A Comparative Study on Different Skull Stripping Techniques from Brain Magnetic Resonance Imaging

Magnetic Resonance Imaging (MRI) is a popular tool for detection of diseases, as it can provide details about physiological and the chemical components of the tissues, for which the investigation needs to be carried out. The advantage of MRI over other medical imaging techniques is that sectional image of same resolution can be produced without moving the patients. However, the pixel intensity of the grey matter and non-grey matter, which are present in the brain, is almost similar. Hence it creates difficulty in identification and diagnosis of brain diseases. Therefore, identifying and removing the non-brain tissue like skull is very vital for accurate diagnosis of brain-related diseases. This removal of skeletal structure from a brain MRI is called skull stripping. In this paper, different brain MRI skull stripping techniques are discussed and performance analysis is presented with respect to their ground truth images.

[1]  K. K. Bhoyar,et al.  Color Image Segmentation using Fast Fuzzy C-Means Algorithm , 2010 .

[2]  Aman Kumar Sharma,et al.  Split and Merge: A Region Based Image Segmentation , 2018 .

[3]  Md. Nurul Kabir,et al.  A Survey on Image Segmentation Methods , 2016 .

[4]  P. Kalavathi,et al.  Methods on Skull Stripping of MRI Head Scan Images—a Review , 2016, Journal of Digital Imaging.

[5]  J. Harikiran,et al.  Impulse Noise Removal in Digital Images , 2010 .

[6]  Ana Kuzmanić Skelin,et al.  Fast two-step histogram-based image segmentation , 2011 .

[7]  Vandana Shah,et al.  Image segmentation using K-mean clustering for finding tumor in medical application , 2013 .

[8]  Yong Yang,et al.  Image Segmentation by Fuzzy C-Means Clustering Algorithm with a Novel Penalty Term , 2007, Comput. Artif. Intell..

[9]  K. Kishan Rao,et al.  A review on noise reduction methods for brain MRI images , 2015, 2015 International Conference on Signal Processing and Communication Engineering Systems.

[10]  Wen-Jyi Hwang,et al.  Efficient Fuzzy C-Means Architecture for Image Segmentation , 2011, Sensors.

[11]  V. P. Pawar,et al.  K-Means-Based Method for Clustering and Validating Wireless Sensor Network , 2019 .

[12]  Nalin Kumar,et al.  Noise Removal and Filtering Techniques used in Medical Images , 2017 .

[13]  Lei Zhang,et al.  Noise Reduction for Magnetic Resonance Images via Adaptive Multiscale Products Thresholding , 2003, IEEE Trans. Medical Imaging.

[14]  Fuzzy Risk Assessment Information System for Coronary Heart Disease , 2018, International Conference on Innovative Computing and Communications.

[15]  Humera Tariq,et al.  K-Means Cluster Analysis for Image Segmentation , 2014 .

[16]  Akwinder Kaur,et al.  A review on various brain tumor detection techniques in brain MRI images , 2014 .