Selection of Optimal Denoising Filter Using Quality Assessment for Potentially Lethal Optical Wound Images

The objective of this paper is to select the best filter for the camera-captured digital wound image pre-processing. Wound images give the most essential information about the wound. The information may be size, status of the wound, tissue composition and rate of healing. These images are most often corrupted by impulse or random noises while capturing them. Corrupted images often suffer from sharpness, chrominance and luminance. Application of several filtering schemes such as linear and non-linear filtering suppresses noise and improves the image quality. In this paper, a comparative study of five filters has been performed using mathematical morphology operations for removing the impulse/random noise. These five filters were applied on seventy-five randomly selected wound images from the developed image database as well as online chronic wound image database. In order to assess the quality of the filtered image, seven quality measures have been applied. Local first order statistics (LFOS) is the best and efficient filter in the context of reduced mean square error (MSE) and high peak signal to noise ratio (PSNR) between the reference original and the filtered image.