A Combination of Restoration, Enhancement and Skull Stripping for Brain MRI

Abstract

The preprocessing steps have substantial influence on the accuracy of segmentation and classification of lesions. The background on the image grid, behind the brain structures in the MRI images may not be always homogeneous. The edges or sharp pixel intensity transitions present in the back ground may get preserved during edge sensitive noise restoration and highlighted during contrast enhancement. If conventional noise restoration methods as Gaussian Kernels are adopted, the weak edges of lesions and structures get smoothened. Similarly, common contrast enhancement schemes like Global/Local histogram equalization either over saturate the image or degrade the textural, intensity and geometrical features of the image above tolerable limit. This study proposes a novel combination of preprocessing methods which is exclusively suitable for MR images carrying weak edges. The proposed combination of preprocessing comprises back ground elimination, restoration with bilateral filter, enhancement with Contrast Limited Adaptive Histogram Equalization (CLAHE) and skull stripping. Back ground elimination and skull stripping are performed by multiplying the original image and contrast enhanced image respectively with a multiplication mask. Multiplication mask for background elimination is generated by gradient based thresholding and a series of morphological operations and the multiplication mask for skull stripping is generated via adaptive Otzu’s thresholding. MR images of tumor edema complex are used for testing the proposed ® strategy. The method is experimented in Matlab . Qualitative inspection of the skull stripped images reveals that the weak edges of tumor-focus and perifocal edema are well preserved, inhomogeneity in the uniform regions is suppressed, CLAHE do not alter the textural intensity and geometrical image features and the brain region is accurately extracted.