Remodeling of the collagen architecture in the extracellular matrix (ECM) has been implicated in ovarian cancer. To quantify these alterations, we implemented a form of 3D texture analysis method based on textons to delineate the fibrillar morphology observed in 3D Second Harmonic Generation (SHG) microscopy image data of normal (1) and high risk (2) ovarian stroma, (3) benign ovarian tumors, low grade (4) and high grade (5) serous tumors, and endometrioid tumors (6). We developed a tailored set of 3D filters which extract textural features in the 3D image sets to build (or learn) statistical models of each tissue class. By applying k-nearest neighbor classification using these learned models, we achieved 83-91% accuracies for the six classes. The 3D method outperformed the analogous 2D classification by 10-15% on the same tissues, where we suggest this is due the increased information content available in 3D voxels. This classification based on ECM structural changes will complement conventional classification based on genetic profiles and can serve as an additional biomarker. Moreover, the texture analysis algorithm is quite general, as it does not rely on single morphological metrics such as fiber alignment, length, and width but their combined convolution with a customizable basis. We further discuss a new approach to achieve complete 3D SHG imaging, that is based on a rotating multiview platform. We show this visualizes axially oriented features missing in conventional en face imaging. The data sets are compatible with the texture analysis here and will further improve upon this approach.