The Medical Image Display and Analysis Group at the University of North Carolina: Reminiscences and philosophy

The period of the Medical Image Display and Analysis Group (MIDAG) so far is 1974-2002: more than 27 years. We began with a focus on two-dimensional (2-D) display: contrast enhancement, display scale choice, and display device standardization. We co-invented adaptive histogram equalization and later improved it to contrast-limited AHE, and we were perhaps the first to show that adaptive contrast enhancement, i.e., care in the mapping between recorded and displayed intensity and variation of that mapping with the local properties of the image, could significantly affect diagnostic or therapeutic decisions. MIDAG prides itself in having affected medical practice and, thus, the lives of patients. Despite the fact that bringing research from conception to actual medical use is a process sometimes taking a decade, the largest fraction, perhaps all, of our graduate students and faculty are attracted to these applications of computers by this altruism. Areas in which MIDAG research has come to this fruition are the uses of color display in nuclear medicine, the standardization of CRT display and the realization of how many bits of intensity are needed, and the use of tested contrast enhancement methods in areas of medical image use where subtle changes must be detected. Medical areas where we have had an effect are mammography, a major target area for both the standardization and contrast enhancement ends, and portal imaging in radiotherapy, a target area for contrast enhancement. In the 1980s, some of MIDAG's attention moved to image analysis. Also beginning in the 1980s we began to make some contributions to the notions of scale space description of images. With emphasis on the development of segmentation by deformable models and our aforementioned principle that validation is a critical part of research developing image analysis and display methods, we have begun to seriously face the issues of how to validate segmentation and how to choose the parameters of a segmentation method. Our experimental design and analysis techniques involve a variety of new methods for repeated variables designs.

The University of North Carolina (UNC) Medical Image Display and Analysis Group (MIDAG) traces its history to Steve Pizer's Ph.D. degree work in restoration of scintigrams at Massachusetts General Hospital (MGH) in the middle 1960s. Indeed, the word “scintigram” did not appear in his dissertation and had not yet come into common usage at that time. These nuclear medicine images were the first targets of medical image computing simply because their 32 × 32 or 64 × 64 size made them the only medical images that could fit into the memories of the computers of the time. Done at roughly the same time as Robert Nathan's work at the Jet Propulsion Laboratory and Richard Robb's work at the University of Utah, the image processing system Pizer built on a PDP-7 in 1966–1967 was one of the earliest pieces of medical image processing software; we are interested in hearing of earlier ones. Steve's dissertation research, done in collaboration with Henri Vetter, combined image processing and human vision research, a practice continued in MIDAG. The initial reason for involving human vision research was that the resulting images were to be viewed by humans, but later we also realized that understanding how humans analyzed images could give important hints at how to get computers to analyze them.