Artificial Intelligence in Recognition and Classification of Astrophysical and Medical Images
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This book presents innovative techniques in recognition and classification of astrophysical and medical images. Coverage includes: image standardization and enhancement; region-based methods for pattern recognition in medical and astrophysical images; advanced information processing using statistical methods; and feature recognition and classification using spectral method.
During the past decade digital imaging has significantly progressed in all imaging areas ranging from medicine, pharmacy, chemistry, biology to astrophysics, meteorology and geophysics. The avalanche of digitized images produced a need for special techniques of processing and knowledge extraction from many digital images with minimal or even without human interaction. This has resulted in a new area in the digital processing called pattern recognition that becomes increasingly necessary owing to a growing number of images to be processed. The first applications of pattern recognition techniques were for the analysis of medical X rays and MMR images that enabled the extraction of quantified information in terms of texture, intensity and shape and allowed to significantly improve a di agnosis of human organs. These techniques were significantly developed over the la st few years and combined feature detection and classification by using re gion based and artificial intelligence methods. By using growing databases of medical images processed with pattern recognition and classification t echniques, one can produce fast and consistent diagnosis of diseases based on the accumulated knowledge obtained from many other similar cases from the stored databases. The use of CCD cameras for astroph ysical instruments on the ground and space produce digitized images in va rious fields of astrophysics. In the past decade, many space and ground based instruments provide large numbers of digitized images of the ni ght skies and of the Sun, our closest star.
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