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- W2477033558 abstract "This chapter introduced content-based image retrieval (CBIR) and its key components. CBIR methods support full retrieval by visual content/properties of images, by retrieving image data at a perceptual level with objective and quantitative measurements of the visual content and integration of image processing, pattern recognition, and computer vision. The four key issues in any CBIR system are feature extraction, similarity comparison, the indexing scheme, and the interactive query interface. Feature extraction is the basic and most important component of the CBIR system. The potential benefits of content-based medical image retrieval (CBMIR) range from clinical decision support to medical education and research. Diagnosis by comparing past and current medical images associated with pathological conditions has become one of the primary approaches in case-based reasoning. The CBMIR techniques are designed mainly for anatomical images that capture human anatomy at different levels and provide primarily structural information. The ultimate goal for CBMIR is to find medically meaningful similar cases. Sometimes similar visual features in images may not imply similar diagnoses or symptoms, and vice versa. Combining visual features in the image with domain knowledge to reach the right subset of relevant cases in the database is a key to the success of CBMIR. It would therefore be useful as a training tool for medical students, residents, and researchers to browse and search large collections of disease-related illustrations using their visual attributes." @default.
- W2477033558 created "2016-08-23" @default.
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- W2477033558 date "2008-01-01" @default.
- W2477033558 modified "2023-09-25" @default.
- W2477033558 title "Content-Based Medical Image Retrieval" @default.
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- W2477033558 doi "https://doi.org/10.1016/b978-012373583-6.50008-6" @default.
- W2477033558 hasPublicationYear "2008" @default.
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