Medical images are a fundamental part of medical diagnosis and treatment. These images are different from typical photographic images primarily because they reveal internal anatomy as opposed to an image of surfaces. They include both projection x-ray images and cross-sectional images, such as those acquired by means of computed tomography (CT) or magnetic resonance imaging (MRI), or one of the other tomographic modalities (SPECT, PET, or ultrasound, for example). Medical image processing is that branch of image processing that deals with such images. It is driven both by the peculiar nature of the images and by the medical applications that make them useful.
The Engineering School houses the Medical Image Processing Laboratory in the Department of Electrical Engineering and Computer Science. This laboratory is directed by professors Benoit M. Dawant and J. Michael Fitzpatrick. While any image processing problem pertaining to medical images is considered important in this laboratory, the primary current projects lie in the areas of image segmentation, image registration, and image-guided surgery. Image segmentation is the process that permits the automatic extraction of structures of interest from the images. Image registration is the determination of a point-to-point mapping that aligns the anatomy in one image with that in another. The images may have been acquired from the same patient or from different patients and may involve the same or different imaging modalities. Image-guided surgery is any surgical procedure in which a surgeon's approach is guided in part by the tracking of instruments relative to images of the patient.