An integrated blockchain and fractional DCT based highly secured framework for storage and retrieval of retinal images

Abstract

The digital image capability of storing large amount of data has resulted in it’s increased popularity. Images are used to transmit large amount of information across different geographical locations using different cloud services. Securing these digitally stored images has remained a challenging task for researchers as they are prone to cyber- attacks. A potential solution to this problem can be blockchain, which provide secure and unchangeable storage. However, securing images on blockchain has another challenge as the image size increases the associated cost involved in blockchain. Fractional Discrete Cosine Transform(fctDCT) has the capability to minimizes the amount of data necessary for expressing an image in a secure way. This paper presents a novel framework for securely storing and retrieving medical images by extracting feature maps from medical images by fctDCT, followed by encoding and storing the feature map on decentralized cloud and linking them on blockchain. The integration has been implemented by using four different α angles which are stored on blockchain and are needed to be same at storage and retrieval stage as only the authentic user would have access to unique α angles and number of coefficients that have been used in storing their medical images. The performance of proposed framework has been evaluated by employing image quality metric such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and multi-SSIM by comparing it with correct and incorrect α values on four different values of α.

Keywords

• Fractional DCT
• Fractional angle’s
• Blockchain
• Feature Extraction
• Distributed cloud