# Conventional and Deep Learning Methods for Skull Stripping in Brain MRI
## 3 Classical or Conventional Skull Stripping Approaches
### Deformable Surface Model-Based Methods  
Deformable surface model-based approaches normally evolve and deform a self-regulating dynamic curve (an active contour) to fit towards the brain surface based on the energy functions. Initially, a surface model is defined (e.g., a tessellated mesh of triangles) and then \"fitted\" to the surface of the brain in the image. Generally, there are two major parts of constraints to the fitting. The first part imposes some form of smoothness on the brain surface to keep it well-conditioned and match the actual smoothness value of the brain surface. The second part of constraints fits the model to the accurate region (or segments) of the brain surface. The fitting (sometimes referred to as curve evolution) is usually accomplished by iteratively deforming the active contour model from its starting position until an appropriate solution is achieved. These algorithms highly rely upon the initial position of the fitting curve and the image intensity variation (i.e., the image gradient). These parameters can betray the actual boundary of the brain and produce over/under the segmentation of the brain. For instance, [PERSON] [60] utilized an active contour to segment the cerebrospinal fluid (CSF) and the white matter (WM) in brain MRIs using a fuzzy membership function, an image gradient detector, and a deformable model. The algorithm brain extraction tool (BET), developed by [PERSON] [25], is the most prevalent and freely available algorithm of this category. In this method, the center of gravity and a rough brain/non-brain threshold of the brain MRI are found using a \"robust\" lower and upper grayscale values of the brain image. Then, it imposes locally adopted model forces to fit the surface of the brain employing a triangular tessellation of a sphere's surface. This is also very fast and needs no preprocessing or other preregistration before being applied. BET was unsuccessful used to extract the brain region in the inferior brain slices (slices with the neck) when the center of gravity of the brain MRI volume lay outside the brain [61]. A new model-based level set (MLS) algorithm to separate intracranial tissues and the skull encircling the brain MRI was developed by [PERSON] et al. [62]. The two terms (whose values represent the forces that influence the speed of the evolving curve) in the level set equation control the evolution of the curve. The first term was inferred from the mean curvature of the curve and the second was obtained from the intensity characteristics of the cortex in the brain MRIs. The combination of these two terms (forces) in the level set framework evolved the curve toward the brain surface. [PERSON] et al. [63] presented a robust and automated brain extraction technique that utilizes an implicit deformable model to represent the contours of the brain and to segment the brain region. A set of [PERSON]'s radial basis functions (RBFs) described the model that has the advantages of low computational complexity and a compact support property. The contours of the brain are identified separately on 2D coronal and sagittal brain slices. The results from these two views are integrated to get a whole 3D brain MRI volume. A very recent article based on improved BET [25] was proposed by [PERSON] et al. [64]. It evolves a 3D active mesh model to fit the brain surface in brain MRIs. A polygon fill algorithm has been used to generate a brain mask. Finally, a ray-casting volume rendering algorithm is utilized to visualize the brain surface from these generated brain masks. One of the advantages associated with these techniques is that they can recognize both the exterior and interior boundaries of the brain simultaneously. Normally, deformable surface model-based approaches have the potential to achieve more precise and accurate skull stripping in brain MRIs than methods based on histogram thresholding, mathematical morphology, and edge detestation.