Biomedical Image Processing and Classification

Biomedical image processing is an interdisciplinary field [1] that spreads its foundations throughout a variety of disciplines, including electronic engineering, computer science, physics, mathematics, physiology, and medicine. Several imaging techniques have been developed [2], providing many approaches to the study of the body, including X-rays for computed tomography, ultrasounds, magnetic resonance, radioactive pharmaceuticals used in nuclear medicine (for positron emission tomography and single-photon emission computed tomography), elastography, functional near-infrared spectroscopy, endoscopy, photoacoustic imaging, and thermography. Even bioelectric sensors, when using high-density systems sampling a two dimensional surface (e.g., in electroencephalography or electromyography [3]), can provide data that can be studied by image processing methods. Biomedical image processing is finding an increasing number of important applications, for example, to make image segmentation of an organ to study its internal structure and to support the diagnosis of a disease or the selection of a treatment [4].
Classification theory is another well developed field of research [5] connected to machine learning, which is an important branch of artificial intelligence. Different problems have been addressed, from the supervised identification of a map relating input features to a desired output, to the exploration of data by unsupervised learning (cluster analysis, data mining) or online training through experience. The estimation of informative features and their further processing (by feature generation) and selection (either by filtering or with approaches wrapped to the classifier) are important steps, both to improve classification performance (avoiding overfitting) and to investigate the information provided by candidate features to the output of interest. Excellent results have also been recently documented by deep learning approaches [6], in which optimal features are automatically extracted in deep layers on the basis of training examples and then used for classification.
When classification methods are associated with image processing, computer-aided diagnosis (CAD) systems can be developed, e.g., for the identification of diseased tissue [7] or a specific lesion or malformation [4]. These results indicate interesting future prospects in supporting the diagnosis of diseases [8].