A multidimensional approach to medical image processing

This paper addresses the issue of writing image processing algorithms and programs that are independent of the dimension of the dataset. Such an approach aims at writing libraries and tool-boxes that will be smaller (because one routine works on all dimensions) as well as easier to debug (the more the routine is used, the easier it is debugged). The data to be processed is stored in a multi-dimensional, self-documented format describing, not only the content of the image, but also its context and the conditions of its acquisition. The work presented in this paper is based on the image kernel of the MIMOSA standard. We propose a recursive programming scheme that allows one to write general algorithms for such multi-dimensional images. Oddly enough, the design of such algorithms is easy and intuitive, thanks to the recursion. Moreover, the computational cost;remains comparable to the one of dimension-specific algorithms. The cost of the recursion is indeed negligible compared to the cost of non trivial processings. We present an implementation of a reduced version of the MIMOSA image kernel, show how elementary processing such as convolution and filtering can be easily implemented. Finally we propose an algorithm for the no Fast Fourier Transform operating on real data.