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Updated: Jul 19, 2026

Detection and Quantification of Tunneling Nanotubes Using 3D Volume View Images
Published on: August 31, 2022
Three-dimensional nonlinear invisible boundary detection
Maria Petrou1, Vassili A Kovalev, Jürgen R Reichenbach
1Electrical and Electronics Engineering Department, Imperial College, London, UK. maria.petrou@imperial.ac.uk
Abstract:
The human vision system can discriminate regions which differ up to the second-order statistics only. We present an algorithm designed to reveal "hidden" boundaries in gray level images, by computing gradients in higher order statistics of the data. We demonstrate it by applying it to the identification of possible "hidden" boundaries of glioblastomas as manifest themselves in three-dimensional (3-D) MRI scans, using a model driven approach. We also demonstrate the method using a nonmodel driven approach where we have no prior information about the location of possible boundaries. In this case, we use 3-D MRI data concerning schizophrenic patients and normal controls.
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