Computational intelligence based secure three-party CBIR scheme for medical data for cloud-assisted healthcare applications

Abstract

Medical images with various modalities have become an integral part of the diagnosis and treatment of several diseases. The medical practitioners often use previous case studies to deal with the current medical condition of any particular patient. In such circumstance, they need to securely access medical images of various cases which are generally stored in a network and are vulnerable to malicious attacks. To address these sensitive inadequacies, we have proposed computational intelligence based secure healthcare Content based Image Retrieval (CBIR) for medical image retrieval scheme through which any medical practitioner can retrieve the image in an encrypted domain in cloud environment. In this regard, hamming distance-based similarity matching is the only available technique that effectively handles the comparison between encrypted features. This technique requires binary features to perform similarity matching, and the performance of such features in image retrieval is poor. In this concern, we have suggested a salient component-based binary feature extraction approach to enhance retrieval accuracy. Initially, we have re-arranged the input image using the saliency map, principal texture direction, and entropy to place the salient components at the starting blocks. Subsequently, we have employed a block-level majority voting scheme on the salient blocks of the image to obtain local binary features. As a result, the final feature vector carries more features from the salient part of the image, which propitiously improves the retrieval accuracy. Later, we have encrypted the binary feature vector and performed image retrieval on cloud environment which involve Data Owner, Database Service Provider and Client over encrypted domain to full fill the security aspect. Finally, we have used medical as well as Corel image datasets to validate the retrieval performance accuracy of the proposed scheme. The experimental results obtained from real life datasets exhibit that the proposed method is secure and provides comparable retrieval accuracy concerning other related schemes in the domain.