Hyperspectral imaging (HSI) is increasingly gaining acceptance in the medical field.
Up until now, HSI has been used in conjunction with rigid endoscopy to detect cancer in vivo.
The logical next step is to pair HSI with flexible endoscopy, since it improves access to hard-to-reach
areas. While the flexible endoscope’s fiber optic cables provide the advantage of flexibility, they
also introduce an interfering honeycomb-like pattern onto images. Due to the substantial impact
this pattern has on locating cancerous tissue, it must be removed before the HS data can be further
processed. Thereby, the loss of information is to minimize avoiding the suppression of small-area
variations of pixel values. We have developed a system that uses flexible endoscopy to record HS
cubes of the larynx and designed a special filtering technique to remove the honeycomb-like pattern
with minimal loss of information. We have confirmed its feasibility by comparing it to conventional
filtering techniques using an objective metric and by applying unsupervised and supervised
classifications to raw and pre-processed HS cubes. Compared to conventional techniques, our
method successfully removes the honeycomb-like pattern and considerably improves classification
performance, while preserving image details.