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Proceedings Paper

Mitotic cell recognition with hidden Markov models
Author(s): Greg M. Gallardo; Fuxing Yang; Fiorenza Ianzini; Michael Mackey; Milan Sonka
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Paper Abstract

This work describes a method for detecting mitotic cells in time-lapse microscopy images of live cells. The image sequences are from the Large Scale Digital Cell Analysis System (LSDCAS) at the University of Iowa. LSDCAS is an automated microscope system capable of monitoring 1000 microscope fields over time intervals of up to one month. Manual analysis of the image sequences can be extremely time consuming. This work is part of a larger project to automate the image sequence analysis. A three-step approach is used. In the first step, potential mitotic cells are located in the image sequences. In the second step, object border segmentation is performed with the watershed algorithm. Objects in adjacent frames are grouped into object sequences for classification. In the third step, the image sequences are converted to feature vector sequences. The feature vectors contain spatial and temporal information. Hidden Markov Models (HMMs) are used to classify the feature vector sequences into dead cells, cell edges, and dividing cells. Discrete and continuous HMMs were trained on 500 sequences. The discrete HMM recognition rates were 62% for dead cells, 77% for cell edges, and 75% for dividing cells. The continuous HMM results were 68%, 88% and 77%.

Paper Details

Date Published: 5 May 2004
PDF: 8 pages
Proc. SPIE 5367, Medical Imaging 2004: Visualization, Image-Guided Procedures, and Display, (5 May 2004); doi: 10.1117/12.535778
Show Author Affiliations
Greg M. Gallardo, Univ. of Iowa (United States)
Fuxing Yang, Univ. of Iowa (United States)
Fiorenza Ianzini, Univ. of Iowa (United States)
Michael Mackey, Univ. of Iowa (United States)
Milan Sonka, Univ. of Iowa (United States)


Published in SPIE Proceedings Vol. 5367:
Medical Imaging 2004: Visualization, Image-Guided Procedures, and Display
Robert L. Galloway, Editor(s)

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