FP-MMR: A Framework for the Preprocessing of Multimodal MR Images

Bias field and intensity inhomogeneities are an objectionable artifact that mainly arises from the inappropriate image acquisition process which leads to worsening the medical images. For many automated analysis techniques like segmentation, classification, and registration, etc., image preprocessing is an essential step to reduce various artifacts and tends to improve the standards of medical MRI images. FP-MMR presented a preprocessing method for the brain MRI images. We report a promising result on the BRATS 2013 challenge with an average accuracy of 90.76% and dice coefficient of 70.6% with an increase of 6.9% and 3.5%, respectively. FP-MMR is computationally efficient as it gives the adoption in various fields of research, especially in medical image analysis.