Computer-aided automatic approach for denoising of magnetic resonance images

Abstract

ABSTRACT This paper proposes a dual path deep convolution network based on discriminative learning for denoising MR images. The noise in MR images causes problems in identifying the regions of interest. The proposed approach is incorporated using depthwise separable convolution and local response normalisation in one path. Another path of the network is implemented using depthwise separable convolution and group normalisation. The proposed network was used to denoise the images of different noise levels, and it yields better performance as compared with various networks. The network produces clinically relevant results without retraining the network on other datasets as depicted by the evaluation metrics. The evaluation metrics improved remarkably in the results obtained by the proposed model. The additional external clinical validation was performed by the senior radiologists, and the results were found to be satisfactory for medical diagnosis. The statistical analysis of the results proves the suitability of the results for medical analysis. Further, the images were segmented for checking the applicability of the results in the biomedical domain and a remarkable improvement of about (7 ± 0.03) % and (6.5 ± 0.02) was observed in mIoU and BF score.