In the medical field, content-based image retrieval (CBIR) is used to aid radiologists in the retrieval of images with similar contents. CBIR methods are usually developed for specific features of images, so that those methods are not readily applicable across different kinds of medical images. This study proposes a sound methodology for CBIR of mammograms, which is applicable to various formats of medical image. The methodology is divided into two partsimage analysis and image retrieval. In the image analysis part, 19 abnormal regions of interest (ROI) and 20 normal ROIs are selected as samples for the whole ROI dataset. These two groups of ROIs are used to analyze 11 textural features based on gray level co-occurrence matrices. The multivariate t test is then applied to examine the significance of the differences for these 11 textural features from normal and abnormal ROIs. The discriminating features are incorporated into a feature descriptor for the ROI. This descriptor is embedded into the CBIR system. In the image retrieval part, a CBIR system for mammograms is developed. For normalization of feature vectors, a novel technique is proposed to clip the values of feature elements of the top 5%, and then project each image feature onto the unit sphere.To determine the similarity between query image and each ROI in the dataset, the 2 L norm is used to measure the similarity between two images. This system was designed by query-by-example (QBE). Query images were selected from different classes of abnormal ROIs. To evaluate the performance of the CBIR system, the precision and recall were measured. A maximum precision of 51% and recall of 19% were obtained using the gray level co-occurrence matrices and a distance of 5. The averages of precision and recall are 49% and 18% in this experiment.

Content-based image retrieval (CBIR) makes use of image features, such as color and texture, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. This chapter introduces a content-based approach to medical image retrieval. Fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. A case study, which describes the methodology of a CBIR system for retrieving digital mammogram database, is then presented. This chapter is intended to disseminate the knowledge of the CBIR approach to the applications of medical image management and to attract greater interest from various research communities to rapidly advance research in this field.

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As distributed mammogram databases at hospitals and breast screening centers are connected together through PACS, a mammogram retrieval system is needed to help medical professionals locate the mammograms they want to aid in medical diagnosis. This chapter presents a complete content-based mammogram retrieval system, seeking images that are pathologically similar to a given example. In the mammogram retrieval system, the pathological characteristics that have been defined in Breast Imaging Reporting and Data System (BI-RADSTM) are used as criteria to measure the similarity of the mammograms. A detailed description of those mammographic features is provided in this chapter. Since the user’s subjective perception should be taken into account in the image retrieval task, a relevance feedback function is also developed to learn individual users’ knowledge to improve the system performance.

An image is a symbolic representation; people interpret an image and associate semantics with it based on their subjective perceptions, which involves the user’s knowledge, cultural background, personal feelings and so on. Content-based image retrieval (CBIR) systems must be able to interact with users and discover the current user’s information needs. An interactive search paradigm that has been developed for image retrieval is machine learning with a user-in-the-loop, guided by relevance feedback, which refers to the notion of relevance of the individual image based on the current user’s subjective judgment. Relevance feedback serves as an information carrier to convey the user’s information needs / preferences to the retrieval system. This chapter not only provides the fundamentals of CBIR systems and relevance feedback for understanding and incorporating relevance feedback into CBIR systems, but also discusses several approaches to analyzing and learning relevance feedback.