Abstract: Medical image registration (MIR) has played an important role in medical image processing during the last decade. Its main objective is to integrate information inherent in two images, from different scanning sources, of the same object for guiding medical treatments such as diagnostic, surgery and therapy. A challenging task of MIR arises from the complex relationships of image intensities between the two images. Its performance is primarily depending on a chosen similarity measure technique. In this work, a statistical local binary descriptor (SLBD) is proposed as novel local descriptor of similarity measure, which is simple for computation and can handle Multi-modal registration more effectively. The proposed SLBD employs two statistical values, i.e., the mean and the standard deviation, of all intensities within the image patch for its computation. Finally, these experimental results have shown that SLBD outperforms other descriptors in terms of registration accuracy. In addition, SLBD has demonstrated that SLBD is robust to different modalities.

Thuvanan Borvornvitchotikarn, “Multi-modal Brain MR Image Registration using A Novel Local Binary Descriptor based on Statistical Approach” International Journal of Advanced Computer Science and Applications(IJACSA), 13(6), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130632

@article{Borvornvitchotikarn2022,
title = {Multi-modal Brain MR Image Registration using A Novel Local Binary Descriptor based on Statistical Approach},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130632},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130632},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {6},
author = {Thuvanan Borvornvitchotikarn}
}