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Books > Medicine > Nursing & ancillary services > Biomedical engineering
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Content-based Retrieval of Medical Images - Landmarking, Indexing, and Relevance Feedback (Paperback)
Loot Price: R1,014
Discovery Miles 10 140
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Content-based Retrieval of Medical Images - Landmarking, Indexing, and Relevance Feedback (Paperback)
Series: Synthesis Lectures on Biomedical Engineering
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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.
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