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Proceedings Paper

An automated image segmentation and classification algorithm for immunohistochemically stained tumor cell nuclei
Author(s): Hangu Yeo; Vadim Sheinin; Yuri Sheinin
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Paper Abstract

As medical image data sets are digitized and the number of data sets is increasing exponentially, there is a need for automated image processing and analysis technique. Most medical imaging methods require human visual inspection and manual measurement which are labor intensive and often produce inconsistent results. In this paper, we propose an automated image segmentation and classification method that identifies tumor cell nuclei in medical images and classifies these nuclei into two categories, stained and unstained tumor cell nuclei. The proposed method segments and labels individual tumor cell nuclei, separates nuclei clusters, and produces stained and unstained tumor cell nuclei counts. The representative fields of view have been chosen by a pathologist from a known diagnosis (clear cell renal cell carcinoma), and the automated results are compared with the hand-counted results by a pathologist.

Paper Details

Date Published: 27 March 2009
PDF: 8 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 725948 (27 March 2009); doi: 10.1117/12.811185
Show Author Affiliations
Hangu Yeo, IBM Thomas J. Watson Research Ctr. (United States)
Vadim Sheinin, IBM Thomas J. Watson Research Ctr. (United States)
Yuri Sheinin, Mayo Clinic and Foundation (United States)


Published in SPIE Proceedings Vol. 7259:
Medical Imaging 2009: Image Processing
Josien P. W. Pluim; Benoit M. Dawant, Editor(s)

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