Effective sparse representation of X-Ray medical images
This addresses data storage and processing challenges for medical imaging applications, but appears incremental as it builds on existing sparse representation methods.
The paper tackles the problem of data reduction for X-Ray medical images by proposing a sparse representation framework, achieving an enormous reduction in data set cardinality while maintaining very good image quality.
Effective sparse representation of X-Ray medical images within the context of data reduction is considered. The proposed framework is shown to render an enormous reduction in the cardinality of the data set required to represent this class of images at very good quality. The particularity of the approach is that it can be implemented at very competitive processing time and low memory requirements