DocumentCode
77909
Title
Automated Histology Analysis: Opportunities for signal processing
Author
McCann, M.T. ; Ozolek, J.A. ; Castro, C.A. ; Parvin, B. ; Kovacevic, J.
Author_Institution
Biomed. Eng., Carnegie Mellon Univ., Pittsburgh, PA, USA
Volume
32
Issue
1
fYear
2015
fDate
Jan. 2015
Firstpage
78
Lastpage
87
Abstract
Histology is the microscopic inspection of plant or animal tissue. It is a critical component in diagnostic medicine and a tool for studying the pathogenesis and biology of processes such as cancer and embryogenesis. Tissue processing for histology has become increasingly automated, drastically increasing the speed at which histology labs can produce tissue slides for viewing. Another trend is the digitization of these slides, allowing them to be viewed on a computer rather than through a microscope. Despite these changes, much of the routine analysis of tissue sections remains a painstaking, manual task that can only be completed by highly trained pathologists at a high cost per hour. There is, therefore, a niche for image analysis methods that can automate some aspects of this analysis. These methods could also automate tasks that are prohibitively time-consuming for humans, e.g., discovering new disease markers from hundreds of whole-slide images (WSIs) or precisely quantifying tissues within a tumor.
Keywords
cancer; medical image processing; tissue engineering; tumours; animal tissue; automated histology analysis; cancer; diagnostic medicine; disease markers; embryogenesis; image analysis method; microscopic inspection; pathogenesis; plant tissue; signal processing; tissue processing; tissue sections; tumor; Biological tissues; Biomedical imaging; Biomedical signal processing; Biopsy; Histology; Image analysis; Image color analysis; Microscopy; Signal processing; Visualization;
fLanguage
English
Journal_Title
Signal Processing Magazine, IEEE
Publisher
ieee
ISSN
1053-5888
Type
jour
DOI
10.1109/MSP.2014.2346443
Filename
6975290
Link To Document