# Segmentation of Infant Brain Using Nonnegative Matrix Factorization
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metric** & **SDD** & **Otsu** & **K-Means** & **EM** & **Our System** \\ \hline
**DSC (\%)** & 87.82 \(\pm\) 7.70 & 91.06 \(\pm\) 3.28 & 72.75 \(\pm\) 8.64 & 70.81 \(\pm\) 8.28 & 96.64 \(\pm\) 1.15 \\
**H95 (mm)** & 14.83 \(\pm\) 8.16 & 12.37 \(\pm\) 6.67 & 16.88 \(\pm\) 5.85 & 21.8 \(\pm\) 8.2 & 7.17 \(\pm\) 3.96 \\
**AVD (\%)** & 12.41 \(\pm\) 10.65 & 14.09 \(\pm\) 6.49 & 41.92 \(\pm\) 25.89 & 81.54 \(\pm\) 31.77 & 2.66 \(\pm\) 2.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative evaluation of our brain segmentation system compared to other segmentation approaches using DSC (%), H95 (mm), and AVD (%). Note that SDD and EM stand for slope difference distribution based clustering and expectation maximization clustering, respectively. Results represent \(\mu\pm\sigma\) values. employed the sparsely represented tissue complementary distribution for segmenting brains from DTI, T1, and T2. The initial segmentation for the various brain structures was performed using patching, along with a ground truth-aligned multi-modal image library. Geometric constraints are then integrated for refinement. Aranguren et al. [11] proposed a multilevel thresholding technique to improve the LSHADE algorithm [12] for the segmentation of magnetic resonance brain images. The other methodology used for segmentation is using predefined atlases [2; 14; 15; 16; 20]. Some of the strongest segmentation approaches employ atlases based on a priori information on the position of the various structures of the brain, so the segmentation could be viewed as a registration problem. An atlas-based segmentation approach from MRI for newborn brains was developed by Prastawa et al. [14]. The approach started with graph robust clustering and estimation of the parameters required to model the initial intensity distributions, then bias correction is performed using the initial intensity distributions as well as the spatial priors. Non-parametric kernel density estimation was used to refine the corrected initial segmentation to produce the final segmentation. An unsupervised deep learning framework for atlas-based registration [20] was introduced for embryonic brain segmentation. In [21], Ruan et al. compared the performance of two different automated brain segmentation in terms of dice coefficient, Hausdorff distance, and the standardized uptake value, to manual segmentation on PET/MR imaging. Weisenfeld et al. [2] proposed an adaptive fully automated classifier fusion technique to segment different tissues of newborn infant brains from MRI, to eliminate bias and variability associated with manual interaction. Tissue class intensity distributions were estimated nonparametrically using samples of the MR intensities of each class representative tissue. The proposed algorithm started with a library of template MRI images, and each template has a large number of manually selected tissue class prototypes. After template-subject registration, different prototype lists lead to nonparametric density estimates that result in different segmentations. These segmentations were then fused to increase the final segmentation accuracy. Shi et al. [15] developed a longitudinal brain segmentation technique for the newborns. Their technique has a bias correction step. It utilizes the probabilistic atlas of the tissues of each subject specifically and builds an atlas longitudinally for each subject from late time scans. To build an atlas for each subject specifically, the late time scans at the ages of 1 and 2 years were segmented using an adaptive fuzzy segmentation algorithm (AFCM) to get the probabilistic maps of tissue classes. The atlas construction is then followed by an iterative process of segmentation and registration carried out jointly, resulting in aligning the atlas. Kim et al. [16] used spatial intensity growth maps computed by linear regression on a voxel basis to compensate for the inhomogeneity present in the infant brain intensity values.