The National Library of Medicine (NLM), an institute in the National Institutes of Health (NIH), maintains a collection of 17,000 digitized spine X-ray images obtained from the second National Health and Nutrition Examination Survey (NHANES II). Research effort has been devoted to develop a web-accessible retrieval system that allows retrieval of images from the NHANES II database on relevant and frequently found pathologies. A comprehensive and successful image retrieval system requires effective image representation and matching methods, relevance feedback algorithms to incorporate user opinions, and efficient indexing schemes for fast access to image databases. This dissertation studies and develops approaches for all of the above areas within the context of content-Based Image Retrieval (CBIR) of spine X-ray images from the NHANES II collection. Shape is an important characteristic for describing pertinent pathologies in various types of medical images, including spine X-ray images. Retrieving images with shapes similar to a specific user query can be useful for finding pathologies exhibited in images in large survey collections. In this work, vertebral outlines are extracted for image retrieval using shape matching methods to detect the presence of anterior osteophytes. The Multiple Open Triangle (MOT) shape representation method is proposed for partial shape matching (PSM), and a Corner-Guided Dynamic Programming (DP) strategy is developed to search partial intervals for matching comparison based on a 9-point model marked by a board-certified radiologist. The MOT method demonstrates higher retrieval accuracy compared to other approaches and the retrieval speed is improved significantly through the use of Corner-Guided DP. Computer-calculated low-level image features fall short when imitating high-level human visual perception. Relevance Feedback (RF) attempts to bridge the gap between the two by analyzing and employing user feedback. The need for overcoming this gap is more evident in medical image retrieval. Existing RF approaches are analyzed and a weight-updating formula for RF is developed. A hybrid retrieval approach is proposed that utilizes both CBIR with RF and RF history. This hybrid approach uses short-term memory to store the feedback history, which contributes to the retrieval results and helps select images for user feedback. An approximate 20% average increase in retrieval recall percentage is achieved within two RF iterations. Efficient indexing methods are desired for fast database access. An agglomerative clustering algorithm is adopted to pre-index the database based on pre-calculated pair-wise distances between indexed parts. Retrieval with this pre-indexing procedure is shown to offer faster retrieval and maintain a comparable recall percentage.