# Segmentation of Infant Brain Using Nonnegative Matrix Factorization
## 5 Discussion  
This study presented an adaptive, atlas-based technique for automatically segmenting different structures of infant brains at the isointense age (6-12 months) from DTI scans. The proposed technique integrated NMF-fused DTI feature, intensity MGRF models of visual appearance, adaptive shape prior, and shape homogeneity MGRF model to ensure the accuracy and efficiency of the segmentation process. Moreover, the proposed technique was able to avoid the limitations associated with other segmentation techniques that targeted infant brains. In the next paragraphs, we will discuss different segmentation techniques presented in the literature, along with each technique's advantages and limitations. Eventually, we will explain how our proposed technique could enhance the accuracy of infant brains segmentation and mitigate limitations of other segmentation methods. As mentioned in the introduction section, brain segmentation from MRI is classified under one of the following categories: statistical techniques, atlas-based techniques, and deformable model-based techniques. While the main goal of most of these techniques is segmenting adult brains, a few of them can fit infant brains. In the subsequent paragraphs, we will overview and address the limitations of the current segmentation techniques that deal with MR infant brains, with a special focus on those which incorporate DTI measurements in their framework. Generally, brain segmentation techniques are categorized as under statistical methods, atlas-based methods, and deformable model-based methods. Statistical models are one of the most commonly used models in segmentation. For example, prior models are used to characterize the distribution of signals for each structure of the brain. Expectation-Maximization (EM)-based parametric Gaussian density estimation was used by Makropoulos et al. [6], where Markov random field (MRF) regularization accounted for the MRI spatial homogeneity constraints. Automated segmentation of brain structures by Anbeek et al. [7] used probability maps to segment each brain tissue class with a \(K\)-nearest neighbor classifier, which employed features such as voxel intensities and coordinates that were manually constructed. Cardoso et al. [8] proposed a posteriori EM multi-modality segmentation algorithm that included the use of a relaxation approach that iteratively adjusts the tissue priors to match the input subject. The segmentation algorithm also included intensity non-uniformity (INU) correction model, an MRF-based homogeneity descriptor, and a correction of partial volume containing voxels. More generalized image priors were introduced by Gui et al. [9], where the creation of these priors was based on a fusion between high-level morphological data such as connectivity and relative spatial location. This morphology information was incorporated in the segmentation process through a neighborhood selection criterion, which guides a region growing algorithm in separating different brain structures. Patrial volume errors are corrected by imposing a condition on WM growing into neighboring extra-ventricular CSF. Wang et al. [10] \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metric** & **CSF** & **GM** & **WM** & **Others** & **Brain** \\ \hline
**DSC (\%)** & 87.96 \(\pm\) 3.31 & 89.92 \(\pm\) 2.86 & 95.23 \(\pm\) 1.18 & 92.81 \(\pm\) 5.26 & 96.64 \(\pm\) 1.15 \\
**H95 (mm)** & 2.42 \(\pm\) 0.56 & 1.98 \(\pm\) 1.07 & 1.98 \(\pm\) 0.01 & 6.60 \(\pm\) 2.75 & 7.17 \(\pm\) 3.96 \\
**AVD (\%)** & 6.10 \(\pm\) 4.70 & 9.85 \(\pm\) 2.34 & 5.15 \(\pm\) 2.03 & 9.74 \(\pm\) 9.65 & 2.66 \(\pm\) 2.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Segmentation summary results, using DSC (%), H95 (mm), and AVD (%). Results represent \(\mu\pm\sigma\) values.