title: Histopathological image analysis: a review.
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.
authors.affiliation: Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA. metin.gurcan@osumc.edu
authors.forename: Metin N
authors.initials: MN
authors.lastname: Gurcan
authors.forename: Laura E
authors.initials: LE
authors.lastname: Boucheron
authors.forename: Ali
authors.initials: A
authors.lastname: Can
authors.forename: Anant
authors.initials: A
authors.lastname: Madabhushi
authors.forename: Nasir M
authors.initials: NM
authors.lastname: Rajpoot
authors.forename: B
authors.initials: B
authors.lastname: Yener
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D005060:Europe; D006651:Histocytochemistry; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D011379:Prognosis; D014481:United States