Issue 3: Flesh out README.rst (#4)

Co-authored-by: Lee Newberg <[email protected]>

Author: Leengit

README.rst

@@ -1,5 +1,5 @@
 ITKColorNormalization
-=================================
+=====================
 
 .. image:: https://github.com/InsightSoftwareConsortium/ITKColorNormalization/workflows/Build,%20test,%20package/badge.svg
 
@@ -14,6 +14,39 @@ ITKColorNormalization
 Overview
 --------
 
-This performs structure preserving color normalization on an image using a reference image.
+This performs "Structure Preserving Color Normalization" on an H&E image using a reference image.
 
-By performing a non-negative matrix factorization on an input image and a reference image, the colors in use in the reference image are transfered to the input image.
+H&E (hematoxylin and eosin) are stains used to color parts of cells in a histological image, often for medical diagnosis.
+Hematoxylin is a compound that stains cell nuclei a purple-blue color.  Eosin is a compound that stains extracellular matrix
+and cytoplasm pink.  However, the exact color of purple-blue or pink can vary from image to image, and this can make
+comparison of images difficult.  This routine addresses the issue by re-coloring one image (the first image supplied to the
+routine) using the color scheme of a reference image (the second image supplied to the routine).
+
+Structure Preserving Color Normalization is a technique described in [VPSAWBSSEN2016]_ and modified in [RAS2019]_.  The idea
+is to model the color of an image pixel as something close to pure white, which is reduced in intensity in a color-specific
+way via an optical absorption model that depends upon the amounts of hematoxylin and eosin that are present.  Non-negative
+matrix factorization is used on each analyzed image to simultaneously derive the amount of hematoxylin and eosin stain at
+each pixel and the effective colors of each stain.
+
+The implementation here accelerates the non-negative matrix factorization by choosing the initial estimate for the color
+absorption characteristics using a technique mimicking that presented in [AGHMMSWZ2013]_ and [NCKZ2018]_.  This approach
+finds a good solution for a non-negative matrix factorization by first transforming it to the problem of finding a convex
+hull for a set of points in a cloud.
+
+Bibliography
+------------
+
+.. [AGHMMSWZ2013] Arora S, Ge R, Halpern Y, Mimno D, Moitra A, Sontag D, Wu Y, Zhu M. A Practical Algorithm for Topic
+   Modeling with Provable Guarantees. Proceedings of the 30th International Conference on Machine Learning, PMLR
+   28(2):280-288, 2013.
+
+.. [NCKZ2018] Newberg LA, Chen X, Kodira CD, Zavodszky MI. Computational de novo discovery of distinguishing genes for
+   biological processes and cell types in complex tissues. PLoS One. 2018;13(3):e0193067. Published 2018
+   Mar 1. doi:10.1371/journal.pone.0193067
+
+.. [RAS2019] Ramakrishnan G, Anand D, Sethi A.  Fast GPU-Enabled Color Normalization for Digital Pathology.
+   arXiv:1901.03088. 2019 Jan.
+
+.. [VPSAWBSSEN2016] Vahadane A, Peng T, Sethi A, Albarqouni S, Wang L, Baust M, Steiger K, Schlitter AM, Esposito I,
+   Navab N. Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images. IEEE Trans Med
+   Imaging. 2016 Aug;35(8):1962-71. doi: 10.1109/TMI.2016.2529665. Epub 2016 Apr 27. PMID: 27164577.