# Segmentation of Infant Brain Using Nonnegative Matrix Factorization
## 5 Discussion
Enhanced EM was then used to segment the resulting images. Two registration frameworks that use T2-weighted and DTI images were utilized in [17; 18; 19] for building the atlas. The registrations within a given subject and between the different modalities were built on a multi-scale using B-spline and affine transformation, where the matching metric was the normalized mutual information (MI). Scans captured at different intervals of time were linearly mapped to the atlas of a 1-year-old subject. Nonlinear transformation mapping was then performed relative to a T2-weighted atlas, where the tensor images were extracted from the aligned DWI. The final DTI atlas was obtained by the log-Euclidean averaging of the tensor images. Although statistical segmentation is faster than other techniques, it generally does not handle all real distributions of data, as it relies on predefined statistics. Among the factors that affect this are the scanning parameters and the uniqueness of each patient, especially in the case of MRI-scanned brain tissues that have similar intensities, which makes segmentation inaccurate when using intensity alone. While atlas-based segmentation is more accurate than statistical-based segmentation, it is still challenging as it requires heavy computations and appropriate selection of atlases. Moreover, the obtained segmentation accuracy is highly dependable on the involved registration quality. In addition, atlas-based segmentation depends on the feature selection required in linking the test subject and the prior data that were used in constructing the atlas. For example, most of the current techniques use signal intensity to find the correspondence between the prior atlas and the data that need to get segmented, leading to inaccuracy in the results as the signal intensities are variant with scanner, age, and patient. Segmentation using deformable models is also gaining popularity for its effectiveness [23; 24]. Deformable boundaries have been used for the continuous and accurate segmentation of brain structures from MR. Deformable models-based segmentation techniques are based on deforming curves or surfaces (2D image domain) or hyper-surfaces under the effect of both external and internal forces to outline the targeted object boundary. Leroy et al. [23] introduced a surface reconstruction framework for automated segmentation of the boundary between the cerebral cortex and the underlying white matter in infant brains. After the spatial intensity inhomogeneity correction, the initial cortical surface was identified based on two local priors that were not affected by the intensity inhomogeneity. The final reconstruction of the inner cortical surface was obtained by applying deformation on two surfaces located at each side of the cortical region, and the competition between these two surfaces results in a more robust reconstruction. A segmentation framework based on level sets was developed by Wang et al. [24], in which a combination of local intensity information (modeled by the local Gaussian distribution fitting (LGDF) [25]), atlas spatial prior, and a constraint on the thickness of the cortical surface was used to construct the final coupled level sets. An initial segmentation was first obtained by using coupled level set convex optimization based on the statistical information present globally in the images as well as the spatial prior of the atlas. In another study, Wang et al. [26] developed a segmentation technique in which the level sets are guided longitudinally. They used the segmentation result from later time points in guiding the segmentation of neonatal brains. In particular, two-year-old and neonatal brain images are independently segmented using the adaptive fuzzy c-means algorithm [27] and the previously described coupled level sets segmentation framework [24]. Then, the neonatal segmentation was refined by wrapping the older two-year scan to the neonatal space. A 4D multi-modality extension to the last segmentation framework is presented by Wang et al. [28].