Secured Compression for 2D Medical Images Through the Manifold and Fuzzy Trapezoidal Correlation Function

Srinivasu, Pakkurthi; Norwawi, Norita Md; AMİRİPALLİ, Shanmuk Srinivas; Deepalakshmi, P.

doi:10.35378/gujs.884880

Cited by 13 publications

(1 citation statement)

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“…Despite its high visual quality, this technique achieved an average PSNR of 44.1 dB, reflecting inherent limitations in data capacity. In 16 , the authors introduced a technique for securely compressing 2D medical images, such as MRI, CT, and ultrasound scans, to efficiently manage storage space while maintaining image integrity and privacy. The method employs a multi-level compression strategy using a dictionary mechanism, combined with a 256-bit symmetric key encryption based on a hashing technique to ensure data security.…”

Section: Related Workmentioning

confidence: 99%

High-capacity data hiding for medical images based on the mask-RCNN model

Saidi,

Tibermacine,

Elhadad

2024

Sci Rep

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This study introduces a novel approach for integrating sensitive patient information within medical images with minimal impact on their diagnostic quality. Utilizing the mask region-based convolutional neural network for identifying regions of minimal medical significance, the method embeds information using discrete cosine transform-based steganography. The focus is on embedding within “insignificant areas”, determined by deep learning models, to ensure image quality and confidentiality are maintained. The methodology comprises three main steps: neural network training for area identification, an embedding process for data concealment, and an extraction process for retrieving embedded information. Experimental evaluations on the CHAOS dataset demonstrate the method’s effectiveness, with the model achieving an average intersection over union score of 0.9146, indicating accurate segmentation. Imperceptibility metrics, including peak signal-to-noise ratio, were employed to assess the quality of stego images, with results showing high capacity embedding with minimal distortion. Furthermore, the embedding capacity and payload analysis reveal the method’s high capacity for data concealment. The proposed method outperforms existing techniques by offering superior image quality, as evidenced by higher peak signal-to-noise ratio values, and efficient concealment capacity, making it a promising solution for secure medical image handling.

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Section: Related Workmentioning

confidence: 99%