The thalamus comprises multiple nuclei that support higher-order cognitive functions. However, its internal architecture remains difficult to delineate using conventional T1- or T2-weighted MRI because of limited tissue contrast. Diffusion-weighted MRI provides richer microstructural detail, yet accurate segmentation is still challenged by low anisotropy and tissue heterogeneity. To address these challenges, we present a modified spectral clustering framework for thalamic segmentation. Our approach jointly leverages voxel-wise information and fiber orientation distribution (FOD) features derived from multi-shell multi-tissue constrained spherical deconvolution. When evaluated using spatial probabilistic maps that capture across-subject spatial variability in labels, k-means and spectral clustering exhibit broadly similar group-level variability patterns. However, the spectral clustering framework accommodates smaller thalamic subdivisions, including the lateral and medial geniculate nuclei (LGN and MGN), which required exclusion from the k-means configuration for stable parcellation. Under these conditions, spectral clustering achieved Dice scores of 0.73 for the mediodorsal-parafascicular (MD-Pf) complex and 0.51 for the ventral posterolateral (VPL) nucleus and produce a cluster corresponding to LGN. Furthermore, by combining structural and diffusion information, our approach enabled subdivision of the pulvinar into four distinct regions. These result position our modified spectral clustering as a robust and anatomically informed tool for thalamic clustering and pulvinar sub-segmentation.