CN113160944B - Medical image sharing method based on blockchain - Google Patents
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Abstract
The invention provides a medical image sharing method based on a blockchain, which comprises the steps of acquiring an initial medical image and generating a binary authentication graph; calculating a first shadow image of the initial medical image by using a polynomial-based secret image sharing algorithm, and calculating a second shadow image of the binary authentication graph by using a random grid-based visualized secret sharing algorithm; calculating an exclusive OR result of four positions after each pixel in a first shadow image of the initial medical image, screening out a target shadow image meeting the condition through a preset condition, wherein the target shadow image is a shadow image meeting the authentication condition; and executing a secret image restoration algorithm in the blockchain intelligent contract, and restoring by utilizing the target shadow image to obtain a binary authentication graph and an initial medical image which meet the preset identification degree. The invention can safely and quickly realize the sharing of medical images, and the safety of image transmission is high.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a medical image sharing method based on a block chain.
Background
With the development of science and technology and the increasing health demands of people, medical construction of China gradually develops towards digitization, standardization and intellectualization. Among them, medical images obtained by CT, X-ray, ultrasound, nuclear magnetic resonance, and other imaging techniques have a significant role in assisting diagnosis. Thus, medical images are shared with other medical research institutions, which is of great interest for research and diagnosis of diseases.
Early medical images are required to be recorded by a patient and sent to another medical institution in person so as to complete sharing of the medical images of the patient, and the mode has the problems of high cost, low efficiency and the like. To address the shortcomings of physical media transmission, some research institutions have developed an image sharing network that captures collected medical images through a third party and centralizes authorized parties. However, whether the third party is truly trusted cannot be ascertained. The blockchain has the characteristics of safety, reliability, tamper-proof data uplink and traceability, and the trusted third party is replaced by the blockchain, so that the trust safety of the third party is eliminated. Thus, some researchers propose a method of transmitting medical images using a blockchain technique, in which a set of entity signature information authorized by a patient is stored, and when one entity needs to obtain a certain medical image of the patient, it is first required to obtain a private key corresponding to a public key that the patient previously granted access to sign a transaction as an authentication credential, and then obtain the medical image of the patient from a data source. In addition, the blockchain is used for carrying out audit trail on medical/health data transmission so as to carry out future examination, the method applies the zero trust principle to ensure the safety of medical data during transmission, a sender stores shared medical data on the blockchain by utilizing the cryptography principle, and a receiver can obtain the shared data after logging and verification by utilizing a private key of the sender. The above blockchain-based medical image sharing schemes have a drawback in that they require the key to be kept. If the key is lost, the required medical image cannot be shared. Therefore, in order to solve the problem that the key loss cannot be recovered, a secret sharing technique is proposed.
For example, some scholars propose a (k, n) secret sharing method, where secret data is hidden into the constant term of a (k-1) th order polynomial, and n shadows (also called shared shares) are generated. The (k, n) threshold property in secret sharing indicates that the secret can be recovered from any k shares, while any (k-1) or less shares cannot obtain any information about the original secret. The method is further improved by changing text data into image data, and realizing sharing of digital secret images, in the method, all coefficients of the (k, n) Secret Image Sharing (SIS) scheme, the (k-1) degree polynomial are embedded into secret pixels, and n shadow images (shadow for short) are generated. Likewise, a minimum of k shadow images are required to recover the original secret image, otherwise no information about the secret image can be obtained. In order to recover the secret image faster and reduce the computational complexity, visual Secret Sharing (VSS) has been developed, and the VSS proposal makes the recovery of the secret image feature visual to human eyes. The specific process is that the secret image is divided into n shadow images, the n shadow images are printed on a transparent film, and the original secret image can be recovered through simple stacking operation of at least k transparent films printed with the shadow images without calculation. But this method is only applied to binary images. With the development of secret image sharing, there are also many better image sharing schemes in recent years. For example, some scholars propose a secret image sharing scheme based on generation of a countermeasure network (GAN), in which an original sub-image is generated using image segmentation and DNA encoding, and then training is learned by using GAN, and a secret sub-image is generated through its generation network, in this way, sharing of the secret image is safer, and the reconstruction effect of the secret image is better. However, this solution does not enable authentication of the participants. Other scholars propose a secret sharing method based on Visual Cryptography (VC) and participant passwords, which realizes sharing and management of patient medical images. The authentication of the scheme needs hash calculation, so that the scheme has a certain calculation amount. While others have proposed an authenticatable secret image sharing scheme that is suitable for both cases of dealer participation and non-participation, which enables a secret image to be recovered with high quality by improving a polynomial-based secret image sharing algorithm.
However, the existing algorithm is either too large in calculation amount, unfavorable for image sharing, or insufficient in safety and reliability, so that the medical image sharing cannot meet the requirement of safety.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a safe and reliable medical image sharing method based on a blockchain, which aims to solve the problems of insufficient safety or overlarge calculated amount of the existing medical image sharing method.
In order to achieve the above purpose, the present invention is realized by the following technical scheme: a blockchain-based medical image sharing method, comprising: acquiring an initial medical image and generating a binary authentication chart; calculating a first shadow image of the initial medical image by using a polynomial-based secret image sharing algorithm, and calculating a second shadow image of the binary authentication graph by using a random grid-based visualized secret sharing algorithm; calculating an exclusive OR result of four positions after each pixel in a first shadow image of the initial medical image, screening out a target shadow image meeting the condition through a preset condition, wherein the target shadow image is a shadow image meeting the authentication condition; and executing a secret image restoration algorithm in the blockchain intelligent contract, and restoring by utilizing the target shadow image to obtain a binary authentication graph and an initial medical image which meet the preset identification degree.
Preferably, acquiring the initial medical image includes: and carrying out gray scale processing on the color medical image, wherein the initial medical image is a gray scale image of the color medical image.
Preferably, the binary authentication map is an authentication map generated using preset privacy information of the patient.
Preferably, the preset privacy information includes at least one of: name, gender, identification number, social security number, or medical number.
Preferably, calculating the first shadow image of the initial medical image using a polynomial-based secret image sharing algorithm includes: for each pixel point SI (h, w) of the initial medical image SI, calculating a pixel value S of the first shadow image by using a polynomial-based secret image sharing algorithm 1 I 1 (h,w)＝h(1),S 1 I 2 (h,w)＝h(2),…,S 1 I n (h,w)＝h(n)The method comprises the steps of carrying out a first treatment on the surface of the Wherein, h (1), h (2) and h (n) are all k-1 degree polynomials.
Preferably, calculating the first shadow image of the initial medical image using a polynomial-based secret image sharing algorithm includes: the size of the initial medical image is H1×W1, an initial threshold value (k, n) is set, a preset prime number P is selected, each pixel point of the initial medical image is marked as (H, W) ∈ { (H, W) |1 is not less than H and not more than H1, and 1 is not less than W is not less than W1}; for each pixel s=si (h, w), if s≡p, s=p-1 is set, and a k-1 th order polynomial is constructed: h (m) = (b) 0 +b 1 m+···b k-1 m k-1 ) mod P, where b 0 ＝s，b i Is a random number, i=1, 2, …, k-1; calculating n secret values, namely: s is(s) 1 ＝h(1),...,s i ＝h(i),...,s n =h (n), where i is the identity representing the i-th participant; will calculate s 1 ,s 2 ,…,s n Sequentially assigned to S 1 I 1 (h,w),S 1 I 2 (h,w),…,S 1 I n (h, w); finally, n first shadow images SI are output 1 ,SI 2 ,…,SI n 。
Preferably, prime number P has a value of 257.
Preferably, the preset conditions include: simultaneously satisfying the following two conditions: s is S 1 I i (h,w)<P-1，i＝1、2…n；XOR4LBs(S 1 I i (h,w))＝I i (h, w), wherein XOR4LB is an exclusive OR of the four least significant bits.
Preferably, calculating the second shadow image of the binary authentication map using a random mesh based visualized secret sharing algorithm includes: the size of the binary authentication graph is H2 xW 2, each pixel point (H, W) E { (H, W) 1 is more than or equal to H2,1 is more than or equal to W is less than or equal to W2, and a 0-1 inversion function is utilized to randomly generate a random grid as a shadow image I 1 The method comprises the steps of carrying out a first treatment on the surface of the Shadow image I is calculated using the following formula 2 ：
Encrypting I (h, w) into two temporary bits, denoted d respectively 1 And d 2 Calculate d 3 ＝d 1 ,d 4 ＝d 2 ,…d n ,d n+1 Wherein, if (n+1 mod 2) =0, d n+1 ＝d 2 Otherwise d n+1 ＝d 1 The method comprises the steps of carrying out a first treatment on the surface of the Random permutation d 1 ,d 2 ,…,d n+1 And assign it to I 1 (h,w),I 2 (h,w),…,I n+1 (h, w); finally output n+1 second shadow images I 1 ,I 2 ,…,I n+1 。
Compared with the prior art, the invention has the beneficial effects that:
because the blockchain has the characteristics of decentralization, distrust, tamper resistance, tracing and the like, the invention can avoid the reliability problem of a third party by applying the blockchain to medical image sharing, and in addition, in the blockchain medical image sharing, the problem of key loss can be solved by utilizing secret image sharing based on a polynomial. In addition, the shadow image can be authenticated by combining a visual secret sharing technology, the authenticity of the shadow image is distinguished, the intelligent contract is an automatically executed program, and an authentication recovery image with higher recognition degree and a medical image with lossless recovery can be obtained by using the intelligent contract to call a recovery algorithm. Therefore, the method of the invention provides a new idea for sharing medical images. By adopting the method, effective content protection and safe sharing can be carried out on the medical image.
The invention can obtain good effects of gray medical image sharing and authentication, and has the advantages of lossless recovery, no pixel expansion and simple authentication.
Drawings
FIG. 1 is a flow chart of an embodiment of a blockchain-based medical image sharing method of the present invention;
FIG. 2 is an original image and a shadow image of an initial medical image and a binary authentication map in an embodiment of a blockchain-based medical image sharing method of the present invention;
FIG. 3 is a restoration graph of an initial medical image and a binary authentication graph without false shadow image participation in an embodiment of a blockchain-based medical image sharing method of the present invention;
FIG. 4 is a restoration graph of an initial medical image and a binary authentication graph without false shadow image participation in an embodiment of a blockchain-based medical image sharing method of the present invention;
FIG. 5 is a histogram of a target shadow image obtained in an embodiment of a blockchain-based medical image sharing method of the invention;
FIG. 6 is a bar graph and graph comparing PSNR and entropy with other methods of the prior art for a blockchain-based medical image sharing method embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention relates to a medical image sharing method based on a block chain, which is realized by the following steps with reference to fig. 1: first, step S1 is executed to acquire an initial medical image and a binary authentication map. In this embodiment, the initial medical image is a gray scale image, but since the medical image is usually a color image, the color medical image needs to be converted into the gray scale image, and the converted gray scale image is the initial medical image. In fig. 2, fig. 2 (a) is an initial medical image.
The binary authentication chart is generated by using some private information of the patient, such as name, gender, identity card number, social security card number, medical number and the like, and in fig. 2, fig. 2 (b) is a binary authentication chart, which is a black-and-white image.
Then, step S2 is performed to calculate a first shadow image of the initial medical image, specifically, using a polynomial-based secret image sharing algorithm. For example, for each pixel point SI of the initial medical image SIh, w), calculating to obtain pixel value S of the shadow image by using a polynomial-based secret image sharing algorithm 1 I 1 (h,w)＝h(1),S 1 I 2 (h,w)＝h(2),…,S 1 I n (h, w) =h (n), and the calculation for each pixel is repeated, thereby obtaining a shadow image of the medical image.
Specifically, the following steps are performed:
in step S11, assuming that the initial medical image has a size h1×w1, an initial threshold (k, n) is set, and a preset prime number P is selected, in this embodiment, the value of the preset prime number P is 257. Each pixel point of the initial medical image is marked as (H, W) ∈ { (H, W) 1.ltoreq.h.ltoreq.H2 }. In this embodiment, each pixel point of the initial medical image SI may be represented as SI (h, w).
Step S12, for each pixel point s=si (h, w) of the initial medical image, if S is greater than or equal to P, setting s=p-1, and constructing a k-1 degree polynomial: h (m) = (b) 0 +b 1 m+···b k-1 m k-1 ) mod p, where b0=s, bi is a random number, i=1, 2, …, k-1, mod is a modulo operation, i.e. computation (b 0 +b 1 m+···b k-1 m k-1 ) The remainder obtained after dividing by P.
Then, step S13 is performed to calculate n secret values, where the n secret values are respectively: s is(s) 1 ＝h(1),...,s i ＝h(i),...,s n =h (n), where i is the identity representing the i-th participant.
Next, step S14 is executed to calculate S obtained in step S13 1 ,s 2 ,…,s n Sequentially assigned to S 1 I 1 (h,w),S 1 I 2 (h,w),…,S 1 I n (h,w)。
Finally, repeatedly executing the steps S12 to S14 until all pixels of the initial medical image are calculated, and finally outputting n first shadow images SI 1 ,SI 2 ,…,SI n 。
After the step S2 is completed, step S3 is executed to calculate a second shadow image of the binary authentication map. In particular, the present embodiment employs a random mesh based visualized secretThe sharing algorithm calculates a second shadow image of the binary authentication map. For each pixel point I (h, w) of the binary authentication graph I, calculating to obtain a pixel value I of the second shadow image by using a (2, n+1) random grid-based visualized secret sharing algorithm 1 (h,w),I 2 (h,w),…,I n+1 (h, w) repeating the calculation of each pixel, thereby obtaining a second shadow image of the authentication map.
Specifically, the following steps are performed:
step S21 is first performed: obtaining a binary authentication graph with the size of H2XW2, wherein each pixel point of the binary authentication graph is expressed as (H, W) ∈ { (H, W) } 1.ltoreq.h2, 1.ltoreq.w.ltoreq.W2, and a 0-1 inversion function is utilized to randomly generate a random grid serving as a shadow image I 1 。
Then, step S22 is performed to calculate the shadow image I using the following formula 2 ：
Next, step S23 is executed, I (h, w) is encrypted into two temporary bits, denoted as d, by step S21, step S22 1 And d 2 Then calculate d 3 ＝d 1 ,d 4 ＝d 2 ,…d n ,d n+1 Wherein if (n+1 mod 2) =0, d n+1 ＝d 2 Otherwise d n+1 ＝d 1 Where mod is the remainder operation.
Then, step S24 is performed to randomly replace d 1 ,d 2 ,…,d n+1 And assign it to I 1 (h,w),I 2 (h,w),…,I n+1 (h,w)。
Finally, executing step S24, repeatedly executing step S22 to step S24, and finally outputting n+1 second shadow images I 1 ,I 2 ,…,I n+1 Wherein image I n+1 Is an authentication shadow image belonging to the distributor.
After executing step S3, executing step S4, and screening out the target shadow image satisfying the preset condition. Specifically, the exclusive or result of the last four bits of each pixel in the first shadow image of the initial medical image is calculated, and a target shadow image meeting the condition is screened out through a preset condition, wherein the target shadow image is a shadow image meeting the authentication condition.
For example, the first shadow image obtained by the initial medical image and the second shadow image obtained by the binary authentication image calculation are subjected to the filtering operation under the preset conditions, that is, the following two conditions need to be satisfied at the same time: (1) S is S 1 I i (h,w)<P-1；(2)XOR4LBs(S 1 I i (h,w))＝I i (h, w); wherein XOR4LB is an exclusive or calculation of the four least significant bits. Only if the two conditions are satisfied at the same time, the target shadow image SI which is considered to satisfy the authentication condition 1 ,SI 2 ,…,SI n D, where D is I n+1 。
Then, step S5 and step S6 are executed, and a secret image restoration algorithm is executed in the blockchain intelligent contract, and a binary authentication image and an initial medical image which meet a preset identification degree are restored by using the target shadow image, wherein the secret image restoration algorithm is a reverse algorithm of the algorithm for calculating the first shadow image and the second shadow image.
The method of the invention is applied to carry out experimental analysis on the real medical data set, specifically, the (k, n) threshold value selected by experiments is k=2 and n=3, as shown in (c) to (f) of fig. 2, wherein (c) to (e) are respectively three first shadow images of an initial medical image, and (f) is a second shadow image of a binary authentication image belonging to a distributor. When no false shadow image participates, as shown in fig. 3 (a) and (b), the medical images restored from the two shadow images and the medical images restored from the three shadow images are respectively obtained, and fig. 3 (c) and (d) are respectively obtained from two binary authentication images. The false shadow image referred to herein is randomly generated by malicious nodes, and the experiment distinguishes that all participants are real and that there are unreal two cases, the false shadow image participates in the calculation of the authentication shadow image and the recovery of the initial medical image.
In the case where a false shadow image is involved, as shown in fig. 4 (a) to (d), an authentication restoration map in which a false shadow image is involved in restoration, an authentication restoration map in which no false shadow image is involved in restoration, a medical image in which a false shadow image is involved in restoration, and a medical image in which no false shadow image is involved in restoration are respectively included.
As can be seen from fig. 3 and 4, in the case of the participation of the false shadow image, the generated shadow image does not satisfy the condition screening, so that the generated shadow image is authenticated as false, and the original medical image cannot be restored.
Fig. 5 (a) and (b) are histograms of two shadow images of an initial medical image, respectively, and fig. 6 (a) and (b) are PSNR contrast maps of different methods and information entropy contrast maps of different methods, respectively. As can be seen from fig. 5 and 6, the PSNR of the medical restoration map obtained by the method of the present invention is inf, i.e., the method of the present invention can restore the original medical image without loss through analysis of the peak signal-to-noise ratio (PSNR), information entropy, pixel spread and histogram of the image; the information entropy value of the shadow image is close to 8, and the pixels in the histogram are uniformly distributed, which indicates that the confidentiality of the shadow image is good. As can be obtained from experimental analysis, the method of the present invention can be effectively applied to content protection and secure sharing of medical images.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. A blockchain-based medical image sharing method, comprising:
acquiring an initial medical image and generating a binary authentication chart;
calculating a first shadow image of the initial medical image by using a polynomial-based secret image sharing algorithm, and calculating a second shadow image of the binary authentication image by using a random grid-based visualized secret sharing algorithm;
calculating an exclusive OR result of four positions after each pixel in a first shadow image of the initial medical image, and screening out a target shadow image meeting the condition through a preset condition, wherein the target shadow image is a shadow image meeting the authentication condition;
executing a secret image restoration algorithm in the blockchain intelligent contract, and restoring by utilizing the target shadow image to obtain a binary authentication image and an initial medical image which meet preset identification degree;
wherein calculating the first shadow image of the initial medical image using a polynomial-based secret image sharing algorithm comprises: for each pixel point SI (h, w) of the initial medical image SI, calculating a pixel value S of the first shadow image by using a polynomial-based secret image sharing algorithm 1 I 1 (h,w)＝h(1),S 1 I 2 (h,w)＝h(2),…,S 1 I n (h, w) =h (n), h (1), h (2), h (n) are all k-1 degree polynomials;
calculating a first shadow image of the initial medical image using a polynomial-based secret image sharing algorithm includes: the size of the initial medical image is H1 xW 1, an initial threshold value (k, n) is set, a preset prime number P is selected, each pixel point of the initial medical image is marked as (H, W) ∈ { (H, W) |1 is not less than H and not more than H1, and 1 is not less than W and not more than W1}; for each pixel s=si (h, w), if s≡p, s=p-1 is set, and a k-1 th order polynomial is constructed: h (m) = (b) 0 +b 1 m+…b k-1 m k-1 ) mod P, where b 0 ＝s，b i Is a random number, i=1, 2, …, k-1, a step of; calculating n secret values, namely: s is(s) 1 ＝h(1),...,s i ＝h(i),...,s n =h (n), where i is the identity representing the i-th participant; will calculate s 1 ,s 2 ,…,s n Sequentially assigned to S 1 I 1 (h,w),S 1 I 2 (h,w),…,S 1 I n (h, w); finally, n first shadow images SI are output 1 ,SI 2 ,…,SI n ；
Calculating the second shadow image of the binary authentication map using a random mesh based visualized secret sharing algorithm includes:
for each pixel point I (h, w) of the binary authentication graph I, calculating to obtain a pixel value I of the second shadow image by using a (2, n+1) random grid-based visualized secret sharing algorithm 1 (h,w),I 2 (h,w),…,I n+1 (h, w) repeating the calculation of each pixel, thereby obtaining a second shadow image of the authentication image;
wherein the size of the binary authentication graph is H2XW2, each pixel point (H, W) E { (H, W) |1 is not less than H2,1 is not less than W is not more than W2}, and a 0-1 inversion function is utilized to randomly generate a random grid as a shadow image I 1 ；
Shadow image I is calculated using the following formula 2 ：
Encrypting I (h, w) into two temporary bits, denoted d respectively 1 And d 2 Calculate d 3 ＝d 1 ,d 4 ＝d 2 ,…d n ,d n+1 Wherein if (n+1 mod 2) =0, d n+1 ＝d 2 Otherwise d n+1 ＝d 1 ；
Random permutation d 1 ,d 2 ,…,d n+1 And assign it to I 1 (h,w),I 2 (h,w),…,I n+1 (h,w)；
Finally output n+1 second shadow images I 1 ,I 2 ,…,I n+1 。
2. The blockchain-based medical image sharing method of claim 1, wherein:
acquiring the initial medical image includes: and carrying out gray scale processing on the color medical image, wherein the initial medical image is a gray scale image of the color medical image.
3. The blockchain-based medical image sharing method of claim 2, wherein:
the binary authentication map is an authentication map generated using preset privacy information of a patient.
4. A blockchain-based medical image sharing method as in claim 3, wherein:
the preset privacy information includes at least one of the following: name, gender, identification number, social security number, or medical number.
5. The blockchain-based medical image sharing method of claim 1, wherein:
the prime number P takes a value of 257.
6. The blockchain-based medical image sharing method of claim 1, wherein:
the preset conditions include: simultaneously satisfying the following two conditions:
S 1 I i (h,w)<P-1，i＝1、2…n；
XOR4LBs(S 1 I i (h,w))＝I i (h,w)，
where XOR4LB is an exclusive or of the four least significant bits.
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