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020 _a9783031016516
_9978-3-031-01651-6
024 7 _a10.1007/978-3-031-01651-6
_2doi
050 4 _aT1-995
072 7 _aTBC
_2bicssc
072 7 _aTEC000000
_2bisacsh
072 7 _aTBC
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082 0 4 _a620
_223
100 1 _aAzevedo-Marques, Paulo Mazzoncini de.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_984116
245 1 0 _aContent-based Retrieval of Medical Images
_h[electronic resource] :
_bLandmarking, Indexing, and Relevance Feedback /
_cby Paulo Mazzoncini de Azevedo-Marques, Rangaraj Rangayyan.
250 _a1st ed. 2013.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2013.
300 _aXIX, 125 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Biomedical Engineering,
_x1930-0336
505 0 _aIntroduction 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.
520 _aContent-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 performanceis 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.
650 0 _aEngineering.
_99405
650 0 _aBiophysics.
_94093
650 0 _aBiomedical engineering.
_93292
650 1 4 _aTechnology and Engineering.
_984118
650 2 4 _aBiophysics.
_94093
650 2 4 _aBiomedical Engineering and Bioengineering.
_931842
700 1 _aRangayyan, Rangaraj.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_984120
710 2 _aSpringerLink (Online service)
_984122
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031005237
776 0 8 _iPrinted edition:
_z9783031027796
830 0 _aSynthesis Lectures on Biomedical Engineering,
_x1930-0336
_984123
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01651-6
912 _aZDB-2-SXSC
942 _cEBK
999 _c85619
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