Morphology Based Enhancement and Skull Stripping of MRI Brain Images

Brain is an important part of human body. Many complex human body functions are controlled by brain. Brain imaging is a widely applicable method for diagnosing many brain abnormalities like brain tumor, stroke, paralysis etc. Magnetic Resonance Imaging (MRI) is a recently emerged method for brain imaging. Two major limitations of brain MR Images are low contrast image and presence of skull region in the image. So before detailed analysis of an image, as a preprocessing stage these two problems must be resolved. In this study we propose a method for MR Image contrast enhancement and skull stripping based on the Morphological image processing technique. The proposed method works on T1, T2 and FLAIR axial images. Experimental results show that the proposed method efficiently works for enhancing and skull removal of brain MR Images.

[1]  Noor Elaiza Abdul Khalid,et al.  MRI Brain Abnormalities Segmentation using K-Nearest Neighbors (k-NN) , 2011 .

[2]  Rozi Mahmud,et al.  Skull stripping of MRI brain images using mathematical morphology , 2010, 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES).

[3]  Iván R. Terol-Villalobos,et al.  Morphological contrast measure and contrast enhancement: One application to the segmentation of brain MRI , 2007, Signal Process..

[4]  A. M. Dale,et al.  A hybrid approach to the skull stripping problem in MRI , 2004, NeuroImage.

[5]  Sudipta Roy,et al.  Artefact Removal and Skull Elimination from MRI of Brain Image , 2013 .

[6]  A. C. Phadke,et al.  Feature Extraction and Texture Classification in MRI , 2010 .

[7]  Peter Kulchyski and , 2015 .

[8]  Abdul Hanan Abdullah,et al.  Efficient way of skull stripping in MRI to detect brain tumor by applying morphological operations, after detection of false background , 2012 .

[9]  Suyash Agrawal,et al.  A Ful ly Automatic Approach to Detect Brain Cancer Using Random Walk Algorithm , 2012 .

[10]  M. Stella Atkins,et al.  Fully automatic segmentation of the brain in MRI , 1998, IEEE Transactions on Medical Imaging.

[11]  Atanu Saha,et al.  BRAIN TUMOR SEGMENTATION AND QUANTIFICATION FROM MRI OF BRAIN , 2011 .

[12]  K. Thanushkodi,et al.  Tracking Algorithm For De-Noishing of MR Brain Images , 2009 .

[13]  Barry R. Masters,et al.  Digital Image Processing, Third Edition , 2009 .

[14]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[15]  Heinz-Otto Peitgen,et al.  The Skull Stripping Problem in MRI Solved by a Single 3D Watershed Transform , 2000, MICCAI.

[16]  Pratik Vinayak Oak,et al.  Contrast Enhancement of brain MRI images using histogram based techniques , 2013 .