Mostly computer and information scientists address advanced techniques for biomedical image processing, information retrieval in biomedical images, and prediction and simulation in biomedical image mining. Among the topics are applying genetic algorithms in de-noising magnetic resonance images clouded with Rician noise, compressed sensing and its application in computed tomography and electro encephalography, mining medical trends using social networks, computational intelligence-based cell nuclei segmentation from pap smear images, and predicting and detecting epileptic seizure. Annotation ©2016 Ringgold, Inc., Portland, OR (protoview.com)
Every second, users produce large amounts of image data from medical and satellite imaging systems. Image mining techniques that are capable of extracting useful information from image data are becoming increasingly useful, especially in medicine and the health sciences. Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes addresses major techniques regarding image processing as a tool for disease identification and diagnosis, as well as treatment recommendation. Highlighting current research intended to advance the medical field, this publication is essential for use by researchers, advanced-level students, academicians, medical professionals, and technology developers. An essential addition to the reference material available in the field of medicine, this timely publication covers a range of applied research on data mining, image processing, computational simulation, data visualization, and image retrieval.
