Unsupervised segmentation of cell nuclei using geometric models

Fitch, Shaun; Jackson, Trevor; Andras, Peter; Robson, Craig

Abstract

Fluorescent microscopy of biological samples allows non-invasive screening of specific molecular events in-situ. This approach is useful for investigating intricate signalling pathways and in the drug discovery process. The large volumes of data involved in image analysis are a limiting factor. As manual image interpretation relies on expensive manpower automated analysis is a far more appropriate solution. In this paper we discuss our approach to achieve reliable automated segmentation of individual cell nuclei from wide field images taken of prostate cancer cells. We present a novel analysis routine to accurately identify cell nuclei based upon intensity clustering and morphological validation using a data derived geometric model. This approach is shown to consistently outperform the standard analysis technique using real data.

Citation

Fitch, S., Jackson, T., Andras, P., & Robson, C. (2008, May). Unsupervised segmentation of cell nuclei using geometric models. Presented at 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Paris, France

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro
• Start Date: May 14, 2008
• End Date: May 17, 2008
• Online Publication Date: Jun 13, 2008
• Publication Date: 2008
• Deposit Date: Nov 17, 2021
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 728-731
• Series ISSN: 1945-7928
• Book Title: 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro
• ISBN: 978-1-4244-2002-5
• DOI: https://doi.org/10.1109/ISBI.2008.4541099
• Keywords: Model-based segmentation, Fluorescence, Microscopy, Screening
• Public URL: http://researchrepository.napier.ac.uk/Output/2809224