# Conventional and Deep Learning Methods for Skull Stripping in Brain MRI
## 3 Classical or Conventional Skull Stripping Approaches
### Histogram Thresholding with Mathematical Morphology
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Anatomical Plane** & **IBSR** & **LPBA40** & **Oasis** & **NAMIC** & **NFBS** & **CC-12** \\ \hline Coronal & raw & & & & & \\ skull-stripped & & & & & & \\ sagittal & raw & & & & & \\ skull-stripped & & & & & & \\ axial & raw & & & & & \\ skull-stripped & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Figures visualizing anatomical planes of publicly available datasets. histogram and morphological operations. [PERSON] et al. [46] proposed an automatic skull stripping method called SMHASS (simplex mesh and histogram analysis skull stripping) based on deformable models and histogram analysis. Initially, a rough segmentation step building on thresholds and morphological operations is used to find the optimal starting point for the deformation. The simplex mesh deformation is controlled by the local gray levels of the image and the information achieved on the gray level modeling of the rough segmentation. The brain extraction algorithm (BEA) was developed by [PERSON] and [PERSON] [47; 48] using diffusion, morphological operations, and connect component analysis (CCA) for T\({}_{1}\)W and T\({}_{2}\)W brain MRIs. Two-dimensional (2D) brain extraction was proposed by [PERSON] et al. [49] based on fuzzy c-means and morphological operations. The Brain Surface Extractor (BSE) is a well-known and publicly available tool for skull stripping devised by [PERSON] et al. [50]. It employs the combination of anisotropic diffusion filtering, a Marr-Hildreth edge detector, and a series of morphological operations to identify the brain and non-brain regions. The BSE is extremely fast and generates highly explicit whole brain segmentation. It took only 3.5 (\(\pm\) 0.4) s for the whole brain extraction. BSE is also a part of BrainSuite [51] that affords a user interface that permits for human interface. The key deficiency of this technique is that it usually involves parameter tuning to work on a particular brain MRI dataset. The method based on grayscale transformation and morphological operations was presented by [52]. Similar to other approaches, the method devised by [PERSON] et al. [53] applies the grayscale level (or intensity) thresholding followed by the elimination of thin connections to acquire a fine brain mask. A graph cut technique was considered to smooth the contours of the brain instead of morphological filters. [PERSON] et al. [54] proposed HEAD (human encephalon automatic delimiter), an automatic skull stripping method combining an efficient histogram analysis procedure and binary morphological operations to achieve accurate brain segmentation. [PERSON] et al. [55] developed SMSS (the statistical morphology skull stripper), which employs statistical self-similarity and morphological operations to delineate the brain in an MRI. However, their algorithm produces skull stripping with minor amounts of over- and under-segmentation of the brain. [PERSON], [PERSON] et al. [56] introduced a robust skull stripping algorithm based on rough-fuzzy connectedness, termed ARoSi. Recently, [PERSON] et al. [57] proposed an intelligent and robust mathematical morphology-based algorithm. The other latest methods include [58; 59]. The major drawback of these approaches is that morphological operations depend on many fixed parameters, such as the shapes and sizes of structuring elements (for erosion, dilation, opening, etc. ), which can only be found by empirical experimentation. For initial segmentation, the human (user) is normally involved in choosing the threshold value from a variety of starting thresholds. Another problem with these algorithms is that it is very hard to develop a general algorithm for diverse datasets of brain MRIs. It has proved challenging to automatically cope with a range of brain MRI resolutions and sequences [25]. In part, this is because it is very difficult to implement the problem-specific logical constraints (e.g., prior knowledge about brain MRIs, intensity/gray level, etc.) with these approaches.