Original Medical Image Research Articles

Medical images lossless recovery based on POB number system and image compression.

To protect the information integrity of the medical images, this paper proposes a secure lossless recovery scheme for medical images based on image block compression and permutation ordered binary (POB) number system, which includes two parts: share generation, and lossless recovery. In the shares generation stage, the region of interest (ROI) of the original medical image and the image block compression coding algorithm JPEG-LS, are firstly adopted to generate the compressed data, which can use limited storage space to place the data repeatedly. Then, after separating the bit-plane, the compressed data are executed by data reorganization and data encryption respectively on the high-plane and the low-plane. Finally, two authentication bits are extracted by 8-bit pixel of two planes to be inserted into the pixel itself, and then the 10-bit pixel is converted into an 8-bit POB value to produce two shares of ShareH and ShareL, respectively. In the lossless recovery stage, data of placing repeatedly can restore the original image. At the same time, three attacks are carried in the two shares, which contain content cropping, content exchange, and text addition. Some comparisons with other schemes present that the proposed scheme implements a better performance under some criteria. Theoretical analysis and experimental results demonstrate that the original image can be recovered losslessly even if two shares are tampered at the ration more than 50%.

Security of medical images over insecure communication channels using zero-steganography

Secure and private communication has gained much focus on the international arena recently due to the ability of certain organizations to eavesdrop on private communication. This ability is further increased in the Internet of Medical Things scenario where medical images and private data are shared over insecure channels. This work is focused on a new and exciting question related to medical image steganography: is it possible to send hidden information through a cover medium, that is a medical image, without making any alteration to the cover medium in an effective manner? The proposed work has shown that it is possible to send hidden information through a medical image without making any alteration to the original medical image. This solves two problems at the same time. The first problem is that privacy and security approaches that depend on watermarking and steganography usually need some sort of modification to the medical images. The second is the knowledge that there is steganography taking place is completely hidden as no change is made to the medical image. Experiments have shown that the proposed approach under investigation demonstrates effective robustness against attacks like filtering, compression, and addition of noise.

Fingerprint-based robust medical image watermarking in hybrid transform.

To protect the medical images integrity, digital watermark is embedded into the medical images. A non-blind medical image watermarking scheme based on hybrid transform is propounded. In this paper, fingerprint of the patient is used as watermark for better authentication, identifying the original medical image and privacy of the patients. In this scheme, lifting wavelet transform (LWT) and discrete wavelet transform (DWT) are utilized for amplifying the watermarking algorithm. The scaling and embedding factors are calculated adaptively with the help of Local Binary Pattern values of the host medical image to achieve better imperceptibility and robustness for medical images and fingerprint watermark, respectively. Two-level decomposition is done where for the first level LWT is utilized and for the second level decomposition DWT is utilized. At the extraction side, non-blind recovery of fingerprint watermark is performed which is similar to the embedding process. The propounded design is implemented on various medical images like Chest X-ray, CT scan and so on. The propounded design provides better imperceptibility and robustness with the combination of LWT–DWT. The result analysis proves that the proposed fingerprint watermarking scheme has attained best results in terms of robustness and authentication with different medical image attacks. Peak Signal to Noise Ratio and Normalized Correlation Coefficient metrics are used for evaluating the proposed scheme. Furthermore, superior results are obtained when compared to related medical image watermarking schemes.

A novel convolution kernel-based robust watermarking scheme applied in medical image

When the existing digital watermarking algorithm is applied to the medical image, the watermark embedded in the Region of Interest (ROI) will change the properties of the original medical image, causing misdiagnosis. To solve these problems, a robust watermarking method for medical images based on feature extraction of convolution kernels is proposed in this paper. First, the embedded watermark is encrypted by logistic mapping to improve the security of the transmission of patients' personal information on the internet. Then, the convolution kernels are used to perform feature extraction on the original medical image to increase the algorithm's ability to resist geometric attacks. Finally, the zero-watermarking technology is used to organically combine the watermark and the medical image to prevent the region of interest of the medical image from being damaged by the watermark. Experimental results show that the algorithm has good performance on security and robustness.

Multiple Zero-Watermarking of Medical Images for Internet of Medical Things

The Internet of Medical Things (IoMT) plays a vital role in healthcare systems to increase electronic devices’ accuracy, reliability, and productivity. This paper presents fast multiple zero-watermarking methods based on Multi-channel Fractional Legendre Fourier moments (MFrLFMs) for medical image security and copyright protection in IoMT applications without deforming the original medical images. The MFrLFMs are utilized due to their high accuracy, numerical stability, geometric invariances, and high resistance to various attacks. Based on the most significant features generated from MFrLFMs, after scrambling using a two-dimensional Discrete Henon Map, then XORed with binary scrambled watermark to construct owner share. The proposed watermarking method is implemented using a low-cost Raspberry Pi Linux microprocessor, which ensures the suitability of medical devices in the IoMT environment. We evaluated the robustness of the proposed algorithm against different geometric and common signal processing attacks using various medical images. The proposed method gives better BER, NC, and SSIM values than existing methods.

Study of patient specific vascular modeling method considering its zero-pressure state

Medical Images such as MRI and CT can reproduce in-vivo configuration of an artery not in zero-pressure state (ZPS) but in physiological blood pressure. To determine the geometry of a patient-specific artery in zero-pressure state is important for structural analyses of arterial wall. Thus, we propose a new method for ZPS estimation of patient specific artery. The local diameter of an arterial cross section in the zero-pressure state is estimated using our proposing ZPS estimation method by applying negative pressure. The reduction rate of the vessel diameter is obtained for various diameters. Based on the relation between in-vivo diameter and the reduction rate, a medical image-based geometry model is shrunk toward the axial center line of the artery. We found that the ZPS-estimated model could reconstruct the cross section area of the original medical image at the physiological pressure, but some difficulties on the estimation of the bifurcation geometry due to the non-circular cross sectional shape. For better estimation of a bifurcation, the interpolation function should be modified.

Watermarking berbasis Redundant Discrete Wavelet Transform dan Arnold Transform pada Citra Medis

In telemedicine, data transmission in digital medical images and electronic medical records through the internet is vulnerable to various threats of theft and manipulation. Image watermarking is needed to provide authentication and security to medical images. This paper proposes an image watermarking scheme based on Redundant Discrete Wavelet Transform (RDWT) and Discrete Cosine Transform (DCT) with watermark encryption using Arnold transform. First, the original host medical image was decomposed into four subbands using RDWT. Then, DCT is applied to the LH subband of the RDWT. On the other hand, the watermark is scrambled using Arnold transform to ensure identity security. The singular value of the watermarked image is obtained by modifying the singular value of the host image and the watermark. Tests were carried out on different medical images, namely X-ray, MRI, CT, and ultrasound, with a watermark in a proprietary logo. The host medical image is the same size as the watermark image. The result of this study can provide high authentication, imperceptibility and security in medical images, with an average PSNR value of 65.67 dB, SSIM 1, BER 0, NC 1. This scheme is resistant to JPEG compression, noise addition, filtering, image sharpening, image enhancement, geometric operations, motion blur, image sharpening, and histogram equalization.

Robust Zero Watermarking Algorithm for Medical Images Based on Zernike-DCT

Digital medical system not only facilitates the storage and transmission of medical information but also brings information security problems. Aiming at the security of medical images, a robust zero watermarking algorithm for medical images based on Zernike-DCT is proposed. The algorithm first uses a chaotic logic sequence to preprocess and encrypt the watermark, then performs edge detection and Zernike moment processing on the original medical image to get the accurate edge points, and then performs discrete cosine transform (DCT) on them to get the feature vector. Finally, it combines perceptual Hash and zero watermark technology to generate the key to complete the watermark embedding and extraction. The algorithm has good robustness to conventional and geometric attacks, strong antinoise ability, high positioning accuracy, and processing efficiency and is superior to the classical edge detection algorithm in extraction effect. It is a stable and reliable image edge detection algorithm.

Crypto-Watermarking Algorithm Using Weber’s Law and AES: A View to Transfer Safe Medical Image

We propose a novel method for medical image watermarking in the DCT domain using the AES encryption algorithm. First, we decompose the original medical image into subblocks of 8 × 8. Besides, we apply the DCT and the quantization, respectively, to each subblock. However, in the DCT domain, an adequate choice of the DCT coefficients according to the quantization table in the middle frequencies band is performed. After that, we embed the patient’s data into the corresponding medical image. The insertion step is carried out just after the quantization phase. To increase the robustness, we encrypt the watermarked medical images by using the AES algorithm based on chaotic technique. Arnold’s cat map is used to shuffle the pixel values, and a chaotic Henon map is utilized to generate an aleatory sequence for the AES algorithm. The shuffled watermarked image is encrypted using the modified AES algorithm. The constant of Weber is used to choose the suitable visibility factor for embedding a watermark with high robustness. To control identification, after application of attacks, we use the serial turbo code for correction of the watermark to recover the data inserted. The average peak signal-to-noise ratio (PSNR) of the medical images obtained is 61,7769 dB. Experimental results demonstrate the robustness of the proposed schema against various types of attacks.

New geometrically invariant multiple zero-watermarking algorithm for color medical images

The single watermarking algorithm for medical images faced several inherent problems like lack of security. In this paper, a new geometrically invariant multiple zero-watermarking method is proposed to secure medical images. We proposed a novel set of multi-channels shifted Gegenbauer moments of fractional orders (FrMGMs). These moments are used to extract the geometrically invariant features from the color medical images. Then we construct a featured image of the original medical image using the magnitude of the selected precise FrMGMs moments. Finally, we applied a scrambling method to scramble the watermark image and then the exclusive OR operation to the images' feature and scrambled watermark to construct a zero-watermark. Experimental results proved that the proposed approach effectively provided better robustness to various standard attacks and outperformed the existing single-, dual-, & triple-zero-watermarking algorithms for medical images.

Application of 3D-Modeling in Medicine in Preparation for 3D-Printing

3D-modeling in the medical field can be used to create medical models (eg, tissues and human organs) using 3D-printing or used for digital 3D visualization of the necessary structures. Medical 3D-printing is used when the work on prostheses that should perfectly match the patient's anatomy is needed. In addition, thanks to 3D-modeling technology, it is possible to develop peculiar medical tools. It is also possible to perform trial surgeries on 3D-models before the actual operation. There is special software for creating medical 3D-models for further printing. The purpose of this work is to determine the functions of 3D-modeling in preparation for 3D-printing in the process of creating medical models and comparative analysis of software for 3D-modeling used in the medical field. There is a common workflow that can be used to convert volumetric medical imaging data (created by computer tomography (CT), or other imaging techniques) into physical models printed on a 3D-printer. This process is divided into three stages: image segmentation, polygon mesh refinement, and 3D-printing. 3D-modeling programs are used at the stage of polygon mesh refinement. They allow almost unlimited manipulations to refine the mesh to make the model printable. The main manipulations for post-processing of a segmented model using 3D-modeling are: 1) reparation - correction of errors and discrepancies that sometimes occur in the process of segmentation and images export; 2) smoothing - correction of errors that occur during segmentation due to inappropriate resolution of the original medical image via softening by smoothing the surface of the model; 3) adding elements - combining a segmented model with other structures or removing unnecessary parts from the segmentation. As a result of a comparative analysis of 3D-modeling software used in the medical field, it was found that for 3D-modeling can be used software specifically designed for medical 3D-modeling and regular 3D-modeling software. When using regular software, you need third-party software to get the correct model file format. The choice of software depends on the goal: to work with implants and create patient-specific devices, it is possible to use specially designed programs for these purposes, such as Within Medical and Medical Design Studio; if high accuracy is required, it is possible to use D2P created for working with DICOM-images at the image segmentation stage; to achieve fast results, when maintaining of maximum accuracy is not needed, a mobile version of the software, such as Ossa 3D, can be used; common 3D-modeling software, such as Cinema 4D and Blender, can be used to develop peculiar tools and medical equipment.

Security Analysis of Medical Images using ECC over RSA

The security of medical images is very important to maintain the confidentiality and privacy of patient information. Medical practitioners are required to adopt policies for the security of the access of patient’s electronic information. This paper provides the security analysis of the Medical Images based on Elliptic curve cryptography. Elliptic curve cryptography (ECC) is a complex method and involves intense computation which makes it robust for the intruder attack. In this paper, ECC based Medical Image Encryption algorithm is performed on varieties of medical images like MRI, CT scan and X-ray. Security analysis is presented based on Histogram, Entropy, NPCR and UACI measures and compared with traditional method RSA. Histogram after encryption using ECC has uniform distribution which reflects that the information of original medical image is hidden properly. Entropy and NPCR achieved are 7.86 – 7.99 and 99.62 – 99.64 % respectively which are very close to their ideal values. Results reveal that ECC is more powerful and useful for medical image authentication and key distribution. However, it can be used for exchange of secret key rather than encryption.

Medical Image Authentication by Combining Hash Signature and Watermarking Based on Frequency Domains

Medical images are transmitted to other remote places through the E-healthcare system. The protection of medical images is very crucial. Medical images need to be protected against any modification which the attacker may do through the unsecured channel. It is necessary for inspecting the integrity of the ROI (Region of Interest) of the received medical image prior to the adoption of any diagnostic decision to avoid a misdiagnosis. This paper aimed to confirm the integrity and authenticity of medical images by combining the hash signature with the watermarking technique based on the frequency domains. At first, the medical image is divided into ROI and RONI (Region of Non-Interest). The SLT and DCT transformations are employed together to extract the essential features set from ROI and then combined with EPR (Electronic Patient Registration) to produce a watermark, and then it is encoded by a chaotic map with a secret key to provide a signature. On the other hand, the RONI is mapping into randomly subblocks based on the linear system, and then the Signature is embedded in the chosen block using DWT. The hash code of the recovered image and EPR will be compared with the extracted watermark for integrity and authenticity. The experimental results demonstrate the watermark’s robustness against many of the more aggressive and geometric signal processing distortions without affecting the quality of the original medical image. In particular, compared with state-of-the-art technology, the proposed algorithm improves the average NC value larger than 0.90 under all types of attacks.

Blockchain-Based Reversible Data Hiding for Securing Medical Images.

Medical images carry a lot of important information for making a medical diagnosis. Since the medical images need to be communicated frequently to allow timely and accurate diagnosis, it has become a target for malicious attacks. Hence, medical images are protected through encryption algorithms. Recently, reversible data hiding on the encrypted images (RDHEI) schemes are employed to embed private information into the medical images. This allows effective and secure communication, wherein the privately embedded information (e.g., medical records and personal information) is very useful to the medical diagnosis. However, existing RDHEI schemes still suffer from low embedding capacity, which limits their applicability. Besides, such solution still lacks a good mechanism to ensure its integrity and traceability. To resolve these issues, a novel approach based on image block-wise encryption and histogram shifting is proposed to provide more embedding capacity in the encrypted images. The embedding rate is over 0.8 bpp for typical medical images. On top of that, a blockchain-based system for RDHEI is proposed to resolve the traceability. The private information is stored on the blockchain together with the hash value of the original medical image. This allows traceability of all the medical images communicated over the proposed blockchain network.

The Differentiation in Image Post-processing and 3D Reconstruction During Evaluation of Carotid Plaques From MR and CT Data Sources.

Background: Carotid plaque morphology and tissue composition help assess risk stratification of stroke events. Many post-processing image techniques based on CT and MR images have been widely used in related research, such as image segmentation, 3D reconstruction, and computer fluid dynamics. However, the criteria for the 3D numerical model of carotid plaque established by CT and MR angiographic image data remain open to questioning.Method: We accurately duplicated the geometry and simulated it using computer software to make a 3D numerical model. The initial images were obtained by CTA and TOF-MRA. MIMICS (Materialize’s interactive medical image control system) software was used to process the images to generate three-dimensional solid models of blood vessels and plaques. The subsequent output was exported to the ANSYS software to generate finite element simulation results for the further hemodynamic study.Results: The 3D models of carotid plaque of TOF-MRA and CTA were simulated by using computer software. CTA has a high-density resolution for carotid plaque, the boundary of the CTA image is obvious, and the main component of which is a calcified tissue. However, the density resolution of TOF-MRA for the carotid plaque and carotid artery was not as good as that of CTA. The results show that there is a large deviation between the TOF-MRA and CTA 3D model of plaque in the carotid artery due to the unclear recognition of plaque boundary during 3D reconstruction, and this can further affect the simulation results of hemodynamics.Conclusion: In this study, two-dimensional images and three-dimensional models of carotid plaques obtained by two angiographic techniques were compared. The potential of these two imaging methods in clinical diagnosis and fluid dynamics of carotid plaque was evaluated, and the selectivity of image post-processing analysis to original medical image acquisition was revealed.

Technology for High-Sensitivity Analysis of Medical Diagnostic Images.

Control and analysis of small, inaccessible to human vision changes in medical images make it possible to focus on diagnostically important radiological signs important for the correct diagnosis.The aim of the study was to develop information technology facilitating the early diagnosis of diseases using medical images.Materials and MethodsTo control changes in the image, we used its transformation based on solving a particular case of the knapsack problem. The proposed transformation is highly sensitive to any changes in the image and provides the possibility to record deviations visually with high accuracy. Medical images were obtained using cone beam computed tomography.ResultsPractical evaluation of the information technology on tomograms showed the following: the transformed images of healthy bone tissue fragments from different parts of the jaw have similar shapes and nearly the same range of brightness. The transformed image of bone tissue after treatment has a shape close to that of the transformed image of healthy bone tissue. The transformed image of the affected bone tissue has a shape and brightness range differing from the shape and color of the transformed images of healthy bone tissue and bone tissue after treatment. However, transformation of medical images obtained with the Planmeca ProMax 3D Classic device (Finland) allows recording changes that account for less than 0.0001% of the entire image.ConclusionThe proposed method allows human vision to capture changes as small as nearly one pixel in the transformed image, which is impossible with the original medical image. Increasing the color contrast of the transformed medical image makes it possible to reveal the structure of the analyzed medical image fragment. The proposed image transformation method can be used for early diagnosis of diseases and in other fields of medicine.

What Is the Best Way for Patients to Take Photographs of Medical Images (Radiographs, CT, and MRI) Using a Smartphone?

Teleradiology has become one of the most important approaches to virtual clinical diagnosis; its importance has only grown during the coronavirus 2019 pandemic. In developing countries, asking patients to take photographs of their images using a smartphone can facilitate the process and help keep its costs down. However, the images taken by patients with smartphones often are of poor quality, and there is no regulation or standard instruction about how to use smartphones to take photographs of medical examination images effectively. These problems limit the use of smartphones in remote diagnosis and treatment. To formulate a set of guidelines for the most appropriate and effective use of smartphones to capture images (radiographs, CT images, and MR images), and to determine whether these guidelines are more effectively adopted by patients of differing ages and genders. In this prospective study, a set of step-by-step instructions was created with the goal of helping patients take better smartphone photographs of orthopaedic diagnostic images for transfer to telemedicine services. Following the advice of surgeons, experts in smartphone technology, imaging experts, and suggestions from patients, the instructions were modified based on clinical experience and finalized with the goals of simplicity, clarity, and convenience. Potentially eligible patients were older than 18 years, had no cognitive impairment, and used smart phones. Based on that, 256 participants (patients or their relatives and friends) who visited the orthopaedic department of our hospital from June to October 2020 potentially qualified for this study. A total of 11% (29) declined to participate, leaving 89% (227) for analysis here. Their mean age was 36 ± 11 years, 50% were women (113 of 227), and the patient himself/herself represented in 34% (78 of 227) of participants while relatives or friends of patients made up 66% (149 of 227) of the group. In this study, the diagnoses included spinal stenosis (47% [107 of 227]), disc herniation without spinal stenosis (31% [71 of 227]), vertebral fractures (14% [32 of 227]), and other (7% [17 of 227]). Each study participant first took photographs of their original medical images based on their own knowledge of how to use the smartphone camera function; each participant then took pictures of their original images again after receiving our instructional guidance. Three senior spine surgeons (YZ, TQL, TCM) in our hospital analyzed, in a blinded manner, the instructed and uninstructed imaging files based on image clarity (the content of the image is complete, the text information in the image is clearly visible, there is neither reflection nor shadow in the image) and image position (it is not tilted, curled, inverted, or reversed). If either of these conditions was not satisfied, the picture quality was deemed unacceptable; two of three judges' votes determined the outcome. Interobserver reliability with kappa values for the three judges were 0.89 (YZ versus TQL), 0.92 (YZ versus TCM), and 0.90 (TQL versus TCM). In this study, the overall proportion of smartphone medical images deemed satisfactory increased from 40% (91 of 227) for uninstructed participants to 86% (196 of 227) for instructed participants (risk ratio 2.15 [95% CI 1.82 to 2.55]; p<0.001). The proportion of acceptable-quality images in different age groups improved after instruction, except for in patients aged 51 years or older (3 of 17 uninstructed participants versus 8 of 17 instructed participants; RR 2.67 [95% CI 0.85 to 8.37]; p = 0.07). The proportion of acceptable-quality images in both genders improved after instruction, but there was no difference between the genders. We believe our guidelines for patients who wish to take smartphone photographs of their medical images will decrease image transmission cost and facilitate orthopaedic telemedicine consultations. However, it appears that patients older than 50 years are more likely to have difficulty with this approach, and if so, they may benefit from more hands-on assistance from clinic staff or younger relatives or friends. The degree to which our findings are culture-specific should be verified by other studies in other settings, but on the face of it, there is little reason to believe our findings would not generalize to a reasonable degree. Other studies in more heterogeneous populations should also evaluate factors related to levels of educational attainment and wealth differences, but in the meantime, our findings can give clinical teams an idea of which patients may need a little extra assistance. Level II, therapeutic study.

Content-Based Medical Image Retrieval Using Delaunay Triangulation Segmentation Technique

This article presents a novel technique for retrieval of lung images from the collection of medical CT images. The proposed content-based medical image retrieval (CBMIR) technique uses an automated image segmentation technique called Delaunay triangulation (DT) in order to segment lung organ (region of interest) from the original medical image. The proposed method extracts novel and discriminant features from the segmented lung region instead of extracting novel features from the whole original image. For the extraction of shape features, the authors employ edge histogram descriptor (EHD) and geometric moments (GM), and for the extraction of texture features, the authors use gray-level co-occurrence matrix (GLCM) technique. The shape and texture features are combined to form the hybrid feature which is used for retrieval of similar lung images. The proposed method is evaluated using two benchmark datasets of lung CT images. The simulation results prove that the proposed CBMIR framework shows improved performance in terms of retrieval accuracy and retrieval time.

Multilayered highly secure authentic watermarking mechanism for medical applications

Telemedicine is a technology-based substitute for conventional health care facilities. It symbolizes the practice of medication via indirect means. The foremost requirement for such remote healthcare practices is the communication of highly sensitive data over insecure networks, which demands very high protection of different types of health-related information such as Electronic Patient Record (EPR) and Related Medical images. Consequently, there is a stern need to develop algorithms to secure all confidential information for overall well being. This paper is an effort to propose one multilayer protection mechanism, which not only secures the patient’s transcript but also protects associated medical Images. EPR is embedded in the original medical image resulting in a watermarked medical image, which is further hidden in the reference image. But these two relevant records are given multiple strata of security before concealing and exposed to the insecure channel. Compression and Quantum Encryption of EPR are done, which is then embed into LWT transformed Medical Image, followed by scrambling and compression of all the planes of a watermarked medical image before embedding in different bands of LWT (Lifting Wavelet Transform), transformed reference image. This stride of hiding image is mandatory because original and watermarked medical images are very similar, thus prone to attacks. In order to avoid this, an image steganography technique is used to change the watermarked medical image’s visual structure, which is also referred to as visually meaningful encryption, as popular encryption algorithms give noise-like textures that can attract attackers. For authentication on the accession of records, biometric-based detection along with hash algorithm is also incorporated. Overall results (implemented using MATLAB) show that the proposed mechanism provides a highly secure and authentic medical healthcare system.

A lossless self-recovery watermarking scheme with JPEG-LS compression

Due to the rapid development of application for medical images in recent years, the security of medical images becomes more vital. In this paper, a fragile watermarking scheme for medical images is proposed. The proposed scheme first divides the whole medical image into ROI (Regions of Interest) and RONI (Regions of Non-interest). Then the JPEG-LS algorithm is applied to ROI to get the compressed data which will be embedded into the whole medical image. And then, image encryption methods are used to assure the invisibility of the medical image. At last, a POB number system enhances the security of the scheme. With the use of the scheme in this paper, we can judge whether the medical image is tampered or not, and can also recover the original medical images in specific conditions without any loss. Moreover, if the medical image is tampered, we can also localize the tampered regions. Experimental results show that the suggested scheme achieves a better performance on security, invisibility and robustness in comparison to the state-of-the-art methods for medical images.