Patient Consent Management by a Purpose-Based Consent Model for Electronic Health Record Based on Blockchain Technology.

OBJECTIVES: Currently, patients' consent is essential to use their medical records for various purposes; however, most people give their consent using paper forms and have no control over it. Healthcare organizations also have difficulties in dealing with patient consent. The objective of this research is to develop a system for patients to manage their consent flexibly and for healthcare organizations to obtain patient consent efficiently for a variety of purposes. METHODS: We introduce a new e-consent model, which uses a purpose-based access control scheme; it is implemented by a blockchain system using Hyperledger Fabric. All metadata of patient records, consents, and data access are written immutably on the blockchain and shared among participant organizations. We also created a blockchain chaincode that performs business logic managing patient consent. RESULTS: We developed a prototype and checked business logics with the chaincode by validating doctors' data access with purpose-based consent of patients stored in the blockchain. The results demonstrate that our system provides a fine-grained way of handling medical staff 's access requests with diverse intended purposes for accessing data. In addition, patients can create, update, and withdraw their consents in the blockchain. CONCLUSIONS: Our consent model is a solution for consent management both for patients and healthcare organizations. Our system, as a blockchain-based solution that provides high reliability and availability with transparency and traceability, is expected to be used not only for patient data sharing in hospitals, but also for data donation for biobank research purposes.

Application of Blockchain to Maintaining Patient Records in Electronic Health Record for Enhanced Privacy, Scalability, and Availability.

OBJECTIVES: Electronic Health Record (EHR) systems are increasingly used as an effective method to share patients' records among different hospitals. However, it is still a challenge to access scattered patient data through multiple EHRs. Our goal is to build a system to access patient records easily among EHRs without relying on a centralized supervisory system. METHODS: We apply consortium blockchain to compose a distributed system using Hyperledger Fabric incorporating existent EHRs. Peer nodes hold the same ledger on which the address of a patient record in an EHR is written. Individual patients are identified by unique certificates issued by a local certificate authorities that collaborate with each other in a channel of the network. To protect a patient's privacy, we use a proxy re-encryption scheme when the data are transferred. We designed and implemented various chaincodes to handle business logic agreed by member organizations of the network. RESULTS: We developed a prototype system to implement our concept and tested its performance including chaincode logic. The results demonstrated that our system can be used by doctors to find patient's records and verify patient's consent on access to the data. Patients also can seamlessly receive their past records from other hospitals. The access log is stored transparently and immutably in the ledger that is used for auditing purpose. CONCLUSIONS: Our system is feasible and flexible with scalability and availability in adapting to existing EHRs for strengthening security and privacy in managing patient records. Our research is expected to provide an effective method to integrate dispersed patient records among medical institutions.

Classification of Elastic and Collagen Fibers in H&E Stained Hyperspectral Images.

This study investigates the classification performance of elastic and collagen fibers using H&E stained hyperspectral images. As many as 1200 sample pixels were trained by using Linear discriminant analysis (LDA) and support vector machine (SVM) methods for reduction and classification. The classification result both using LDA and SVM show that H&E stained hyperspectral images performed better classification than H&E stained RGB image by comparing the classification of EVG stained images visually and quantitatively.

Secure biometric image sensor and authentication scheme based on compressed sensing.

It is important to ensure the security of biometric authentication information, because its leakage causes serious risks, such as replay attacks using the stolen biometric data, and also because it is almost impossible to replace raw biometric information. In this paper, we propose a secure biometric authentication scheme that protects such information by employing an optical data ciphering technique based on compressed sensing. The proposed scheme is based on two-factor authentication, the biometric information being supplemented by secret information that is used as a random seed for a cipher key. In this scheme, a biometric image is optically encrypted at the time of image capture, and a pair of restored biometric images for enrollment and verification are verified in the authentication server. If any of the biometric information is exposed to risk, it can be reenrolled by changing the secret information. Through numerical experiments, we confirm that finger vein images can be restored from the compressed sensing measurement data. We also present results that verify the accuracy of the scheme.

Referenceless image quality evaluation for whole slide imaging.

OBJECTIVE: The image quality in whole slide imaging (WSI) is one of the most important issues for the practical use of WSI scanners. In this paper, we proposed an image quality evaluation method for scanned slide images in which no reference image is required. METHODS: While most of the conventional methods for no-reference evaluation only deal with one image degradation at a time, the proposed method is capable of assessing both blur and noise by using an evaluation index which is calculated using the sharpness and noise information of the images in a given training data set by linear regression analysis. The linear regression coefficients can be determined in two ways depending on the purpose of the evaluation. For objective quality evaluation, the coefficients are determined using a reference image with mean square error as the objective value in the analysis. On the other hand, for subjective quality evaluation, the subjective scores given by human observers are used as the objective values in the analysis. The predictive linear regression models for the objective and subjective image quality evaluations, which were constructed using training images, were then used on test data wherein the calculated objective values are construed as the evaluation indices. RESULTS: The results of our experiments confirmed the effectiveness of the proposed image quality evaluation method in both objective and subjective image quality measurements. Finally, we demonstrated the application of the proposed evaluation method to the WSI image quality assessment and automatic rescanning in the WSI scanner.

Hybrid-resolution multispectral imaging using color filter array.

Hybrid-resolution multispectral imaging is a framework to acquire multispectral images through a reconstruction procedure using two types of measurement data with different spatial and spectral resolutions. In this paper, we propose a new method for such a framework on the basis of a full-resolution RGB image and the data obtained from an image sensor with a multispectral filter array (MSFA). In the proposed method, a small region of each image band is reconstructed as a linear combination of RGB images, where the weighting coefficients are determined using MSFA data. The effectiveness of the proposed approach is shown by simulations using spectral images of natural scenes.

Multispectral image enhancement for effective visualization.

Color enhancement of multispectral images is useful to visualize the image's spectral features. Previously, a color enhancement method, which enhances the feature of a specified spectral band without changing the average color distribution, was proposed. However, sometimes the enhanced features are indiscernible or invisible, especially when the enhanced spectrum lies outside the visible range. In this paper, we extended the conventional method for more effective visualization of the spectral features both in visible range and non-visible range. In the proposed method, the user specifies both the spectral band for extracting the spectral feature and the color for visualization respectively, so that the spectral feature is enhanced with arbitrary color. The proposed color enhancement method was applied to different types of multispectral images where its effectiveness to visualize spectral features was verified.

Known plaintext attack on double random phase encoding using fingerprint as key and a method for avoiding the attack.

We have shown that the application of double random phase encoding (DRPE) to biometrics enables the use of biometrics as cipher keys for binary data encryption. However, DRPE is reported to be vulnerable to known-plaintext attacks (KPAs) using a phase recovery algorithm. In this study, we investigated the vulnerability of DRPE using fingerprints as cipher keys to the KPAs. By means of computational experiments, we estimated the encryption key and restored the fingerprint image using the estimated key. Further, we propose a method for avoiding the KPA on the DRPE that employs the phase retrieval algorithm. The proposed method makes the amplitude component of the encrypted image constant in order to prevent the amplitude component of the encrypted image from being used as a clue for phase retrieval. Computational experiments showed that the proposed method not only avoids revealing the cipher key and the fingerprint but also serves as a sufficiently accurate verification system.

Piecewise Wiener estimation for reconstruction of spectral reflectance image by multipoint spectral measurements.

This study proposes a piecewise Wiener estimation method to reconstruct a spectral reflectance image from a three-band image by multipoint spectral information collected simultaneously with image acquisition. A three-band image is divided into several blocks and the spectral estimation is carried out using the Wiener estimation matrix assigned to each block. Each Wiener estimation matrix is constructed on the basis of spectral measurement data. The experimental results show that the proposed method reduces the average estimation error monotonically as the number of spectral measurements increases. In addition, the computational time of the piecewise Wiener estimation costs only severalfold of the computational time of the conventional single-matrix method.

Feasibility study of near-infrared fluorescence tomography using a positron emission tomograph equipped with depth-of-interaction PET detectors.

We are currently developing an imaging system that combines simultaneous positron emission tomography (PET) with near-infrared (NIR) optical tomography, thus supporting two different types of molecular imaging. For this system, we are considering whether to use depth of interaction (DOI) PET detectors as simultaneous detectors of gamma rays and NIR light by changing the original upper reflectors to dichroic mirrors. The DOI-PET detector has very low spatial resolution for NIR light compared to the charge-coupled device cameras that are normally used. However, it is possible to reconstruct images of comparable value from the data acquired by low-resolution devices because the light is scattered by biological tissues and high-resolution devices are not necessarily effective at improving image quality. In this study, we demonstrate the feasibility of 3D NIR fluorescence tomography imaging by employing DOI-PET detectors in computer simulations. In the simulations, we used a 40 mm x 40 mm x 40 mm cubic phantom, a square detector geometry, and an optical diffusion equation to approximate the light propagation. We then evaluated imaging systems for 3D fluorescence tomography with different detector resolutions and excitation light arrangements using singular-value analysis and imaging simulation. We confirmed that the reconstructed images from low-resolution detectors (8 x 8 pixels for an area of 40 mm x 40 mm) are the same as those from high-resolution detectors (16 x 16 pixels for the same area).

Color reproduction from low-SNR multispectral images using spatio-spectral Wiener estimation.

It is possible to capture images with high-fidelity color by using a spectrum-based color image reproduction method that estimates the spectral information of objects from a multispectral image along with information from illumination. When multispectral images do not have a sufficient signal-to-noise ratio (SNR), the accuracy of the spectral and color estimation will be reduced. To improve color estimation accuracy, this paper proposes a spatio-spectral Wiener estimation, which uses spatial correlation as well as spectral correlation. In order to show the effectiveness of the proposed method, computer simulations and an experiment are carried out using a six-channel video camera. As a result, it is assured that the proposed method improves color estimation accuracy and suppresses color noise.

Extraction of acne lesion in acne patients from multispectral images.

In acne treatment, it is important to accurately evaluate the severity of Acne. The acne should be classified into several skin lesions including comedo, reddish papule, pustule, and scar. However, in some cases, a visual detection from RGB image maybe difficult for the proper evaluation of acne skin lesions. This paper proposes an extraction method using the spectral information of the various type of acne skin lesions calculated from the multispectral images (MSI) of the lesions. In the experiment, we showed the possibility of classifying acne lesion types by applying a combination of several linear discriminant functions (LDF's).

Maximum a posteriori estimation of spectral reflectance from color image and multipoint spectral measurements.

Accurate color image reproduction under arbitrary illumination can be realized if the spectral reflectance functions in a scene are obtained. Although multispectral imaging is one of the promising methods to obtain the reflectance of a scene, it is expected to reduce the number of color channels without significant loss of accuracy. This paper presents what we believe to be a new method for estimating spectral reflectance functions from color image and multipoint spectral measurements based on maximum a posteriori (MAP) estimation. Multipoint spectral measurements are utilized as auxiliary information to improve the accuracy of spectral reflectance estimated from image data. Through simulations, it is confirmed that the proposed method improves the estimation accuracy, particularly when a scene includes subjects that belong to various categories.

Experimental evaluation of fingerprint verification system based on double random phase encoding.

We proposed a smart card holder authentication system that combines fingerprint verification with PIN verification by applying a double random phase encoding scheme. In this system, the probability of accurate verification of an authorized individual reduces when the fingerprint is shifted significantly. In this paper, a review of the proposed system is presented and preprocessing for improving the false rejection rate is proposed. In the proposed method, the position difference between two fingerprint images is estimated by using an optimized template for core detection. When the estimated difference exceeds the permissible level, the user inputs the fingerprint again. The effectiveness of the proposed method is confirmed by a computational experiment; its results show that the false rejection rate is improved.

Digital staining for multispectral images of pathological tissue specimens based on combined classification of spectral transmittance.

In this study, the digital transformation (digital staining) of the 16-band multispectral image of a hematoxylin and eosin (HE) stained pathological specimen to its Masson's trichrome (MT) stained counterpart is addressed. The digital staining procedure involves the classification of the various H&E-stained tissue components and then the transformation of their transmittance spectra to their equivalent MT-stained transmittance configurations. Combination of transmittance classifiers were designed to classify the various tissue components found in the multispectral images of an HE-stained specimen, e.g. nucleus, cytoplasm, red blood cell (RBC), fibrosis, etc.; while pseudo-inverse method was used to obtain the transformation matrices that would translate the transmittance spectra of the classified HE-stained multispectral pixels to their MT-stained configurations. To generate the digitally stained image, weighting factors, which were based on the classifiers beliefs, were introduced to the generated transformation matrices. Initial results of our experiments on liver specimens show the viability of multispectral imaging (MSI) to implement a digital staining framework in the pathological context.

Transaxial system models for jPET-D4 image reconstruction.

A high-performance brain PET scanner, jPET-D4, which provides four-layer depth-of-interaction (DOI) information, is being developed to achieve not only high spatial resolution, but also high scanner sensitivity. One technical issue to be dealt with is the data dimensions which increase in proportion to the square of the number of DOI layers. It is, therefore, difficult to apply algebraic or statistical image reconstruction methods directly to DOI-PET, though they improve image quality through accurate system modelling. The process that requires the most computational time and storage space is the calculation of the huge number of system matrix elements. The DOI compression (DOIC) method, which we have previously proposed, reduces data dimensions by a factor of 1/5. In this paper, we propose a transaxial imaging system model optimized for jPET-D4 with the DOIC method. The proposed model assumes that detector response functions (DRFs) are uniform along line-of-responses (LORs). Then each element of the system matrix is calculated as the summed intersection lengths between a pixel and sub-LORs weighted by a value from the DRF look-up-table. 2D numerical simulation results showed that the proposed model cut the calculation time by a factor of several hundred while keeping image quality, compared with the accurate system model. A 3D image reconstruction with the on-the-fly calculation of the system matrix is within the practical limitations by incorporating the proposed model and the DOIC method with one-pass accelerated iterative methods.

Appearance of enhanced tissue features in narrow-band endoscopic imaging.

This study was performed to examine the usefulness of medical endoscopic imaging utilizing narrow-band illumination. The contrast between the vascular pattern and the adjacent mucosa of the underside of the human tongue was measured using five narrow-band illuminations and three broadband illuminations. The results demonstrate that the pathological features of a vascular pattern are dependent on the center wavelength and the bandwidth of illumination. By utilizing narrow-band illumination of 415+/-30 nm, the contrast of the capillary pattern in the superficial layer was markedly improved. This is an important benefit that is difficult to obtain with ordinary broadband illumination. The appearances of capillary patterns on color images were evaluated for three sets of filters. The narrow, band imaging (NBI) filter set (415+/-30 nm, 445+/-30 nm, 500+/-30 nm) was selected to achieve the preferred appearance of the vascular patterns for clinical tests. The results of clinical tests in colonoscopy and esophagoscopy indicated that NBI will be useful as a supporting method for observation of the endoscopic findings of early cancer.

Multiple-discriminant analysis for light-scattering spectroscopy and imaging of two-layered tissue phantoms.

We propose a new method for enhancing the sensitivity of the reflectance spectrum to the scattering feature of the superficial tissue layer. This method is based on multiple-discriminant analysis in the eigensubspace of the spectrum. Considering the application of scattering imaging, we evaluated this method by performing multispectral imaging of two-layered tissue phantoms. A color map converted from the spectral reflectance corresponds well to variations in the size of the scattering in the first layer.

Nonlinear estimation of spectral reflectance based on Gaussian mixture distribution for color image reproduction.

Nonlinear estimation method of spectral reflectance from camera responses is proposed. The proposed method minimizes the mean square error of spectral reflectance when the reflectance can be regarded as a random sequence of Gaussian mixture distribution. In computer simulations, 168 samples of spectral reflectance from a color chart are estimated from their image signals obtained by three- and six-band cameras. It is confirmed that the proposed method improves the accuracy in comparison with the conventional Wiener estimation method.

Fast determination method of cerebral metabolic rate images of glucose using dynamic PET data.

Measurement of the rate constant parameters of the tracer 18F-FDG, used with positron emission tomography (PET) to determine the cerebral metabolic rate of glucose (CMRGlc), can provide a clear understanding of the physiological processes in the human brain. At present, the methods that are widely used to obtain CMRGlc, such as nonlinear least squares (NLS), first require the reconstruction of a time sequence of images. The reconstruction of these images requires a large amount of computation, especially in 3D Depth-of Interaction PET (DOI-PET), and the nonlinear based methods also require a large amount of computation. In this paper, we propose a fast parametric image reconstruction method for 18F-FDG dynamic PET studies. In our method a deconvolving process is first employed on the time sequential projection data to remove the effect of the measured plasma time activity. The deconvolved terms are integrated over three different time intervals and the parameters for determining CMRGlc can be obtained analytically. Our method requires only three reconstructing processes and reduces the computational demand to estimate CMRGlc. The algorithm performance is evaluated using a digital phantom and a clinical data set and the results show that the proposed method produces images with the same or better quality as the images from the NLS method, with much less computation compared to the NLS method.