Angiography is the definitive procedure for characterising the extent and course of coronary artery disease. We describe the methodology required to measure, with optimal resolving power, angiographic changes in coronary artery disease. We utilised recent technological developments in image digitization, storage and analysis. The measures of change quantified both diffuse and focal atherosclerosis. Frames from angiographic cine films were digitized at high resolution (1024 x 1024 pixels, 8 bit grey scale) and archived on optical disk. Four radiographic projections were stored to ensure good visualization of as many as possible of a set of ten major arterial segments. Edges of segments and catheter were automatically delineated by computer using a dynamic programming algorithm involving a cost function which contained terms based on edge strength and on continuity. For every digitized radiographic projection, delineation was repeated in three adjacent frames, to improve precision. Edge points for each coronary segment were stored on disk. From these we computed the mean width along the segment (pixels). Scaling to obtain the Mean Absolute Width of the Segment (MAWS, mm) was achieved using catheter dimensions known from micrometry, systematic error due to imaging system line-spread function being corrected using data from computer simulations and phantom studies. Correction for geometric image intensifier distortion was also applied. We used the methodology in a randomized, controlled trial of the effect of lipid-lowering therapy, the St Thomas' Atherosclerosis Regression Study. The fundamental measure of change of disease in each segment was the change in MAWS (delta MAWS). Using in-vitro and in-vivo studies we established that the overall resolving power for one segment delta MAWS was 0.10 mm at 2 mm width and 0.14 mm at 4 mm width. Subsidiary end-points were the change (delta) in minimum absolute width of segment (MinAWS), edge irregularity index (EII) and percent diameter stenosis (%DS). Delta%DS (the conventional angiographic measure of coronary disease) was significantly correlated with change in all indices, closest correlation being seen with delta EII (r = 0.94, p < 0.001).