Color Correction of Pathological Images Based on Dye Amount Quantification

Pathological images are color images of stained tissue slides, the color of which varies depending on staining conditions. For reliable diagnosis, the color variation must be corrected in these images. This paper proposes a color correction method for hematoxylin and eosin (H&E) stained pathological images in which the amounts of H&E dyes are estimated based on multispectral imaging technique and Beer Lambert law, and the color image is generated corresponding to the adjusted amount of dyes. This enables us to correct an image to an arbitrary or specified optimal staining-condition image. Through experiments using H&E stained human liver slide images, the effectiveness of the proposed method was confirmed.     

Development of aromatic ethers as solvents for a calix[6]arene derivative and extraction of amino acids and proteins

Journal of Inclusion Phenomena and Macrocyclic Chemistry - Calixarene derivatives are excellent host compounds for ionic species in liquid–liquid extraction. However, many studies using...     

Digital Staining of Unstained Pathological Tissue Samples through Spectral Transmittance Classification

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