Immunohistochemistry (IHC) staining is an important technique for the detection of one or more biomarkers within a single tissue section. In digital pathology applications, the correct unmixing of the tissue image into its individual constituent dyes for each biomarker is a prerequisite for accurate detection and identification of the underlying cellular structures. A popular technique thus far is the color deconvolution method¹ proposed by Ruifrok et al. However, Ruifrok's method independently estimates the individual dye contributions at each pixel which potentially leads to “holes and cracks” in the cells in the unmixed images. This is clearly inadequate since strong spatial dependencies exist in the tissue images which contain rich cellular structures. In this paper, we formulate the unmixing algorithm into a least-square framework of image patches, and propose a novel color deconvolution method which explicitly incorporates the spatial smoothness and structure continuity constraint into a neighborhood graph regularizer. An analytical closed-form solution to the cost function is derived for this algorithm for fast implementation. The algorithm is evaluated on a clinical data set containing a number of 3,3-Diaminobenzidine (DAB) and hematoxylin (HTX) stained IHC slides and demonstrates better unmixing results than the existing strategy.

Date Published: 19 March 2015
PDF: 6 pages
Proc. SPIE 9420, Medical Imaging 2015: Digital Pathology, 942004 (19 March 2015); doi: 10.1117/12.2079893

Published in SPIE Proceedings Vol. 9420:
Medical Imaging 2015: Digital Pathology
Metin N. Gurcan; Anant Madabhushi, Editor(s)