Selective Encryption using Segmentation Mask with Chaotic Henon Map for Multidimensional Medical Images
<|reference_start|>Selective Encryption using Segmentation Mask with Chaotic Henon Map for Multidimensional Medical Images: A user-centric design and resource optimization should be at the center of any technology or innovation. The user-centric perspective gives the developer the opportunity to develop with task-based optimization. The user in the medical image field is a medical professional who analyzes the medical images and gives their diagnosis results to the patient. This scheme, having the medical professional user's perspective, innovates in the area of Medical Image storage and security. The architecture is designed with three main segments, namely: Segmentation, Storage, and Retrieval. This architecture was designed owing to the fact that the number of retrieval operations done by medical professionals was toweringly higher when compared to the storage operations done for some handful number of times for a particular medical image. This gives room for our innovation to segment out the medically indispensable part of the medical image, encrypt it, and store it. By encrypting the vital parts of the image using a strong encryption algorithm like the chaotic Henon map, we are able to keep the security intact. Now retrieving the medical image demands only the computationally less stressing decryption of the segmented region of interest. The decryption of the segmented region of interest results in the full recovery of the medical image which can be viewed on demand by the medical professionals for various diagnosis purposes. In this scheme, we were able to achieve a retrieval speed improvement of around 47% when compared to a full image encryption of brain medical CT images.<|reference_end|>
@article{prakash2024selective,
    title={Selective Encryption using Segmentation Mask with Chaotic Henon Map for
  Multidimensional Medical Images},
    author={S Arut Prakash, Aditya Ganesh Kumar, Prabhu Shankar K. C., Lithicka
  Anandavel, Aditya Lakshmi Narayanan},
    journal={arXiv preprint arXiv:2403.04781},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.04781},
    primaryClass={cs.CR cs.CV cs.LG eess.IV}
}