CVIVOct 4, 2020

3D Orientation Field Transform

arXiv:2010.01453v1
Originality Incremental advance
AI Analysis

This work addresses the problem of enhancing 3D curves in images for fields like microscopy, though it appears incremental as it extends an existing 2D method to 3D.

The authors tackled the lack of a 3D counterpart to the 2D orientation field transform for enhancing curves in images by generalizing it to 3D, demonstrating its effectiveness on transmission electron microscopy tomograms with varying modular combinations that enhance curves to different extents.

The two-dimensional (2D) orientation field transform has been proved to be effective at enhancing 2D contours and curves in images by means of top-down processing. It, however, has no counterpart in three-dimensional (3D) images due to the extremely complicated orientation in 3D compared to 2D. Practically and theoretically, the demand and interest in 3D can only be increasing. In this work, we modularise the concept and generalise it to 3D curves. Different modular combinations are found to enhance curves to different extents and with different sensitivity to the packing of the 3D curves. In principle, the proposed 3D orientation field transform can naturally tackle any dimensions. As a special case, it is also ideal for 2D images, owning simpler methodology compared to the previous 2D orientation field transform. The proposed method is demonstrated with several transmission electron microscopy tomograms ranging from 2D curve enhancement to, the more important and interesting, 3D ones.

Code Implementations1 repo
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