To enhance breast cancer screening practices, artificial intelligence (AI) systems have been developed to aid radiologists in a variety of tasks. Machine learning (ML) techniques for computer-aided diagnosis are based on human-engineered or deep learning methods, and they depend on accurate segmentation for useful feature extraction. As the use of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) has increased in breast imaging, particularly for high-risk screening, the potential for AI to provide significant clinical benefit has grown. There is need for a deeper understanding of how breast MRI can be used for diagnosis and risk assessment in order to develop robust, generalizable AI systems for quantifying clinically valuable breast characteristics. This dissertation presents novel methods for computerized assessment of background parenchymal enhancement (BPE), a known risk factor for breast cancer, from breast DCE-MRI. In Chapter 1, we introduce the background of breast cancer screening with a focus on AI applications to motivate the subsequent chapters. In Chapter 2, we investigate segmentation techniques for lesions and breast regions. In Chapter 3, we develop an ML technique for computer BPE scoring that includes electronic lesion removal. In Chapter 4, we perform an independent evaluation of the BPE scoring algorithm applied to high-risk patients. Ultimately, the results of this work have the potential to encourage future incorporation of quantitative image analysis into the clinical workflow for radiologists and therefore improve patient care. Segmentation of lesions and breasts: Methods for segmentation of breast lesions and breasts from DCE-MRI were investigated using a dataset of patients diagnosed with cancerous or benign mass- or nonmass-enhancing lesions. Lesion segmentation performances of U-Net convolutional neural networks were compared to the fuzzy c-means (FCM) clustering algorithm and to radiologist delineations. Breast segmentation was performed on post-contrast subtraction maximum intensity projection images. Results suggest that using a 2D U-Net on post-contrast subtraction DCE-MRIs is feasible and could be an effective alternative to FCM or 3D U-Net for lesion segmentation. Computerized assessment of BPE: An automatic computer BPE scoring method that includes electronic lesion removal was developed using a dataset of DCE-MRIs that had radiologist BPE ratings available from prior clinical review. Qualitative, radiologist-reported BPE ratings and quantitative, computer BPE scores were evaluated for different breast regions, and the effect of varying image types and magnet strengths was investigated. A statistically significant correlation was found between the radiologist and computer BPEs. Results demonstrated promising performances of the computerized method for classifying BPE levels across various viewing projections and DCE timepoints. BPE scoring on a high-risk dataset: The role of BPE in predicting breast cancer was explored for a dataset of high-risk screening DCE-MRIs. An independent validation of the BPE scoring algorithm reproduced findings from the initial dataset on an independent dataset. In addition, results found a statistically significant difference between the computer BPE scores of patients that developed cancer and those of non-cancer patients with low BPE. Future investigations involving enriched datasets would expand the understanding of the role that computer BPE scores can have in predicting cancer.