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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 segmenta- - tion method. Our experimental design and analysis techniques involve a variety of new methods for repeated variables designs.