DocumentCode :
1524735
Title :
Histopathological Image Analysis: A Review
Author :
Gurcan, M.N. ; Boucheron, L.E. ; Can, A. ; Madabhushi, A. ; Rajpoot, N.M. ; Yener, B.
Author_Institution :
Dept. of Biomed. Inf., Ohio State Univ., Columbus, OH, USA
Volume :
2
fYear :
2009
fDate :
7/1/1905 12:00:00 AM
Firstpage :
147
Lastpage :
171
Abstract :
Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe.
Keywords :
biological tissues; biomedical optical imaging; computer aided analysis; feature extraction; fluorescence; image colour analysis; image segmentation; medical image processing; reviews; CAD; auto-fluorescence compensation; color normalization; computer-assisted diagnosis; cytopathology; digitized tissue histopathology; feature extraction; feature selection; histopathology; image analysis; image preprocessing; image segmentation; imaging modalities; immunofluorescence; medical imaging; spectral imaging; Algorithm design and analysis; Application software; Biomedical imaging; Computer aided diagnosis; Computer applications; Coronary arteriosclerosis; Digital images; Image analysis; Machine learning; Machine learning algorithms; Computer-aided diagnosis; computer-assisted interpretation; digital pathology; histopathology; image analysis; microscopy analysis; Algorithms; Artificial Intelligence; Europe; Histocytochemistry; Humans; Image Interpretation, Computer-Assisted; Prognosis; United States;
fLanguage :
English
Journal_Title :
Biomedical Engineering, IEEE Reviews in
Publisher :
ieee
ISSN :
1937-3333
Type :
jour
DOI :
10.1109/RBME.2009.2034865
Filename :
5299287
Link To Document :
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