Optimization of Curvi-Linear Tracing Applied to Solar Physics and Biophysics

TL;DR

This paper presents an advanced automated pattern recognition algorithm, OCCULT-2, capable of extracting curvi-linear features from diverse 2D images across solar physics and biophysics, demonstrating high universality and accuracy.

Contribution

The paper introduces an improved version of the OCCULT code for automated curvi-linear feature detection, applicable across multiple scientific fields and image types.

Findings

The algorithm successfully traces structures across size scales up to 16 orders of magnitude.

Comparison shows high agreement between automated and manual tracing methods.

The method is versatile, applicable to solar images and biological microscopy data.

Abstract

We developed an automated pattern recognition code that is particularly well suited to extract one-dimensional curvi-linear features from two-dimensional digital images. A former version of this {\sl Oriented Coronal CUrved Loop Tracing (OCCULT)} code was applied to spacecraft images of magnetic loops in the solar corona, recorded with the NASA spacecraft {\sl Transition Region And Coronal Explorer (TRACE)} in extreme ultra-violet wavelengths. Here we apply an advanced version of this code ({\sl OCCULT-2}) also to similar images from the {\sl Solar Dynamics Observatory (SDO)}, to chromospheric H-$\alpha$ images obtained with the {\sl Swedish Solar Telescope (SST)}, and to microscopy images of microtubule filaments in live cells in biophysics. We provide a full analytical description of the code, optimize the control parameters, and compare the automated tracing with visual/manual methods. The traced structures differ by up to 16 orders of magnitude in size, which demonstrates the universality of the tracing algorithm.