title: Sparse Non-negative Matrix Factorization (SNMF) based color unmixing for breast histopathological image analysis.
abstract: Color deconvolution has emerged as a popular method for color unmixing as a pre-processing step for image analysis of digital pathology images. One deficiency of this approach is that the stain matrix is pre-defined which requires specific knowledge of the data. This paper presents an unsupervised Sparse Non-negative Matrix Factorization (SNMF) based approach for color unmixing. We evaluate this approach for color unmixing of breast pathology images. Compared to Non-negative Matrix Factorization (NMF), the sparseness constraint imposed on coefficient matrix aims to use more meaningful representation of color components for separating stained colors. In this work SNMF is leveraged for decomposing pure stained color in both Immunohistochemistry (IHC) and Hematoxylin and Eosin (H&E) images. SNMF is compared with Principle Component Analysis (PCA), Independent Component Analysis (ICA), Color Deconvolution (CD), and Non-negative Matrix Factorization (NMF) based approaches. SNMF demonstrated improved performance in decomposing brown diaminobenzidine (DAB) component from 36 IHC images as well as accurately segmenting about 1400 nuclei and 500 lymphocytes from H & E images.
authors.affiliation: Jiangsu Key Laboratory of Big Data Analysis Technique, Nanjing University of Information Science and Technology, Nanjing 210044, China; CICAEET, Nanjing University of Information Science and Technology, Nanjing 210044, China. Electronic address: xujung@gmail.com.
authors.forename: Jun
authors.initials: J
authors.lastname: Xu
authors.affiliation: Jiangsu Key Laboratory of Big Data Analysis Technique, Nanjing University of Information Science and Technology, Nanjing 210044, China; CICAEET, Nanjing University of Information Science and Technology, Nanjing 210044, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Xiang
authors.affiliation: Jiangsu Key Laboratory of Big Data Analysis Technique, Nanjing University of Information Science and Technology, Nanjing 210044, China; CICAEET, Nanjing University of Information Science and Technology, Nanjing 210044, China.
authors.forename: Guanhao
authors.initials: G
authors.lastname: Wang
authors.affiliation: Rutgers Cancer Institute of New Jersey, NJ 08901, USA.
authors.forename: Shridar
authors.initials: S
authors.lastname: Ganesan
authors.affiliation: Department of Pathology, Hospital of the University of Pennsylvania, PA 19104, USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Feldman
authors.affiliation: Department of Pathology, Hospital of the University of Pennsylvania, PA 19104, USA.
authors.forename: Natalie Nc
authors.initials: NN
authors.lastname: Shih
authors.affiliation: Institute for Pathology, University Hospitals Case Medical Center, Case Western Reserve University, OH 44106-7207, USA.
authors.forename: Hannah
authors.initials: H
authors.lastname: Gilmore
authors.affiliation: Department of Biomedical Engineering, Case Western Reserve University, OH 44106, USA.
authors.forename: Anant
authors.initials: A
authors.lastname: Madabhushi
mesh_terms: D000465:Algorithms; D001940:Breast / Q000473:pathology*; D003116:Color; D004396:Coloring Agents; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D015233:Models, Statistical
chemical_list: D004396:Coloring Agents
keywords: Breast histology; Color unmixing; Hematoxylin and Eosin image; Immunohistochemistry image