Several image processing techniques have been employed for quantitative analysis of the collagen morphology observed in SHG microscopy. The simplest approach is to use segmentation methods. For example, Schanne–Klein used a thresholding process of image segmentation of collagen fibers for scoring fibrosis in a mouse model of kidney disease.23 Similarly, Tai et al.24 applied Otsu’s segmentation to score liver fibrosis in both mouse and human tissues. However, segmentation is most sensitive to brightness and the collagen area covered in the image and is not as sensitive to fibrillar alignment and organization, which are often more important markers of diseased states. To help alleviate this limitation, several researchers have explored the use of other signal processing concepts. For example, FFT analysis has been used in several studies for analysis of SHG images.25–28 Although this is simple to implement and has been successful in some cases, it is a global approach, analyzing the frequency components that are present in the entire image. However, perceptually the morphology that often discriminates one type of tissue from another is composed of predominately rapidly changing “local” features. Other transforms, such as wavelets and their variants, are more powerful for local analysis of the fibrillar morphology within such images. For example, we previously used wavelets to examine the length of sarcomeres in normal and optically cleared skeletal muscle and calculated the entropy as a measure of organization.29 More recently, we used two-dimensional (2-D) wavelet transforms to delineate normal lung tissue from that diseased with idiopathic pulmonary fibroses.30 In a different approach, Keely et al. used curvelets, which are highly sensitive to edges,31 to delineate tumor boundaries in different stages of breast cancer.32 Although more applicable than FFT these transforms, in their simplest implementation, still lack the ability to analyze more random patterns of collagen that are representative of the stroma of most ECM tissues (normal and diseased). For example, 2-D wavelet transforms were not successful in accurately classifying the ovarian tissues studied here (unpublished results).