Content-based image retrieval (CBIR) is the process of retrieval of images from a database that
are similar to a query image using measures derived from the images themselves rather than
relying on accompanying text or annotation. To achieve CBIR the contents of the images need to
be characterized by quantitative features the features of the query image are compared with
the features of each image in the database and images having high similarity with respect to
the query image are retrieved and displayed. CBIR of medical images is a useful tool and could
provide radiologists with assistance in the form of a display of relevant past cases. One of
the challenging aspects of CBIR is to extract features from the images to represent their
visual diagnostic or application-specific information content. In this book methods are
presented for preprocessing segmentation landmarking feature extraction and indexing of
mammograms for CBIR. The preprocessing steps include anisotropic diffusion and the Wiener
filter to remove noise and perform image enhancement. Techniques are described for segmentation
of the breast and fibroglandular disk including maximum entropy a moment-preserving method
and Otsu's method. Image processing techniques are described for automatic detection of the
nipple and the edge of the pectoral muscle via analysis in the Radon domain. By using the
nipple and the pectoral muscle as landmarks mammograms are divided into their internal
external upper and lower parts for further analysis. Methods are presented for feature
extraction using texture analysis shape analysis granulometric analysis moments and
statistical measures. The CBIR system presented provides options for retrieval using the
Kohonen self-organizing map and the k-nearest-neighbor method. Methods are described for
inclusion of expert knowledge to reduce the semantic gap in CBIR including the query point
movement method for relevance feedback (RFb). Analysis of performance is described in terms of
precision recall and relevance-weighted precision of retrieval. Results of application to a
clinical database of mammograms are presented including the input of expert radiologists into
the CBIR and RFb processes. Models are presented for integration of CBIR and computer-aided
diagnosis (CAD) with a picture archival and communication system (PACS) for efficient workflow
in a hospital. Table of Contents: Introduction to Content-based Image Retrieval Mammography
and CAD of Breast Cancer Segmentation and Landmarking of Mammograms Feature Extraction and
Indexing of Mammograms Content-based Retrieval of Mammograms Integration of CBIR and CAD
into Radiological Workflow
