http://www.nitrc.org/forum/forum.php?forum_id=2964 <p class="abstract"><div class="Abstract" lang="en"><a name="Abs1"></a><span class="AbstractHeading">Abstract&nbsp;&nbsp;</span><div class="normal">Developments in image acquisition technology make high volumes of neuron images available to neuroscientists for analysis. However, manual processing of these images is not practical and is infeasible for larger and larger scale studies. Reliable interpretation and analysis of high volume data requires accurate quantitative measures. This requires analysis algorithms to use mathematical models that inherit the underlying geometry of biological structures in order to extract topological information. In this paper, we first introduce principal curves as a model for the underlying skeleton of axons and branches, then describe a recursive principal curve tracing&nbsp;(RPCT) method to extract this topology information from 3D microscopy imagery. RPCT first finds samples on the one dimensional principal set of the intensity function in space. Then, given an initial direction and location, the algorithm iteratively traces the principal curve in space using our principal curve tracing&nbsp;(PCT) method. Recursive implementation of PCT provides a compact solution for extracting complex tubular structures that exhibit bifurcations. </div> </div></p><ul> <li><span class="labelName">Content Type </span><span class="labelValue">Journal Article</span></li><li>Pages 181-191</li><li>DOI 10.1007/s12021-011-9105-2</li><li><span class="labelName">Authors</span><ul> <li>Erhan Bas, Cognitive Systems Laboratory, Northeastern University, 360 Huntington Ave., Boston, MA, USA</li><li>Deniz Erdogmus, Cognitive Systems Laboratory, Northeastern University, 360 Huntington Ave., Boston, MA, USA</li> </ul></li> </ul><ul class="parents"> <ul class="details"> <li><span class="header labelName">Journal </span><span class="labelValue"><a href="http://www.springerlink.com/content/120559/">Neuroinformatics</a></span></li><li><span class="labelName">Online ISSN </span><span class="labelValue">1559-0089</span></li><li><span class="labelName">Print ISSN </span><span class="labelValue">1539-2791</span></li> </ul><ul class="details"> <li><span class="header labelName">Journal Volume </span><span class="labelValue">Volume 9</span></li> </ul><ul class="details"> <li><span class="header labelName">Journal Issue </span><span class="labelValue"><a href="http://www.springerlink.com/content/q1w234205257/">Volume 9, Numbers 2-3</a></span></li> </ul> </ul> en-us Copyright 2000-2026 NITRC OSI Sun, 19 Apr 2026 19:07:01 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator