Automatic Adaptive Enhancement for Images Obtained With Fiberscopic Endoscopes

Abstract

Modern techniques for medical diagnostics and therapy in keyhole surgery scenarios as well as technical inspection make use of flexible endoscopes. Their characteristic bendable image conductor consists of a very limited number of coated fibers, which leads to so-called comb structure. This effect has a negative impact on further image processing steps such as feature tracking because these overlaid image structures are wrongly detected as image features. With respect to these tasks, we propose an automatic approach to generate optimal spectral filter masks for enhancement of fiberscopic images. We apply the Nyquist-Shannon sampling theorem to the spectrum of fiberscopically acquired images to obtain parameters for optimal filter mask calculation. This can be done automatically and independently of scale and resolution of the image conductor as well as type and resolution of the image sensor. We designed and verified simple rotation invariant masks as well as star-shaped rotation variant masks that contain information about orientation between the fiberscope and sensor. A subjective survey among experts between different modes of filtering certified the best results to the adapted star-shaped mask for high-quality glass fiberscopes. We furthermore define an objective metric to evaluate the results of different filter approaches, which verifies the results of the subjective survey. The proposed approach enables the automated reduction of fiberscopic comb structure. It is adaptive to arbitrary endoscope and sensor combinations. The results give the prospect of a large field of possible applications to reduce fiberscopic structure both for visual optimization in clinical environments and for further digital imaging tasks.

Automated Multiscale Morphometry of Muscle Disease From Second Harmonic Generation Microscopy Using Tensor-Based Image Processing

“…MAGING techniques in combination with image-processing strategies have become indispensable in biomedical research to elucidate biomolecule structures and function [1], [2], or in medical diagnostics to track disease states, progression, or remission [3], [4]. Common far-field imaging techniques, e.g., magnetic resonance imaging, computed tomography, or ultrasound, have the advantage of being noninvasive, yet suffer from restricted spatial resolution.…”

Automatic Quantification of Vocal Cord Paralysis - An Application of Fibre-optic Endoscopy Video Processing

“…This artefact is produced due to the sub-sampling of the scene by the amount of glass fibres present in the fibre-optic bundle. It was eliminated by spectral filtering using a star shaped band stop filter (Winter et al, 2006). The filtered image was smoothed with a wiener filter.…”

Multicolor probe-based confocal laser endomicroscopy: a new world for in vivo and real-time cellular imaging

“…As similar problems arise in the single spectral band case, algorithms that take on the image reconstruction task in real time have already been developed at Mauna Kea Technologies to provide users with high-quality, smooth-motion video sequences in the single band case. 14,29 Most of the available methods from other groups [30][31][32] are only focused on the removal of the honeycomb pattern and often only imply performing a simple low-pass filtering. The approach of Mauna Kea Technologies for the single spectral band case not only removes the honeycomb pattern but also recovers the true signal that comes back from the tissue and compensates for the geometric distortions.…”