Comparative analysis of denoising techniques in burn depth discrimination from burn hyperspectral images

This study analyzes and compares the performance of five denoising techniques (Lee filter, gamma filter, principal component analysis, maximum noise fraction, and wavelet transform) in order to identify the most appropriate one that lead to the most accurate classification of burned tissue in hyperspectral images. Fifteen hyperspectral images of burned patients were acquired and denoising techniques were applied to each image. Spectral angle mapper classifier was used for data classification and the confusion matrix was used for quantitative evaluation of the performances of the denoising methods. The results revealed that gamma filter performed better than other denoising techniques with values of overall accuracy and kappa coefficient of 91.18% and 0.8958 respectively. The lowest performance was detected for principal component analysis. In conclusion, the gamma filter could be considered an optimal choice for noise reduction in burn hyperspectral images and could be used for a more accurate diagnosis of burn depth.