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25 April 1997 Multidimensional approach to medical image processing
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Abstract
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 as well as easier to debug. 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 costs 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 nD fast fourier transform operating on real data.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xavier L. Battle and Yves Bizais "Multidimensional approach to medical image processing", Proc. SPIE 3034, Medical Imaging 1997: Image Processing, (25 April 1997); https://doi.org/10.1117/12.274155
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