Microstructure characterization and reconstruction are important for both microstructure design representation and design evaluation. Design representation requires a finite set of quantitative descriptors of the infinite dimensional microstructure image as design variables, which can be obtained via microstructure characterization. In design evaluation, statistical FEA analysis of material properties [9,10] requires microstructure reconstruction to generate multiple random but statistically equivalent microstructures. Microstructure characterization describes the heterogeneous microstructure quantitatively with a small set of microstructure parameters based on image analysis. For given microstructure descriptions, microstructure reconstruction generates statistically equivalent digital microstructures for stochastic analysis of material behavior [11]. Three basic categories of microstructure characterization/reconstruction approaches are widely adopted in material analysis and design: (1) the physical descriptor-based approach, (2) the correlation function-based approach, and (3) the random field-based approach. In the physical descriptor-based approach, important structural parameters (descriptors) highly related to the interested material properties are chosen as microstructure representation. Typical microstructure descriptors fall into three categories: composition, dispersion status, and geometry of the inclusions. Composition descriptors indicate the percentage of each material constituent, such as volume fraction of filler. Dispersion descriptors depict the inclusions' spatial relation and their neighbor status, such as the nearest neighbor distance, the ranked neighbor distances, pair correlation [12-16], and spatial pattern descriptors obtained from the infinite dimensional F-, G-, and K-functions [17]. Geometry descriptors provide information of the shapes of inclusions such as the size/radius distribution, surface area, surface-to-area/volume fraction, roundness, eccentricity, elongation, rectangularity, tortuosity, aspect ratio, etc. [12,16,18-23]. The major strength of physical descriptors is the clear physical meanings they offer and the meaningful mappings to processing parameters [24,25].