# Segmentation of Infant Brain Using Nonnegative Matrix Factorization
###### Abstract  
This study develops an atlas-based automated framework for segmenting infants' brains from magnetic resonance imaging (MRI). For the accurate segmentation of different structures of an infant's brain at the isointense age (6-12 months), our framework integrates features of diffusion tensor imaging (DTI) (e.g., the fractional anisotropy (FA)). A brain diffusion tensor (DT) image and its region map are considered samples of a Markov-Gibbs random field (MGRF) that jointly models visual appearance, shape, and spatial homogeneity of a goal structure. The visual appearance is modeled with an empirical distribution of the probability of the DTI features, fused by their nonnegative matrix factorization (NMF) and allocation to data clusters. Projecting an initial high-dimensional feature space onto a low-dimensional space of the significant fused features with the NMF allows for better separation of the goal structure and its background. The cluster centers in the latter space are determined at the training stage by the \(K\)-means clustering. In order to adapt to large infant brain inhomogeneities and segment the brain images more accurately, appearance descriptors of both the first-order and second-order are taken into account in the fused NMF feature space. Additionally, a second-order MGRF model is used to describe the appearance based on the voxel intensities and their pairwise spatial dependencies. An adaptive shape prior that is spatially variant is constructed from a training set of co-aligned images, forming an atlas database. Moreover, the spatial homogeneity of the shape is described with a spatially uniform 3D MGRF of the second-order for region labels. In vivo experiments on nine infant datasets showed promising results in terms of the accuracy, which was computed using three metrics: the 95-percentile modified Hausdorff distance (MHD), the Dice similarity coefficient (DSC), and the absolute volume difference (AVD). Both the quantitative and visual assessments confirm that integrating the proposed NMF-fused DTI feature and intensity MGRF models of visual appearance, the adaptive shape prior, and the shape homogeneity MGRF model is promising in segmenting the infant brain DTI. Keywords: infant brain; DTI; segmentation; atlas; NMF; MGRF +
Footnote †: journal:
Regarding infants, for instance, analyzing brain magnetic resonance imaging (MRI) scans in order to treat injuries and disorders caused by brain prematurity depends significantly on brain segmentation [2]. Currently, manual segmentation done by experts is the gold standard. However, it is time consuming and can be subjective. To handle these issues, automated and semi-automated segmentation approaches need to be developed. Brain segmentation from MRI can be very challenging due to the similar appearance of neighboring structures inside the brain, which make it difficult to identify boundaries among them, especially in the presence of inhomogeneity, image noise, and partial volume effect. In addition, in diffusion-weighted imaging (DWI), the effects of the distortions arising from the motion of the patients can be significantly emphasized by the diffusion sensitizing gradient [3]. As opposed to segmenting adult brains, infant brain segmentation is harder, as it can be carried out only based on the image intensity, which is the main focus of this study. The tissues of infant brains are not mature enough to give sufficient contrast between the GM and WM [4] along with the presence of much noise [5]. Moreover, distortions from eddy currents and motion of unseated infants add more difficulty to the segmentation problem. Over the past decades, researchers have been working hard to develop techniques for brain segmentation from MRI. Primarily, state-of-the art segmentation techniques deal with adult brains; however, a few of them can fit infant brains.