Content-based retrieval of medical images : landmarking, indexing, and relevance feedback /
By: Azevedo-Marques, Paulo Mazzoncini de.
Contributor(s): Rangayyan, Rangaraj M.
Material type:
BookSeries: Synthesis digital library of engineering and computer science: ; Synthesis lectures on biomedical engineering: # 48.Publisher: San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, c2013Description: 1 electronic text (xxiv, 119 p.) : ill., digital file.ISBN: 9781627051422 (electronic bk.).Subject(s): Breast -- Radiography -- Data processing | Content-based image retrieval | Picture archiving and communication systems in medicine | Diagnostic imaging | Digital images -- Abstracting and indexing | Mammography | Radiology Information Systems | Diagnostic Imaging | anisotropic diffusion | breast cancer | breast density | computer-aided diagnosis | content-based image retrieval | fibroglandular disk | granulometry | image enhancement | image segmentation | information retrieval | Kohonen self-organizing map | landmarking of images | mammography | nipple detection | pattern recognition | pectoral muscle | picture archival and communication system | Radon transform | relevance feedback | texture analysis | Wiener filterDDC classification: 618.1907572 Online resources: Abstract with links to resource Also available in print.| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
E books
|
PK Kelkar Library, IIT Kanpur | Available | EBKE461 |
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
Part of: Synthesis digital library of engineering and computer science.
Series from website.
Includes bibliographical references (p. 101-113) and index.
Preface -- Acknowledgments -- Symbols and abbreviations --
1. Introduction to content-based image retrieval -- 1.1 Digital imaging in medical diagnostics -- 1.2 Anatomical and physiological features in medical images -- 1.3 Databases of medical images -- 1.4 CBIR of medical images -- 1.5 Remarks --
2. Mammography and CAD of breast cancer -- 2.1 Mammography for the detection of breast cancer -- 2.2 Characteristics of mammograms -- 2.2.1 X-ray imaging -- 2.2.2 Mammographic imaging -- 2.2.3 Diagnostic features in mammograms -- 2.3 Database of mammograms -- 2.4 CAD of breast cancer -- 2.5 Remarks --
3. Segmentation and landmarking of mammograms -- 3.1 Objectives of mammographic image processing -- 3.2 Enhancement of mammograms -- 3.2.1 The Wiener filter -- 3.2.2 Anisotropic diffusion -- 3.3 Segmentation of mammograms -- 3.3.1 Segmentation of the breast region -- 3.3.2 Segmentation of the fibroglandular disk -- 3.3.3 The radon transform -- 3.3.4 Detection of the pectoral muscle -- 3.3.5 Detection of the nipple -- 3.4 Landmarking of mammograms -- 3.5 Remarks --
4. Feature extraction and indexing of mammograms -- 4.1 Quantitative representation of mammographic features -- 4.2 Statistical analysis of gray-level variation -- 4.3 Statistical analysis of texture -- 4.4 Granulometric analysis -- 4.5 Analysis of shape -- 4.5.1 Morphometric and shape factors -- 4.5.2 Shape analysis using moments -- 4.6 Feature extraction, selection, and indexing of images -- 4.6.1 Feature extraction -- 4.6.2 Feature selection -- 4.6.3 Indexing of images for CBIR -- 4.7 Remarks --
5. Content-based retrieval of mammograms -- 5.1 Query and comparison of images for CBIR -- 5.1.1 Measures of distance and similarity -- 5.1.2 The Kohonen self-organizing map -- 5.1.3 Results of CBIR with mammograms -- 5.2 CBIR with relevance feedback -- 5.2.1 Methods of relevance feedback -- 5.2.2 Relevance-weighted precision of retrieval -- 5.2.3 Assessment of the benefits of RFb in CBIR -- 5.3 Remarks --
6. Integration of CBIR and CAD into radiological workflow -- 6.1 The hospital information system -- 6.2 The radiology information system -- 6.3 Picture archival and communication systems -- 6.4 The DICOM standard -- 6.5 Client-server configurations and radiological workflow -- 6.6 Integration of CBIR, CAD, PACS, RIS, and HIS -- 6.7 Remarks --
Bibliography -- Authors' biographies -- Index.
Abstract freely available; full-text restricted to subscribers or individual document purchasers.
Compendex
INSPEC
Google scholar
Google book search
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.
Also available in print.
Title from PDF t.p. (viewed on February 17, 2013).
There are no comments for this item.