https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=2007Sandberg_SegmentationReview 2026-05-24T19:51:44Z Revision history for this page on the wiki MediaWiki 1.44.2 https://3demmethods.i2pc.es/index.php?title=2007Sandberg_SegmentationReview&diff=1942&oldid=prev 2009-08-06T09:53:28Z

<p></p> <p><b>New page</b></p><div>== Citation ==<br /> Sandberg K. Methods for image segmentation in cellular tomography. Methods Cell Biol. 2007;79:769-98<br /> <br /> == Abstract ==<br /> In this chapter, we discuss and illustrate some of the challenges for constructing robust and efficient segmentation methods for cellular tomography. We also discuss some approaches for evaluating the usability of segmentation methods and suggest a possible approach for quantifying the accuracy of segmentation algorithms. The chapter includes a review of several existing methods for denoising, contrast enhancement, and segmentation. We point out that some frequently suggested methods are often impractical for real data sets due to the computational complexity and difficulty in finding appropriate algorithm parameters. Furthermore, we claim that segmentation methods based only on intensity and contrast have problems in detecting thin, elongated structures. Instead, we propose using geometrical information, such as correlation among orientations, in order to detect membranes and microtubules. Finally, we outline a new orientation-based segmentation method and demonstrate this method on real tomograms.<br /> <br /> == Keywords ==<br /> <br /> == Links ==<br /> Article: http://dx.doi.org/10.1016/S0091-679X(06)79030-6 <br /> <br /> == Related software ==<br /> <br /> == Related methods ==<br /> <br /> == Comments ==</div>

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