Intelligent Information Description and Recognition in Biomedical Image Databases

Intelligent Information Description and Recognition in Biomedical Image Databases

Khalifa Djemal (University of Evry Val d’Essonne, France) and Hichem Maaref (University of Evry Val d’Essonne, France)
DOI: 10.4018/978-1-60960-551-3.ch003
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There is a significant increase in the use of biomedical images in clinical medicine, disease research, and education. While the literature lists several successful methods that were developed and implemented for content-based image retrieval and recognition, they have been unable to make significant inroads in biomedical image recognition domain. The use of computer-aided diagnosis has been increasing. It is based on descriptors extraction and classification approaches. This interest is due to the need for specialized methods, which are specific to each biomedical image type, and also due to the lack of advances in image recognition systems. In this chapter, the authors present intelligent information description techniques and the most used classification methods in an image retrieval and recognition system. A multicriteria classification method applied for sickle cells disease image databases is given. The recognition performance system is illustrated and discussed.
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The image recognition system consists of extracting from a database all the similar images to a request image chosen by the user. Indeed, the system has attracted research interest in recent years. Principal difficulties consist on the capacity to extract from the image the visual characteristics, the robustness to geometrical deformations and the quantification of similarity concept between images. Indexation and recognition are given from classification methods accomplished on image descriptors.

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