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

Hangu Yeo, Vadim Sheinin, Yuri Sheinin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2009 - Image Processing
DOIs
StatePublished - 2009
EventMedical Imaging 2009 - Image Processing - Lake Buena Vista, FL, United States
Duration: Feb 8 2009Feb 10 2009

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume7259
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2009 - Image Processing
CountryUnited States
CityLake Buena Vista, FL
Period2/8/092/10/09

Keywords

  • Immunohistochemistry
  • Medical image segmentation and analysis and digital pathology

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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