Skull stripping using traditional and soft-computing approaches for magnetic resonance images: a semi-systematic meta-analysis

Main Article Content

Humera Azam
Humera Tariq


Keywords : skull stripping, brain MRI, MR image, soft computing, meta-analysis
Abstract

MRI scanner captures the skull along with the brain and the skull needs to be removed for enhanced reliability and validity of medical diagnostic practices. Skull Stripping from Brain MR Images is significantly a core area in medical applications. It is a complicated task to segment an image for skull stripping manually. It is not only time consuming but expensive as well. An automated skull stripping method with good efficiency and effectiveness is required. Currently, a number of skull stripping methods are used in practice. In this review paper, many soft-computing segmentation techniques have been discussed. The purpose of this research study is to review the existing literature to compare the existing traditional and modern methods used for skull stripping from Brain MR images along with their merits and demerits. The semi-systematic review of existing literature has been carried out using the meta-synthesis approach. Broadly, analyses are bifurcated into traditional and modern, i.e. soft-computing methods proposed, experimented with, or applied in practice for effective skull stripping. Popular databases with desired data of Brain MR Images have also been identified, categorized and discussed. Moreover, CPU and GPU based computer systems and their specifications used by different researchers for skull stripping have also been discussed. In the end, the research gap has been identified along with the proposed lead for future research work.

Article Details

How to Cite
Azam, H., & Tariq, H. (2020). Skull stripping using traditional and soft-computing approaches for magnetic resonance images: a semi-systematic meta-analysis. Machine Graphics and Vision, 29(1/4), 33–53. https://doi.org/10.22630/MGV.2020.29.1.3
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