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Proceedings Paper

Segmentation of 2D gel electrophoresis spots using a Markov random field
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Paper Abstract

We propose a statistical model-based approach for the segmentation of fragments of DNA as a first step in the automation of the primarily manual process of comparing two or more images resulting from the Restriction Landmark Genomic Scanning (RLGS) method. These 2D gel electrophoresis images are the product of the separation of DNA into fragments that appear as spots on X-ray films. The goal is to find instances where a spot appears in one image and not in another since a missing spot can be correlated with a region of DNA that has been affected by a disease such as cancer. The entire comparison process is typically done manually, which is tedious and very error prone. We pose the problem as the labeling of each image pixel as either a spot or non-spot and use a Markov Random Field (MRF) model and simulated annealing for inference. Neighboring spot labels are then connected to form spot regions. The MRF based model was tested on actual 2D gel electrophoresis images.

Paper Details

Date Published: 27 March 2009
PDF: 6 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72594O (27 March 2009); doi: 10.1117/12.811802
Show Author Affiliations
Christopher S. Hoeflich, Univ. at Buffalo (United States)
Jason J. Corso, Univ. at Buffalo (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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