A Novel Approach for Medical Images Noise Reduction Based RBF Neural Network Filter

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

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

This paper is dedicated to the presentation of a Radial basis function neural network (RBFNN) based denoising method for medical images. In the proposed approach, a RBFNN filter is designed where the output of the network is a single denoised pixel and the inputs are its neighborhood in the degraded image. The back-propagation algorithm is used to train the RBFNN filter by minimizing an appropriate error function obtained from the total variation model. The parameters to be adjusted are the weights and the neurons centers of the RBFNN. The considered filter was used to reduce noise from X-ray, MRI and Mammographic medical images giving good results of noise removal when compared to other approaches and using different noise standard deviations.

Mots clés: Noise reduction; medical images; total variation; radial basic fonction neural network; pixel neighborhood

Domaines: Apprentissage [cs.LG]

Mohammed Debakla, Khalifa Djemal, Mohamed Benyettou. A Novel Approach for Medical Images Noise Reduction Based RBF Neural Network Filter. Journal of Computers (JCP), 2015, 10 (2), pp.68--80. ⟨hal-01277516⟩