Region-based progressive localization of cell nuclei in microscopic images with data adaptive modeling

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dc.contributor.authorSong, Yangen
dc.contributor.authorCai, Weidongen
dc.contributor.authorHuang, Hengen
dc.contributor.authorWang, Yueen
dc.contributor.authorFeng, David D.en
dc.contributor.authorChen, Meien
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2013-07-10T10:37:45Zen
dc.date.available2013-07-10T10:37:45Zen
dc.date.issued2013-06-02en
dc.date.updated2013-07-10T10:37:45Zen
dc.description.abstractBackground Segmenting cell nuclei in microscopic images has become one of the most important routines in modern biological applications. With the vast amount of data, automatic localization, i.e. detection and segmentation, of cell nuclei is highly desirable compared to time-consuming manual processes. However, automated segmentation is challenging due to large intensity inhomogeneities in the cell nuclei and the background. Results We present a new method for automated progressive localization of cell nuclei using data-adaptive models that can better handle the inhomogeneity problem. We perform localization in a three-stage approach: first identify all interest regions with contrast-enhanced salient region detection, then process the clusters to identify true cell nuclei with probability estimation via feature-distance profiles of reference regions, and finally refine the contours of detected regions with regional contrast-based graphical model. The proposed region-based progressive localization (RPL) method is evaluated on three different datasets, with the first two containing grayscale images, and the third one comprising of color images with cytoplasm in addition to cell nuclei. We demonstrate performance improvement over the state-of-the-art. For example, compared to the second best approach, on the first dataset, our method achieves 2.8 and 3.7 reduction in Hausdorff distance and false negatives; on the second dataset that has larger intensity inhomogeneity, our method achieves 5% increase in Dice coefficient and Rand index; on the third dataset, our method achieves 4% increase in object-level accuracy. Conclusions To tackle the intensity inhomogeneities in cell nuclei and background, a region-based progressive localization method is proposed for cell nuclei localization in fluorescence microscopy images. The RPL method is demonstrated highly effective on three different public datasets, with on average 3.5% and 7% improvement of region- and contour-based segmentation performance over the state-of-the-art.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationBMC Bioinformatics. 2013 Jun 02;14(1):173en
dc.identifier.doihttps://doi.org/10.1186/1471-2105-14-173en
dc.identifier.urihttp://hdl.handle.net/10919/23296en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.holderYang Song et al.; licensee BioMed Central Ltd.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleRegion-based progressive localization of cell nuclei in microscopic images with data adaptive modelingen
dc.title.serialBMC Bioinformaticsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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