# Conventional and Deep Learning Methods for Skull Stripping in Brain MRI
## 3 Classical or Conventional Skull Stripping Approaches
### Region Growing and Watershed Methods  
Region growing (RG) and watershed methods inspect pixels in the brain MRIs and form separate connected regions based on a similarity criterion by merging neighborhood image pixels with uniformity properties. These methods start with at least one image pixel known as the seed that belongs to the region of interest (ROI). Seed neighbors' pixels are examined against a similarity criterion that is defined prior to the start, and those neighbors' pixels satisfying the criterion are included in the ROI (seed's region). The criteria for similarity can be assessed and specified by intensity (or grayscale value) of the brain MRI pixels and other features in the brain image. Seed(s) can be (are) selected manually or by an automatic procedure based on the image's properties (or prior knowledge). The RG process is repeated until a stopping criterion is reached. [PERSON] et al. (2016) devised an interactive 3D segmentation of CT and brain MRI volumes based on RG and morphological filters. [PERSON] et al. (2016) are also proposed a 3D SRG (seeded region growing) method for brain MRI segmentation. [PERSON] and [PERSON] (2017) formulated an efficient and robust method based on 2-dimensional (2D) RG for brain T\({}_{1}\)W MRIs. In the first step, background voxels of brain MRI volume have been eliminated with histogram analysis. Next, seed regions, one for the brain and the other for the non-brain region, are identified utilizing a mask generated by morphological operations. Finally, the brain region is expanded by emerging neighborhood pixels of brain image with a 2D RG algorithm based on general brain structure information. This method can only be used for the coronal orientation of the brain MRIs. [PERSON] et al. (2017) eliminated this limitation and enabled their algorithm's use in both axial views and low-quality brain images, and presented a new RG-based approach known as the multispectral adaptive region growing algorithm (MARGA) for skull stripping. It exploited the complementary information provided by brain MRIs and acquired a seed region that expanded using a 2D RG. [PERSON], [PERSON] et al. (2017) developed a level set approach in a multistage formulation based on local Gaussian distribution fitting energy for brain extraction in MRI having intensity inhomogeneity. The intensities of the image are modeled by Gaussian distribution with various means and variances. [PERSON] et al. (2016); [PERSON] et al. (2016) proposed skull stripping techniques based on region labeling, clustering, and 2D RG. [PERSON] et al. (2017) presented a simple and robust algorithm based on a modified 3D fast watershed transform to locate the brain tissues and remove the non-cerebal tissue in a T\({}_{1}\)W brain MRI. [PERSON] et al. (2017) combined a deformable surface model ([PERSON] et al. (2016) and the watershed algorithm ([PERSON] et al. (2016) to form a new hybrid watershed algorithm (HWA) for brain segmentation. [PERSON] et al. (2016) argued that the above two algorithms ([PERSON] et al. (2017) and [PERSON] et al. (2017)) diverge in their errors in the dissimilar region; thus, the intersection of the two algorithms would provide more robust skull stripping results.  
The partial volume effect (PVE) ([PERSON] et al., 2016) blurs and distorts the intensity distinctions between the various cerebral tissues of brain MRIs and confines the accuracy of the RG method. Furthermore, watershed algorithms typically suffer from over-segmentation of the brain tissue.