A robust nonlinear tissue-component discrimination method for computational pathology

Laboratory Investigation; a Journal of Technical Methods and Pathology
Jacob S SarneckiPei-Hsun Wu

Abstract

Advances in digital pathology, specifically imaging instrumentation and data management, have allowed for the development of computational pathology tools with the potential for better, faster, and cheaper diagnosis, prognosis, and prediction of disease. Images of tissue sections frequently vary in color appearance across research laboratories and medical facilities because of differences in tissue fixation, staining protocols, and imaging instrumentation, leading to difficulty in the development of robust computational tools. To address this challenge, we propose a novel nonlinear tissue-component discrimination (NLTD) method to register automatically the color space of histopathology images and visualize individual tissue components, independent of color differences between images. Our results show that the NLTD method could effectively discriminate different tissue components from different types of tissues prepared at different institutions. Further, we demonstrate that NLTD can improve the accuracy of nuclear detection and segmentation algorithms, compared with using conventional color deconvolution methods, and can quantitatively analyze immunohistochemistry images. Together, the NLTD method is objective, robust, and effe...Continue Reading

References

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Citations

Mar 23, 2017·IEEE Transactions on Medical Imaging·Luong NguyenS Chakra Chennubhotla

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Software Mentioned

Graphpad Prism
RBJH
MATLAB
Windows 7 Professional
NLTD

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