A Lossless Medical Image Compression Framework Using Logic Minimization

Medical image compression is an active research area owing to the growth of the volume of medical image data in digital form. A method for lossless compression of medical images was proposed using logic minimization. The grayscale medical image is split into bit planes, and each bit plane is divided into blocks of fixed size, e.g., 8 × 4. The binary bit stream resulting from each of these blocks is treated as the output of a Boolean function, and logic minimization is attempted for a compact form. If this step fails to give a compact representation, the bits are stored as such. In this study, an extendable framework for lossless compression of medical images is presented. The bit plane is adaptively divided using a Quadtree to capture large uniform areas as leaf nodes. The non-uniform blocks at the leaf node are subjected to a logic minimization approach, as in the case of fixed-size blocks in previous related work. To improve the result further, the original image is gray-coded as a pre-processing step. On the bit plane, an XOR operation is done for the current block with the neighboring block for redundancy removal. This framework allows further exploration with the incorporation of other Boolean function representation techniques to enhance the compression.

@article{M2026,
  title     = {A Lossless Medical Image Compression Framework Using Logic Minimization},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {2},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Swathi Pai M and Jacob Augustine and Pamela Vinitha Eric},
  doi       = {10.14569/IJACSA.2026.0170219},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170219}
}