Feature Extraction of 3T3 Fibroblast Microtubule Based on Discrete Wavelet Transform and Lucy–Richardson Deconvolution Methods

Abstract

Accompanied by the increasing requirements of the probing micro/nanoscopic structures of biological samples, various image-processing algorithms have been developed for visualization or to facilitate data analysis. However, it remains challenging to enhance both the signal-to-noise ratio and image resolution using a single algorithm. In this investigation, we propose a composite image processing method by combining discrete wavelet transform (DWT) and the Lucy–Richardson (LR) deconvolution method, termed the DWDC method. Our results demonstrate that the signal-to-noise ratio and resolution of live cells’ microtubule networks are considerably improved, allowing the recognition of features as small as 120 nm. The method shows robustness in processing the high-noise images of filament-like biological structures, e.g., the cytoskeleton networks captured by fluorescent microscopes.

Editorial for the Special Issue on Optics and Photonics in Micromachines

“…In this Special Issue, we are glad to collect 15 research articles covering a broad area, including optical field modulation [ 11 ], laser fabrication techniques [ 12 , 13 ], optical measurement [ 14 , 15 , 16 , 17 , 18 ], on-chip photonic devices [ 19 , 20 , 21 , 22 ], super-resolution imaging [ 23 , 24 ], and related theoretical [ 25 ] investigations.…”

“…There are another two research articles related directly to super-resolution imaging. One used the traditional method [ 23 ] by combining Lucy–Richardson deconvolution and discrete wavelet methods, and the other one used an A-net deep learning network [ 24 ]. Both show apparent improvement in the spatial resolution and SNR of biological images.…”