A novel approach for brain MRI segmentation and image restoration under intensity inhomogeneity and noisy conditions

Abstract

Brain magnetic resonance (MR) images are vulnerable to many photometric artifacts such as noise, and intensity inhomogeneity (IH). Furthermore, they contain brain-related intrinsic structural complexities such as edges, corners, and other fine structures. These artifacts and structural complexities pose several challenges for the segmentation process of brain MR images which is needed for the proper diagnosis of brain related diseases. We develop a segmentation framework which deals with noise, IH, uncertainty, and other structural complexities present in the brain MR images in unison. The noise is dealt with by incorporating the local spatial and gray level information in the form of a local parameter-free fuzzy factor. This factor also preserves the fine structural details that are present in abundance in the brain MR images. A recently introduced approach for dealing with the IH is incorporated, and integrated with the local information. The problem pertaining to uncertainty in assigning membership to the pixels lying near the boundaries of different tissues is solved by representing the various components of the information such as intensity, cluster centers, and IH, using the intuitionistic fuzzy set theory. Further, a procedure is developed to obtain artifact-free image which could be useful for its comparison with the input image for the better analysis of the MR images by the experts. Thus, an integrated approach is presented to segment and restore the MR images. Experimental results demonstrate the superiority of the proposed approach over the state-of-the-art approaches about segmentation accuracy and robustness to the various imaging artifacts.