An efficient scheme for secure domain medical image fusion over cloud

Lakshmi, V S; Deepthi, P. P.

doi:10.1007/s11042-019-7378-x

Cited by 3 publications

(4 citation statements)

References 32 publications

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“…For encryption and decryption over the fused image, a pseudorandom number framework 17 Z is developed by pseudorandom‐number generator (PRNG) with the help of an encryption key. Further, the encryption process is performed by expansion modulo 256 to get the encoded image P as follows :

P (p, q) = (A (p, q) + Z (p, q)) m o d 256 .

…”

Section: Proposed Methodsmentioning

confidence: 99%

“…16 By using NSST, more features can be extracted by increasing multiple directions and multimodality image fusion operation can be performed. 17 Over the previous years, many developers [18][19][20][21][22] have applied their fusion ways to deal with various kinds of multimodal images. The developers have utilized average and maximum selection rules with complex wavelet transform.…”

Section: Introductionmentioning

confidence: 99%

“…[30][31][32] With the motivation of higher feature extraction by using NSST [33][34][35] and utilized that features for multimodality medical image fusion, [36][37][38][39] a new methodology is performed and also analyzed in secure environment. 17 The concept of method noise thresholding is generally utilized in medical image denoising which is used to improve the accuracy of the results. In this paper, method noise thresholding 19 is utilized in multimodality image fusion.…”

Section: Introductionmentioning

confidence: 99%

“…NSST helps to avoid shift‐invariance problem 16 . By using NSST, more features can be extracted by increasing multiple directions and multimodality image fusion operation can be performed 17 …”

Section: Introductionmentioning

confidence: 99%

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A novel approach for multimodality medical image fusion over secure environment

Kumar

Diwakar

2020

Trans Emerging Tel Tech

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Summary An emerging trend to deal with the issue of multimodality medical image fusion over the secure communication environment is required. Hence, this paper presents a human visual fusion algorithm for medical images such as computed tomography and magnetic resonance imaging which is based on the nonsubsampled shearlet transform (NSST) over the secure environment. Initially, the images are decayed through NSST into low and detailed highlights. The neighborhood aggregate of correlation‐based movement measures is proposed to intertwine the low frequency and sum‐modified‐Laplacian‐based fusion is utilized on detail subbands of NSST. After getting a fused image, a method noise thresholding approach is utilized to improve the accuracy of the proposed method. As a result, more accurate fused images is received. To measure the proposed method accuracy, the results of the fused images are also tested over the secure environment where encrypted/decrypted fused images are obtained using the random generator method. These decrypted fused images are also analyzed with original fused images with the proposed method as well as existing methods. From result analysis, it is observed that the proposed technique is better in holding a bone, calcification, cerebrospinal liquid, edema, and tumor subtleties of the source images and is more accurate than other existing fusion methods.

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P (p, q) = (A (p, q) + Z (p, q)) m o d 256 .

…”

Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A novel approach for multimodality medical image fusion over secure environment

Kumar

Diwakar

2020

Trans Emerging Tel Tech

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Efficient Parallel Secure Outsourcing of Modular Exponentiation to Cloud for IoT Applications

Duan

Yang

et al. 2021

IEEE Internet Things J.

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CryptoLesion

Tanwar

Raman

Rajput

et al. 2020

ACM Trans. Multimedia Comput. Commun. Appl.

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The low-cost, accessing flexibility, agility, and mobility of cloud infrastructures have attracted medical organizations to store their high-resolution data in encrypted form. Besides storage, these infrastructures provide various image processing services for plain (non-encrypted) images. Meanwhile, the privacy and security of uploaded data depend upon the reliability of the service provider(s). The enforcement of laws towards privacy policies in health-care organizations, for not disclosing their patient’s sensitive and private medical information, restrict them to utilize these services. To address these privacy concerns for melanoma detection, we propose CryptoLesion , a privacy-preserving model for segmenting lesion region using whale optimization algorithm (WOA) over the cloud in the encrypted domain (ED). The user’s image is encrypted using a permutation ordered binary number system and a random stumble matrix. The task of segmentation is accomplished by dividing an encrypted image into a pre-defined number of clusters whose optimal centroids are obtained by WOA in ED, followed by the assignment of each pixel of an encrypted image to the unique centroid. The qualitative and quantitative analysis of CryptoLesion is evaluated over publicly available datasets provided in The International Skin Imaging Collaboration Challenges in 2016, 2017, 2018, and PH 2 dataset. The segmented results obtained by CryptoLesion are found to be comparable with the winners of respective challenges. CryptoLesion is proved to be secure from a probabilistic viewpoint and various cryptographic attacks. To the best of our knowledge, CryptoLesion is first moving towards the direction of lesion segmentation in ED.

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An efficient scheme for secure domain medical image fusion over cloud

Cited by 3 publications

References 32 publications

A novel approach for multimodality medical image fusion over secure environment

A novel approach for multimodality medical image fusion over secure environment

Efficient Parallel Secure Outsourcing of Modular Exponentiation to Cloud for IoT Applications

CryptoLesion

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