Application of the nonlinear optical microscopy (NLOM) for investigation of biological samples has, to date, primarily focused upon the qualitative analysis of images. The general consensus is that the nonlinear optical (NLO) techniques provide enough bio- chemical information when compared to, for example, visible light microscopy. Herein, it is presented a detailed study where a set of tools for quantitative extraction of infor- mation from NLO images were developed and tested for the analysis of complex tissue assemblies. Two-photon excited autofluorescence (TPEF), second-harmonic generation (SHG), and coherent anti-Stokes Raman scattering (CARS) were used for the charac- terization of atherosclerotic plaques. Our NLO-based image analysis of animal arteries affected by atherosclerotic plaque accumulation revealed that images of the healthy regions of the artery can be readily distinguished by marked differences in morphology, due to a fluorescent signal generated from the presence of generally intact elastic layer. Regions affected by lesions were dominated by lipid-rich cells and collagen fibers; the elastic layer was disrupted and the presence of fluorescent particles were also detected. Next, the potential of using information extracted from NLO images lead us to the development of a new optical index for plaque burden (OIPB). Through the OIPB, it was possible to investigate and to classify the plaque severity regarding the already established and currently used definition during clinical analyses. Extrapolating to and anticipating future applications, several methods for extracting specific information from images acquired by each NLOM modality were developed and tested. Texture analysis, particle-specific features, fractal analysis and directionality of components within the images were successfully adapted and tailored to better extract relevant information from the NLO images. Even though the methods presented in this thesis were mostly tested in images from arterial plaques, there is strong evidence that all tools presented here are capable of tracking changes that occur in many medical conditions and applications.