Hi all, To support data transparency between the functions of SimpleITK and NumPy, SimpleITK provides functional integration with NumPy-based script and a method of data-conversion between SimpleITK’s image data and the N-dimensional array object of NumPy. But, there is a significant performance bottleneck in the data-conversion which is based on the operation of data copy. The performance bottleneck can be increased with the size of handling image data and the number of data-conversion.
The performance bottle can be removed by allowing SimpleITK’s image and the arrays of NumPy to share pointers to the same memory symmetrically with copy-on-write semantics. That is, reduce data-copy operations in the data conversion. This approach can be accomplished by developing and/or improving existing data handling methods in ITK and SimpleITK to enable copy-on-write sharing of data between SimpleITK’s image data and other common data object of Python libraries used for scientific computing and image processing. For more detail information, please check the following website: http://www.itk.org/Wiki/SimpleITK/Design_And_Proposals/SIP_002 Any your valuable comments are welcome. Thanks, Hyun Jae
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