A biosegmentation benchmark for evaluation of bioimage analysis methods

Elisa Drelie Gelasca*, Boguslaw Obara, Dmitry Fedorov, Kristian Kvilekval and BS Manjunath
Address: Center for Bio-Image Informatics, Electrical and Computer Engineering Department, University of California Santa Barbara (UCSB), CA 93106, USA
Email: Elisa Drelie Gelasca* - elisa.drelie [at] ieee.org; Boguslaw Obara - obara [at] ece.ucsb.edu; Dmitry Fedorov - fedorov [at] ece.ucsb.edu; Kristian Kvilekval - kris [at] cs.ucsb.edu; BS Manjunath - manj [at] ece.ucsb.edu
* Corresponding author
Published: 1 November 2009 Received: 26 November 2008
Accepted: 1 November 2009
BMC Bioinformatics 2009, 10:368 doi:10.1186/1471-2105-10-368 This article is available from: http://www.biomedcentral.com/1471-2105/10/368
� 2009 Drelie Gelasca et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background: We present a biosegmentation benchmark that includes infrastructure, datasets with associated ground truth, and validation methods for biological image analysis. The primary motivation for creating this resource comes from the fact that it is very difficult, if not impossible, for an end-user to choose from a wide range of segmentation methods available in the literature for a particular bioimaging problem. No single algorithm is likely to be equally effective on diverse set of images and each method has its own strengths and limitations. We hope that our benchmark resource would be of considerable help to both the bioimaging researchers looking for novel image processing methods and image processing researchers exploring application of their methods to biology. Results: Our benchmark consists of different classes of images and ground truth data, ranging in scale from subcellular, cellular to tissue level, each of which pose their own set of challenges to image analysis. The associated ground truth data can be used to evaluate the effectiveness of different methods, to improve methods and to compare results. Standard evaluation methods and some analysis tools are integrated into a database framework that is available online at http:// bioimage.ucsb.edu/biosegmentation/. Conclusion: This online benchmark will facilitate integration and comparison of image analysis methods for bioimages. While the primary focus is on biological images, we believe that the dataset and infrastructure will be of interest to researchers and developers working with biological image analysis, image segmentation and object tracking in general.
[PDF] [BibTex]
Elisa Drelie Gelasca, Boguslaw Obara, Dmitry Fedorov, Kristian Kvilekval and B. S. Manjunath,
BMC Bioinformatics, vol. 10, no. 1, pp. 368, Nov. 2009.
Node ID: 539 , DB ID: 347 , Lab: VRL , Target: Journal
Subject: [Image and Video Segmentation] « Look up more