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Open Access Highly Accessed Methodology article

Extended morphological processing: a practical method for automatic spot detection of biological markers from microscopic images

Yoshitaka Kimori123, Norio Baba4 and Nobuhiro Morone25*

Author Affiliations

1 Japan Association for the Advancement of Medical Equipment, Hongo 3-42-6, Bunkyo-ku, Tokyo, 113-0033, Japan

2 Department of Ultrastructural Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Ogawahigashi-cho 4-1-1, Kodaira, Tokyo, 187-8502, Japan

3 Center for Novel Science Initiatives, National Institutes of Natural Sciences, Toranomon 4-3-13, Minato-ku, Tokyo, 105-0001, Japan

4 Faculty of informatics, Kogakuin University, Nishi-shinjuku 1-24-2, Shinjuku-ku, Tokyo, 163-8677, Japan

5 Institute for Integrated Cell-Material Sciences (iCeMS), Kyoto University, Yoshidahonmachi Sakyo-ku, Kyoto, 606-8085, Japan

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BMC Bioinformatics 2010, 11:373  doi:10.1186/1471-2105-11-373

Published: 8 July 2010



A reliable extraction technique for resolving multiple spots in light or electron microscopic images is essential in investigations of the spatial distribution and dynamics of specific proteins inside cells and tissues. Currently, automatic spot extraction and characterization in complex microscopic images poses many challenges to conventional image processing methods.


A new method to extract closely located, small target spots from biological images is proposed. This method starts with a simple but practical operation based on the extended morphological top-hat transformation to subtract an uneven background. The core of our novel approach is the following: first, the original image is rotated in an arbitrary direction and each rotated image is opened with a single straight line-segment structuring element. Second, the opened images are unified and then subtracted from the original image. To evaluate these procedures, model images of simulated spots with closely located targets were created and the efficacy of our method was compared to that of conventional morphological filtering methods. The results showed the better performance of our method. The spots of real microscope images can be quantified to confirm that the method is applicable in a given practice.


Our method achieved effective spot extraction under various image conditions, including aggregated target spots, poor signal-to-noise ratio, and large variations in the background intensity. Furthermore, it has no restrictions with respect to the shape of the extracted spots. The features of our method allow its broad application in biological and biomedical image information analysis.