Authors : Shant Kaushik and Surender Jangra
Abstract: Medicinal imaging innovation is turning into an imperative segment of expansive quantities of utilizations nowaday. Different medical images (X-ray, CT scan, MRI, ultrasound and echocardiography, etc.) have minute data about heart, nerves and cerebrums which are be more exact and free from twisting or commotion. Noise reduction has emerged as a significant area of research in recent past. Different image enhancement techniques and approaches are developed in the literature based on LDA, NN, wavelets and filtering, etc. In spite of the fact that these sorts of techniques created better results yet have a large scope in enhancing image quality through noise reduction. In this study, a hybrid approach is developed using neural Network (NN), Gabor filter and MDA for enhancing the quality of medical images. First, Gabor filter is apply on the image then neural network are used as the learning calculation which takes after the managed learning after that Gabor filter is characterized for its viability in edge-safeguarded image de-noising. Further, MDA are applied on the processed image and final results are evaluated using PSNR, MSE, mean SSIM, etc. and produces better results comparative to previous one. This approach helps in decision making for diagnosis of different critical diseases.
Shant Kaushik and Surender Jangra, 2018. A Hybrid Medical Image De-Noising Approach Using Gabor, NN and MDA. Journal of Engineering and Applied Sciences, 13: 1365-1370.