Magnetic Resonance Images of Brain often contain herringbone artifact in the form of stripes spread in frequency encoding or phase encoding direction throughout the image. The presence of artifacts create problem in image enhancement and reduces the accuracy of segmentation. In this paper, we propose an efficient, powerful and stable filter based on combined wavelet and Fourier transform for the removal of herringbone artifact. The algorithm strictly separates the features between artifact and original image information and also suppresses the unwanted structures present in the artifact image. It tries to preserve original image information at high degree. The quality of the processed image is evaluated using signal to noise ratio and energy loss measures. The performance and feasibility of the filter are tested on several MR images of brain taken from open source and from radiologists. The results shows that there is a greater improvement in signal to noise ratio and minimal energy loss in the processed image and suggests that the algorithm presented in this paper is suitable in processing and removing herringbone artifact in brain MR images.

Magnetic Resonance Imaging (MRI), Herringbone Artifact, Wavelet Transform, Fast Fourier Transform (FFT), Signal to noise ratio (SNR), Energy loss

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