# Segmentation of Infant Brain Using Nonnegative Matrix Factorization
## 5 Discussion
They extended the previous work to include 6-month-old scans by integrating both newborn and 1-year-old scans in guiding the segmentation. To handle the associated intensity problems, they used the fractional anisotropy (FA) maps extracted from the DTI. Moreover, temporally consistent segmentation results were obtained by a longitudinally consistent term. Wang et al. [29] proposed another MR level set based neonatal brain segmentation approach that is patch-driven through sparse representation, which is effectively capable of creating an atlas for each subject specifically from an aligned library of images that were manually segmented. The resultant tissue probabilistic maps were then refined by measuring the similarities existing between the patch and its neighbors. The final segmentation was obtained by integrating the refined probabilities into a coupled level set framework. Mesejo et al. [30] introduced a new deformable model that combined region and boundary information with prior shape knowledge introduced using nonlinear registration. Level set parameters were optimized using a genetic algorithm, and scatter search was used to find optimal shape priors. Baghdadi et al. [31] combined both a fuzzy C-means model with a deformable model implemented with the generalized fast marching method (GFMM) to segment brain MR images and compared their technique to the well-known SPM, FSL, and Freesurfer neuroimaging tools. In [32], Wu et al. presented their ROI brain segmentation using deformable shape model guided by an image-based regressor, and compared their performance to multi-atlas-based methods and other learning-based methods on three public brain MR datasets. Although deformable model-based segmentation is capable of segmenting connected objects with an accuracy higher than other techniques, the achieved accuracy is dependent on the appropriate design of the statistical and geometric guiding forces, as well as the model initialization. In addition to the limitations associated with each segmentation category, most of the previously mentioned MR segmentation methods for the infant brains were devoted to segment brains either within early infancy (up to 5 months) or early toddler stage (12 months or later), employing T1-weighted MRI, T2-weighted MRI, or both together [2; 4; 8; 9; 14; 15; 16; 23; 24; 26]. Nonetheless, these techniques would come up short for infants within the isointense age (6-12 months), which is the essential concern of this study, since both WM and GM have a high overlap on same levels of intensity in the isointense stage. Hence, a segmentation framework integrating other scan contrasts (e.g., FA maps [10; 28]) will significantly distinguish between WM and GM within the isointense age. In any case, these studies have never attempted to consider other DTI scan contrasts in their segmentation frameworks. Moreover, current DTI-based segmentation methods for infant brains endure from some limitations, such as: (i) atlases developed from more than one modality (e.g., T2- and diffusion-weighted) will diminish the accuracy of the segmentation as a result of different contrast levels and inter-slice variability; (ii) utilizing nonlinear warping contrarily influences the shape information, preventing analyzing the shape of the segmented data statistically, which will be advantageous in assessing the relationship between WM and GM morphology [61]; and (iii) most of them depend on the longitudinal information, which is not feasible in most cases. To overcome these limitations, we proposed in this study an adaptive, atlas-based technique for segmenting different structures of infant brains automatically from DTI scans (see Figure 1). The proposed method involves a novel integration of different models (NMF-based visual appearance model, adaptive shape model, and spatial interactions with MGRF) to account for different information encoded in the brain images, ensure homogeneity, and lessen the effect of noise in the image.