In many three-dimensional imaging applications random shaped objects, reconstructed from serial sections, are isolated to display their overall structure in a single view. This paper presents an algorithm to control an ordered search strategy for locating all contours of random shaped objects intersected by a series of cross-section image planes. Classic search techniques in AI problem solving and software for image processing and computer graphics are combined here to aid program initialization and automate the search process thereafter. Using three-dimensional region growing, this method isolates all spatially connected pixels forming a structure's volume and enters image planes the least number of times to do so. An algorithmic description is given to generalize the process for controlling search in 3-D image data where little core memory is available. Phantom and medical computer tomographic data are used to illustrate the algorithm's performance.

This paper concerns methods for indexing areas in twodimensional array data. A method for naming subpictures from rasterscan image data is presented with notation that eases their subsequent storage access. Equations are given for converting each subpicture name into a storage-location pointer. A function ``NUMERIC'' is described that aids this task. Algorithms enabling efficient retrieval of subpicture areas from sequential and direct access files are presented. Examples are given that show that improved retrieval response is possible from using NUMERIC to sort lists of areas to be recalled. The paper includes an overview of tree data structures, the subject implemented by these techniques. An overlapping picture subareas storage scheme is discussed.