White Matter Fiber Tract Segmentation Using Nonnegative Matrix Factorization

• Main Authors: Xuwei Liang, Jie Wang, Zhenmin Lin, Jun Zhang
• Format: Conference Proceeding
• Language: English
• Subjects: Clustering algorithms; Computer science; Diffusion tensor imaging; Frequency; Humans; Image reconstruction; Image segmentation; Magnetic resonance imaging; Optical fiber communication; Shape
• ISSN: 2151-7614; 2151-7622
• DOI: 10.1109/ICBBE.2009.5163763

Accurate and efficient white matter fiber tract segmentation is an important step in clinical and anatomical studies that use diffusion tensor magnetic resonance imaging (DTI) tractography techniques. In this work, we present a novel technique to group white matter fiber tracts reconstructed from DTI into bundles using Nonnegative Matrix Factorization (NMF) of the frequency-tract matrix. A fiber tract is quantified by Fourier descriptors in terms of frequencies. Fourier descriptors derived from the shape signature, the central angle dot product, are used to construct the nonnegative frequency-tract matrix which is analogous to the term-document matrix in the document clustering context. In the NMF derived feature space, each basis vector captures the base shape of a particular fiber tract bundle. Each fiber tract is represented as an additive combination of the base shapes. The cluster label of each fiber tract is easily determined by finding the basis vector with which a fiber tract has the largest projection value. Preliminary experimental results with real DTI data show that this method efficiently groups tracts into plausible bundles. This indicates that NMF may be used in fiber tract segmentation with appropriate fiber tract encodings.