An automated image segmentation and classification algorithm for immunohistochemically stained tumor cell nuclei

Hangu Yeo; Vadim Sheinin; Yuri Sheinin

doi:10.1117/12.811185

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27 March 2009 An automated image segmentation and classification algorithm for immunohistochemically stained tumor cell nuclei

Hangu Yeo, Vadim Sheinin, Yuri Sheinin

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Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 725948 (2009) https://doi.org/10.1117/12.811185
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

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.

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Hangu Yeo, Vadim Sheinin, and Yuri Sheinin "An automated image segmentation and classification algorithm for immunohistochemically stained tumor cell nuclei", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 725948 (27 March 2009); https://doi.org/10.1117/12.811185

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