Adaptive Filtering Technique for Chronic Wound Analysis under Tele-Wound Network
Chinmay Chakraborty, Bharat Gupta · Journal of CONASENSE · 2016
Efficient diagnosis of chronic wound depends on the quality of digital image that has reached the Tele-Medical Hub through Tele-Wound Network.However, how much ever precaution we take, while capturing the image by digital camera or by smartphone, presence of random/impulse noise would be always there to corrupt the captured image.The wound images give the vital information such as size, wound status, tissue composition and healing rate.In this paper, we have proposed adaptive filtering technique for chronic wound (CW) image analysis under Tele-wound network to improve the diagnosis.Here, the best filter has been chosen which can help to improve the diagnosis of wound.A comparative study of 16 different filters has also been performed on 72 different wound images.The experimental results are given by comparing 8 different parameters.These various parameters are Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Signal to Noise Ratio (SNR), Negative Absolute Error (NAE), Maximum Difference (MD), Mean Structural Similarity Index (MSSIM), Universal Image Quality Index (UIQI) and Mean Absolute Error (MAE).Simulated results shows adaptive median provides better performances with respect to high value of PSNR (66.23),SNR (58.05) and lower value of MSE (3.01), MAE (0.29), and NAE (0.01) between original and filtered image.The proposed methodology will assist the clinicians to take better decision towards diagnosis of CW in terms of qualitative at low-resource setup.