Fiber Clustering Acceleration With a Modified Kmeans++ Algorithm Using Data Parallelism

Fiber clustering methods are typically used in brain research to study the organization of white matter bundles from large diffusion MRI tractography datasets. These methods enable exploratory bundle inspection using visualization and other methods that require identifying brain white matter structures in individuals or a population. Some applications, such as real-time visualization and inter-subject clustering, need fast and high-quality intra-subject clustering algorithms. This work proposes a parallel algorithm using a General Purpose Graphics Processing Unit (GPGPU) for fiber clustering based on the FFClust algorithm. The proposed GPGPU implementation exploits data parallelism using both multicore and GPU fine-grained parallelism present in commodity architectures, including current laptops and desktop computers. Our approach implements all FFClust steps in parallel, improving execution times in all of them. In addition, our parallel approach includes a parallel Kmeans++ algorithm implementation and defines a new variant of Kmeans++ to reduce the impact of choosing outliers as initial centroids. The results show that our approach provides clustering quality results very similar to FFClust, and it requires an execution time of 3.5 s for processing about a million fibers, achieving a speedup of 11.5 times compared to FFClust.

Effect of Fractal-Based Fiber Clustering on Tensile Properties of BFRP

To explore the clustering phenomenon of discontinuous fibers in composite materials, this paper deduces the fiber uniform distribution coefficient and analytical expressions of fiber clustering content based on fractal theory and establishes a tensile strength prediction model of fiber/epoxy resin composite materials containing cluster fibers. With basalt fiber/epoxy resin composites (BFRP) as an example, this paper analyzes the tensile strength law of BFRP under fiber clustering effect. The results show that when the fiber volume fraction is constant, the tensile strength of the composite in the presence of agglomerated fibers is only related to the fractal dimension of the circumference and cross-sectional area of the inner fiber agglomerate. The calculated value of the composite tensile strength based on fractal theory is lower than the experimental value, but closer to the experimental value than the approximate method. The research conclusions can provide theoretical support for strength prediction of fiber/epoxy resin composites.