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Hyperspectral imaging is an optical technique that recently started being used in medical field. The correct extraction of spectral and spatial information from hyperspectral images depends on preprocessing, processing and analysis methods applied for an accurate diagnosis and monitoring medical treatments. A fundamental task in preprocessing hyperspectral images is the elimination of various types of noise generated by the hyperspectral systems. One of the major causes for the noise in a hyperspectral system is dark current noise. This type of noise arises from the temperature difference between environment and charge-coupled device of the hyperspectral camera. Electrons are generated over time and they are independent of the light falling on the detector. These electrons are captured by the potential wells of the charge-coupled device and counted as signal. The dark current noise removal can lead to an improvement in the performance of classification, target detection, anomaly detection and mapping methods, thus contributing to a better and more accurate diagnosis. Two denoising techniques - principal component analysis and minimum noise fractions were used until now in medical hyperspectral imaging applications. In this paper, the wavelet transform was proposed as a denoising technique for medical applications. The study was performed in both laboratory and clinical conditions. Two hyperspectral systems were used for the hyperspectral images acquisition of rabbit liver and a burn wound located on the posterior side of the patient left leg respectively using the same pushbroom hyperspectral camera but with two different scanning components (translation table and scanning mirror). The pushbroom hyperspectral camera acquires the image collecting the x-axis and λ information completely at the same time for a line on the y-axis. The two scanning components are used to move the sample (liver or patient leg) across the field of view of the hyperspectral camera so that the images are acquired line by line. The experimental results showed that the proposed denoising technique achieves better performance when applied to hyperspectral images acquired under laboratory conditions than in clinical situations. In conclusion, the wavelet transform could be considered a successful approach to denoising in laboratory hyperspectral measurements.
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