# Segmentation of Infant Brain Using Nonnegative Matrix Factorization
## 5 Discussion
This integration allowed an automated segmentation of different brain tissues with comparable output masks to expert's manual segmentation. A preprocessing step was required to account for the image venetian blind artifacts, eddy current, and noise associated with DWI. Furthermore, the acquisition time of DWI is longer, which results in higher motion artifacts, particularly when the infants are not sedated, in addition to inconsistencies occurring along the slices and the gradient. In addition, the image distortions are amplified while acquiring DWI due to the echo-planar imaging (EPI) fast gradient alteration, which requires DWI screening to remove any bias in the collected data, and hence avoid undesirable consequences in the results [34]. The skull stripping procedure is not a simple task, since the contrast between brain and some other tissues, such as dura mater, can be very slight. Brain extraction is an important preprocessing step in CAD systems that deal with the brain, especially if any form of shape-based analysis is performed as having some parts of the brain missing or failure to expel the dura can underestimate or overestimate the cortical thickness [62]. Several methods for extracting the brain have been presented in the literature. These methods use different techniques, namely, deformable models [63; 64; 65], atlas-based [66; 67], label-fusion based, and hybrid methods [68; 69; 70]. However, most of the current methods have been developed specifically to work with T1-weighted brain MR images, so they are usually not suitable for extracting the brain directly from DWI scans. Moreover, they are optimized for adult brains and fail to address the additional challenges imposed by MR infant images (e.g., reduced contrast and higher noise). In order to address these limitations, a new stochastic approach for extracting the brain is proposed in this work. The results were evaluated using volumetric, overlap, and distance evaluation metrics. Various anatomical structures in the infant brain can be more precisely distinguished during the isointense age range by incorporating diffusivity characteristics of the tissue such as FA. A novel joint Markov-Gibbs random field (MGRF) segmentation model is introduced for modeling the images and the desired region maps of the brain. In particular, the joint MGRF model relies on three image descriptors: (i) a set of diffusivity features derived empirically using nonnegative matrix factorization (NMF), (ii) a shape model that works adaptively, and (iii) a three-dimensional, spatially invariant, second-order MGRFhomogeneity descriptor. This segmentation framework utilizes NMF features fusion over the whole space of the DTI features, resulting in fewer dominant features, thus reducing the dimensionality and increasing the separability. The NMF-based visual appearance descriptor is modeled using a \(K\)-means classifier with centroids predefined in the training phase. To overcome the atlas bias problem related with most existing techniques, the proposed framework involves the use of an adaptive shape prior. A prior atlas database is used to build our proposed adaptive shape model. Such database is constructed from a subset of the training datasets that are co-aligned and the adaptation is controlled by the first- and second-order visual appearance characteristics of the new NMF-fused image features, which are represented by the voxel-wise intensities of the image and the features of their spatial interaction. Finally, an MGRF spatial interaction model of the second order is added to compensate for the inhomogeneity that exists extensively in infant brains. One limitation of this proposed method is concerning the small sample size. We conducted this study as a pilot study, showing the promising accuracy of our adopted automated segmentation algorithm is comparable to experts manual segmentation. However, we aim to collect more data from the University of Louisville Hospitals to further validate and analyze the performance in the future work.