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Med Biol Eng Comput. 2008 Sep;46(9):943-7. doi: 10.1007/s11517-008-0380-5. Epub 2008 Jul 31.

IICBU 2008: a proposed benchmark suite for biological image analysis.

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Laboratory of Genetics, Image Informatics and Computational Biology Unit, NIA/NIH, 333 Cassell Dr, Baltimore, MD 21224, USA.


New technology for automated biological image acquisition has introduced the need for effective biological image analysis methods. These algorithms are constantly being developed by pattern recognition and machine vision experts, who tailor general computer vision techniques to the specific needs of biological imaging. However, computer scientists do not always have access to biological image datasets that can be used for computer vision research, and biologist collaborators who can assist in defining the biological questions are not always available. Here, we propose a publicly available benchmark suite of biological image datasets that can be used by machine vision experts for developing and evaluating biological image analysis methods. The suite represents a set of practical real-life imaging problems in biology, and offers examples of organelles, cells and tissues, imaged at different magnifications and different contrast techniques. All datasets are available for free download at .

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