Development of an image pre‐processor for operational hyperspectral laryngeal cancer detection. Issue 3 (1st June 2015)

• Record Type: Journal Article
• Authors: Regeling, Bianca; Laffers, Wiebke; Gerstner, Andreas O. H.; Westermann, Stephan; Müller, Nina A.; Schmidt, Kai; Bendix, Jörg; Thies, Boris
• Is Part Of: Journal of biophotonics. Volume 9:Issue 3(2016)
• Page Start: 235
• Page End: 245
• Publication Date: 2015-06-01
• Subjects: hyperspectral imaging -- specular reflection detection -- endoscopy -- in‐vivo -- larynx -- image processing; Photonics -- Periodicals; Optical materials -- Periodicals; Optics -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3605
• DOI: 10.1002/jbio.201500151
• Languages: English
• ISSNs: 1864-063X

Abstract

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.RGB image of the larynx derived from the hyperspectral cube and corresponding specular reflections (a ) manually segmented and (b ) detected by a novel specular reflection detection method. Abstract : Towards an operational hyperspectral based scheme for in‐vivo detection of laryngeal cancer a newly developed image pre‐processor is introduced. The pre‐processor encompasses image registration, image noise removal and a novel specular reflection detection method. Classification results reveal that the pre‐processor improves classification performance, while the newly developed specular reflection detection method outperforms global thresholding techniques.