Validation in Medical Image Processing

Medical imaging is one of our most powerful tools for gaining insight into normal and pathological processes that affect health. The role of image processing in medicine is expanding with the increasing importance of finding ways to improve workflow in reading environments where more images are being acquired in more acquisition modalities. Image processing is playing a crucial role in the maturation of quantitative imaging techniques, such as in functional MRI and diffusion tensor MRI, where visualization of the acquired images alone is insufficient. Image processing, embedded in larger systems and applications, is used more and more extensively in medicine from diagnosis to therapy. Image processing has an important influence on the medical decision making process and even on surgical actions [1]. Therefore, high quality and accuracy are expected [2] and some Working Groups have even been formed and workshops held recently to address the topic of validation [3,4] The performance of image processing methods may have an important impact on the performance of the larger systems as well as on the human observer that needs to analyze all of the available image data and render a diagnostic or therapeutic decision. An emerging focus is the development of imaging biomarkers for drug or therapy response [5], and the development and application of sophisticated image analysis methods in order to improve the accuracy of diagnosis, or to better predict outcomes of disease or treatment and intervention strategies.