A Massively Parallel Approach to Affine Transformataion in Medical Image Registration

Medical image registration plays an important role in investigating disease processes and understanding normal development and ageing. An essential component in most medical registration approaches is affine transformation. The affine transformation is made up of any combination of linear transformations (rotation and scaling) followed by translation. These algorithms are generally computationally expensive. The increasing availability of parallel computers makes parallelizing these tasks an attractive option. This paper proposes a massively parallel approach for affine transformations using a representative data parallel architecture to accelerate such algorithms. The result of our parallel approach is outstanding in terms of both processing performance and energy efficiency. The proposed parallel approach achieves a three order of computational capabilities and a second order of energy efficiency of other implementations using commercial processors such as TI DSP and ARM families.