Abstract:
Hyperspectral imaging (HSI) is a technology with high
potential in the field of non-invasive detection of cancer.
However, in complex imaging situations like HSI of the
larynx with a rigid endoscope, various image interferences
can disable a proper classification of cancerous tissue. We
identified three main problems: i) misregistration of single
images in a HS cube due to patient heartbeat ii) image
noise and iii) specular reflections (SR). Consequently, an
image pre-processor is developed in the current paper to
overcome these image interferences. It encompasses i)
image registration ii) noise removal by minimum noise
fraction (MNF) transformation and iii) a novel SR detection method. The results reveal that the pre-processor improves classification performance, while the newly developed SR detection method outperforms global thresholding technique hitherto used by 46%. The novel pre-processor will be used for future studies towards the development of an operational scheme for HS-based larynx cancer detection.
