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| Biomedical Image Analysis and Machine Learning Technologies: Applications and Techniquesdate: 29 марта 2010 / author: izograv / views: 879 / comments: 0 Biomedical Image Analysis and Machine Learning Technologies: Applications and Techniques by Fabio A. Gonzalez and Eduardo Romero Medical imaging is arguably one of the most impacting technologies in modern society. Its mostly noninvasive nature has substantially contributed to improve the quality of experience in disease treatment and to a better understanding and analysis of conditions in living organisms, specifically human beings. Currently, it is a solid part of electronic medical systems and it is pervasive across medical institutions including health services, hospitals and medical research centres. Over the last two centuries medical imaging has contributed to improve illness diagnosis and treatment across a wide range of conditions. Though, in many cases hidden to the patients, it is delivering substantial contributions to their health and thus tremendously improving their quality of life. Indeed, any of us with access to modern health care services, has been or will be, earlier or later, consciously or unconsciously, enjoy the benefits of this technology. The history of medical imaging expands over more than two centuries and it is full of success stories. It goes back to the end of the 19th century when the German scientist Wilhelm Conrad Röntgen (1845-1923) discovered the X-rays. It was during an eventful night on November 8, 1895, as Wilhelm Röntgen was experimenting with electrified thin gas in vacuum tubes, when he noticed that a barium platinocyanide coated screen across the room was glowing, despite the tube being encased in cardboard. In his experiments he soon noticed that the concerned, so far unknown rays, would pass through his flesh while casting an outline of his bones on the screen. Soon after, his experimental discoveries were published in the Wurzburg Physical- Medical Journal. Six years later, as the community realised the medical value of the X-ray, Wilhelm Röntgen was awarded the first Nobel prize for physics in 1901 and the X-ray became not only a central tool in medicine but also the first cornerstone of medical imaging. Though this discovery is widely known as X-rays, in German language it holds the name “Röntgenstrahlen” in honour to their discoverer. Additional key tools for medical imaging were developed during the last century including magnetic resonance (MR), due to Felix Bloch and Edward Purcell in 1946 (jointly Nobel prize in 1952); nuclear magnetic resonance (NMR), developed for chemical and physical molecular analysis between 1950-1970; magnetic resonance imaging (MRI), developed by Raymonde Damadian (1974-1976); and Computed Tomography (CT), developed by British engineer Godfrey Hounsfield in 1972. With the advent of MRI and CT technology, which involve digital computing and advanced electronics, a quantum leap in medical imaging technology was made and a new branch in science was born: digital medical imaging. Digital medical imaging is nowadays called just “medical imaging” and embraces several areas of science and technology including, conventional medicine, electronics, digital image and signal processing. The latter is fundamental in modern medical imaging since it contributes to the automatic enhancement of sensed information and more critically to its understanding. Digital image and signal processing in medical imaging and more specifically “Machine Learning for Biomedical Image Analysis and Interpretation” is also the core subject of this book.
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