TY  - JOUR
T1  - A Hybrid Medical Image De-Noising Approach Using Gabor, NN and MDA
AU - Kaushik, Shant AU - Jangra, Surender 
JO  - Journal of Engineering and Applied Sciences
VL  - 13
IS  - 6
SP  - 1365
EP  - 1370
PY  - 2018
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2018.1365.1370
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2018.1365.1370
KW  - De-noising
KW  -medical images
KW  -NN
KW  -Gabor filter
KW  -MDA
KW  -diagnosis
AB  - 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.
ER  -