Development of an automatic quantification method for cancer tissue microarray study

• Published in: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2009; pp. 3665 - 3668
• Main Authors: Sanders, T.H., Stokes, T.H., Moffitt, R.A., Chaudry, Q., Parry, R.M., Wang, M.D.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.01.2009
• Subjects: Algorithms; Automatic Data Processing - methods; Automation; Biological tissues; Biomedical engineering; Cancer; Cell Nucleus - metabolism; Coloring Agents - chemistry; Gene Expression Profiling; Guidelines; Head and Neck Neoplasms - diagnosis; Head and Neck Neoplasms - metabolism; Humans; Image analysis; Image databases; Immunohistochemistry - methods; Male; Neoplasms - diagnosis; Neoplasms - metabolism; Pattern recognition; Pixel; Prostatic Neoplasms - diagnosis; Prostatic Neoplasms - metabolism; Reproducibility of Results; Staining and Labeling - methods; Tissue Array Analysis - instrumentation; Tissue Array Analysis - methods; USA Councils
• ISSN: 1094-687X; 1557-170X; 1558-4615
• DOI: 10.1109/IEMBS.2009.5334528

Clinical histopathology is based on the analysis of immunohistochemistry (IHC) stained tissue images. Selection of antibodies for detecting the presence, type, and grade of cancerous tissue has a great influence on the diagnostic potential of IHC tests. Automated evaluation methods for tissue microarrays applied to many combinations of antibody and tissue type can speed development of new clinical assays. We present an automatic method that successfully quantifies stain intensity, fraction of cells stained and sub-cellular location of staining in tissue microarray images. The method combines an opponent color preprocessor and a novel statistical approach for identifying brown and blue staining, followed by multilevel morphological processing. We verify the capability of our method by comparing the results to manually annotated image databases. We also demonstrate cross-tissue robustness using two clinical case study data.