An investigation of parallel computing techniques in clinical image processing, using transputers
The objective of this work is to investigate the prospects for parallel computing in image processing applications in medicine. The tasks involved in filtered back projection reconstruction and retinal image registration are implemented on a hardware system based on the Inmos Transputer, using the OCCAM language. A number of task decomposition methods are used and their advantages and disadvantages discussed. An example which uses a pipeline illustrates the sensitivity of such an approach to uneven computational load over the network of processors. Farm networks and methods which use simple block division of data are investigated. The design of a system of interconnected processes for efficient, deadlock-free, communication within a grid network is proposed, designed and implemented. The difference in observed performance due both to task decomposition and to network communication are discussed. An image registration technique which uses the edges of image features to reduce the amount of data involved is proposed and implemented using the parallel network. The results of tests on the performance of the registration technique are presented. Registration using the edge-based technique is successful for a significant proportion of image pairs but, because of the high likelihood of registration being required for poor quality images, improvements are worthwhile. Where the registration technique failed, the human observer had equal difficulty in manually registering the same pair of images. A number of suggestions are made for further improvements of the technique. A strategy is proposed which uses task overlapping to improve the efficiency of a multi-stage parallel system of processes. The work highlights the most important factors in the application of parallel computing in image processing using a MIMD network and suggests a number of areas where further work is needed.