are closely similar were not completely rectified. 2005 The Optical Society of Japan

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Available from: Masahiro Yamaguchi,
    • "Digital images allow the development of digital algorithms for tissue analysis,[4–7] hence are obvious candidates for computational analysis. The practical application of multispectral and hyper-spectral imaging to pathology has also attracted the attention of several researchers, particularly its usefulness in bringing out details that are otherwise inconspicuous with the conventional RGB color imaging.[8–10] "
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    ABSTRACT: Staining of tissue specimens is a classical procedure in pathological diagnosis to enhance the contrast between tissue components such that identification and classification of these components can be easily performed. In this paper, a framework for digital staining of pathological specimens using the information derived from the L-band spectral transmittance of various pathological tissue components is introduced, particularly the transformation of a Hematoxylin and Eosin (HE) stained specimen to its Masson-Trichrome (MT) stained counterpart. The digital staining framework involves the classification of tissue components, which are highlighted when the specimen is actually stained with MT stain, e.g. fibrosis, from the HE-stained image; and the linear mapping between specific sets of HE and MT stained transmittance spectra through pseudo-inverse procedure to produce the LxL transformation matrices that will be used to transform the HE stained transmittance to its equivalent MT stained transmittance configuration. To generate the digitally stained image, the decisions of multiple quadratic classifiers are pooled to form the weighting factors for the transformation matrices. Initial results of our experiments on liver specimens show the viability of multispectral imaging (MSI) for the implementation of digital staining in the pathological context.
    Proceedings of SPIE - The International Society for Optical Engineering 01/2005; 5747. DOI:10.1117/12.595016 · 0.20 Impact Factor
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