Matching of a Huge Set of MR Images with a Parallel Processing Model

Matching medical image data is a key factor for appropriate computer aided diagnosis. For the past several decades, many image processing technologies have been developed and discussed. However, most of the methods are only of theoretical interest because the time complexity of the matching methods is too high for realistic handling of huge amounts of existing medical images. This paper presents a parallel processing model for matching huge amounts of MR images. A feature vector of an MR image is defined by professionals specifically in the area of neuroscience. Then a matching algorithm is developed based on matching the feature vectors. The algorithm is shown to be suitable for parallel process, and provides acceptable results. The experiments show that the overhead of synchronizing the parallel process is less significant than the improvement of the overall efficiency.