A Novel Approach for Medical Images Noise Reduction Using Neural Filter

Mohammed Debakla (1) , Khalifa Djemal (2) , Mohamed Benyettou (3)

1 LRSBG
2 IBISC - Informatique, Biologie Intégrative et Systèmes Complexes
3 MOSIM

This paper is dedicated to the presentation of a new denoising method for medical images. In the proposed approach, a neural filter is designed based on total variation regularization using a multilayer neural network (MLP) to reduce the noise from the degraded images. To train the network we use optimization techniques, which require the calculation of the gradient of the error to adjust the weights by the minimization of an appropriate error function. The new approach is based on taking profit from local information (neighborhood) of the pixel to denoise it. The proposed method can restore degraded images and preserves the discontinuities. The considered filter was used to reduce noise from X-ray and MRI medical images giving good results when compared to other approaches.

Mots clés: noise reduction; medical images; total variation; neural network; pixel neighborhood

Domaines: Traitement du signal et de l'image [eess.SP]

Mohammed Debakla, Khalifa Djemal, Mohamed Benyettou. A Novel Approach for Medical Images Noise Reduction Using Neural Filter. Global Journal on Technology, 2015, 8, pp.267--275. ⟨hal-01778012⟩