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.
A multispectral enhancement method that preserves the natural color of the background pixels was previously
proposed. In such method, the band for enhancement was identified from the N-band spectral residual-error of the
objects of interest. The spectral residual-error is determined by taking the difference between the original spectrum
of the pixel and its estimate using M<<N principal components in principal component analysis (PCA). However,
for stained histopathology images where staining variations do exist even among tissue sections the band for
enhancement could vary. In this work, we introduced a modification to the previously proposed multispectral
enhancement method such that the band for enhancement could be specified independent of the spectral residualerror
configurations. In the proposed modification the original spectral transmittance of the pixels at each band are
shifted by the product between the spectral residual-error coefficient, which is the most dominant PC coefficient of
the spectral error, of the pixel and the weighting factor assigned by the user to each band. Results of our experiments
on H&E stained sections of liver tissue show that the proposed modification delivers more consistent enhancement
results compared to the previously proposed methods, especially when the band for enhancement varies.
The presence of a liver disease such as cirrhosis can be determined by examining the proliferation of
collagen fiber from a tissue slide stained with special stain such as the Masson's trichrome(MT) stain.
Collagen fiber and smooth muscle, which are both stained the same in an H&E stained slide, are stained
blue and pink respectively in an MT-stained slide. In this paper we show that with multispectral imaging
the difference between collagen fiber and smooth muscle can be visualized even from an H&E stained
image. In the method M KL bases are derived using the spectral data of those H&E stained tissue
components which can be easily differentiated from each other, i.e. nucleus, cytoplasm, red blood cells,
etc. and based on the spectral residual error of fiber weighting factors are determined to enhance spectral
features at certain wavelengths. Results of our experiment demonstrate the capability of multispectral
imaging and its advantage compared to the conventional RGB imaging systems to delineate tissue
structures with subtle colorimetric difference.
Physical staining is indispensable in pathology. While physical staining uses chemicals, "digital staining" exploits the
differing spectral characteristics of the different tissue components to simulate the effect of physical staining. Digital
staining for pathological images involves two basic processes: classification of tissue components and digital
colorization whereby the classified tissue components are impressed with colors associated to their reaction to specific
dyes. Spectral features, i.e. spectral transmittance, of the different tissue structures are dependent on the staining
condition of the tissue slide. Thus, if the staining condition of the test image is different, classification result is affected,
and the resulting digitally-stained image may not reflect the desired result. This paper shows that it is possible to obtain
robust classification results by correcting the dye amount of each test-image pixel using Beer Lambert's Law. Also the
effectiveness of such technique to be incorporated to the current digital staining scheme is investigated as well.
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.