scholarly journals A DICOM dataset for evaluation of medical image de-identification

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Michael Rutherford ◽  
Seong K. Mun ◽  
Betty Levine ◽  
William Bennett ◽  
Kirk Smith ◽  
...  
Keyword(s):  
Health Information ◽  
National Cancer Institute ◽  
Medical Image ◽  
Cancer Imaging ◽  
Protected Health Information ◽  
Dicom Standard ◽  
Clinical Imaging ◽  
X Ray ◽  
Evaluation Dataset ◽  
Data Elements

AbstractWe developed a DICOM dataset that can be used to evaluate the performance of de-identification algorithms. DICOM objects (a total of 1,693 CT, MRI, PET, and digital X-ray images) were selected from datasets published in the Cancer Imaging Archive (TCIA). Synthetic Protected Health Information (PHI) was generated and inserted into selected DICOM Attributes to mimic typical clinical imaging exams. The DICOM Standard and TCIA curation audit logs guided the insertion of synthetic PHI into standard and non-standard DICOM data elements. A TCIA curation team tested the utility of the evaluation dataset. With this publication, the evaluation dataset (containing synthetic PHI) and de-identified evaluation dataset (the result of TCIA curation) are released on TCIA in advance of a competition, sponsored by the National Cancer Institute (NCI), for algorithmic de-identification of medical image datasets. The competition will use a much larger evaluation dataset constructed in the same manner. This paper describes the creation of the evaluation datasets and guidelines for their use.

scholarly journals Enhancing Ant-Based Algorithms for Medical Image Edge Detection by Admissible Perturbations of Demicontractive Mappings

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 885
Author(s):  
Vasile Berinde ◽  
Cristina Ţicală
Keyword(s):  
Edge Detection ◽  
Medical Image ◽  
Test Functions ◽  
Head Ct ◽  
X Ray ◽  
Numerical Tests ◽  
Image Edge Detection ◽  
Evaluation Of Parameters ◽  
Image Edge ◽  
Demicontractive Mappings

The aim of this paper is to show analytically and empirically how ant-based algorithms for medical image edge detection can be enhanced by using an admissible perturbation of demicontractive operators. We thus complement the results reported in a recent paper by the second author and her collaborators, where they used admissible perturbations of demicontractive mappings as test functions. To illustrate this fact, we first consider some typical properties of demicontractive mappings and of their admissible perturbations and then present some appropriate numerical tests to illustrate the improvement brought by the admissible perturbations of demicontractive mappings when they are taken as test functions in ant-based algorithms for medical image edge detection. The edge detection process reported in our study considers both symmetric (Head CT and Brain CT) and asymmetric (Hand X-ray) medical images. The performance of the algorithm was tested visually with various images and empirically with evaluation of parameters.

A Model for the National Mental Health Information Network

2021 ◽  
Author(s):  
Reza Rabiei ◽  
Farkhonde Aasdi ◽  
Hamid Moghaddasi ◽  
Mahdie Shojaei Baghini
Keyword(s):  
Mental Health ◽  
Health Information ◽  
Accurate Information ◽  
Information Network ◽  
Second Phase ◽  
Five Dimensions ◽  
Mental Health Information ◽  
Architectural Model ◽  
National Mental Health ◽  
Data Elements

Aim: Accurate information can be accessed in a timely manner through the Integrated Mental Health Information Network (MHIN). As Iran has no MHIN, this study was undertaken to propose an architectural model.  Method: This research is a sequential mixed method. The organizational structure and database structure of the MHIN was identified, and the architectural model of the NMHIN was presented in two main phases. In the first phase, a quantitative study was conducted in a scoping review with an extensive review of the background, documents, information, and available resources about the mental health information network. In the second phase, to validate the proposed architecture, the Delphi technique was implemented. Questionnaires were distributed and collected both in person and by e-mail, and finally, the data were analyzed using SPSS-19. Results: The model of national MHIN was provided in five dimensions: MH entities, organizational ownership of databases, data elements of each database, linkage among databases, and exchangeable data elements among the databases. Conclusion: This model can be applied as a suitable platform to effectively and efficiently store and use mental health information. So, the available information can be used for providing mental health services more comfortably and appropriately. The results showed that connecting mental health entities can create a flow of information, coordinate MHIN activities, and improve performance, efficiency, and quality of mental health.

scholarly journals Robust Security for Health Information by ECC with Signature Hash Function in WBAN

2018 ◽  
Vol 11 (1) ◽  
pp. 256
Author(s):  
G. Sridevi Devasena ◽  
S. Kanmani
Keyword(s):  
Health Information ◽  
Key Management ◽  
Hash Function ◽  
Data Communication ◽  
Public Key Cryptography ◽  
Protected Health Information ◽  
Body Area ◽  
Efficient Data ◽  
Body Parameters ◽  
Secure Transmission

<p>Wireless Body Area Networks (WBANs) are fundamental technology in health care that permits the information of a patient’s essential body parameters to be gathered by the sensors. However, the safety and concealment defense of the gathered information is a key uncertain problem. A Hybrid Key Management (HKM) scheme [13] is worked based on Public Key Cryptography (PKC)-authentication scheme. This scheme uses a oneway hash function to construct a Merkle Tree. The PKC method increase the computational complexity and lacking scalability. Additionally, it increases expensive computation, communication costs and delay. To overcome this problem, Robust Security for Protected Health Information by ECC with signature Hash Function in WBAN (RSP) is proposed. The system employs hash-chain based key signature technique to achieve efficient, secure transmission from sensor to user in WBAN. Moreover, Elliptical Curve Cryptography algorithm is used to verifies the authenticate sensor. In addition, it describes the experimental results of the proposed system demonstrate the efficient data communication in a network.</p>

Deep neural models for automated multi-task diagnostic scan management - quality enhancement, view classification and report generation

2021 ◽  
Author(s):  
Karthik K ◽  
Sowmya S Kamath
Keyword(s):  
Data Management ◽  
Language Processing ◽  
Medical Image ◽  
Predictive Analytics ◽  
Image Data ◽  
Neural Models ◽  
Report Generation ◽  
Diagnostic Image ◽  
Intelligent Diagnosis ◽  
X Ray

Abstract The detailed physiological perspectives captured by medical imaging provides actionable insights to doctors to manage comprehensive care of patients. However, the quality of such diagnostic image modalities is often affected by mismanagement of the image capturing process by poorly trained technicians and older/poorly maintained imaging equipment. Further, a patient is often subjected to scanning at different orientations to capture the frontal, lateral and sagittal views of the affected areas. Due to the large volume of diagnostic scans performed at a modern hospital, adequate documentation of such additional perspectives is mostly overlooked, which is also an essential key element of quality diagnostic systems and predictive analytics systems. Another crucial challenge affecting effective medical image data management is that the diagnostic scans are essentially stored as unstructured data, lacking a well-defined processing methodology for enabling intelligent image data management for supporting applications like similar patient retrieval , automated disease prediction etc. One solution is to incorporate automated diagnostic image descriptions of the observation/findings by leveraging computer vision and natural language processing. In this work, we present multi-task neural models capable of addressing these critical challenges. We propose ESRGAN, an image enhancement technique for improving the quality and visualization of medical chest X-ray images, thereby substantially improving the potential for accurate diagnosis, automatic detection and region-of-interest segmentation. We also propose a CNN-based model called ViewNet for predicting the view orientation of the X-ray image and generating a medical report using Xception net, thus facilitating a robust medical image management system for intelligent diagnosis applications. Experimental results are demonstrated using standard metrics like BRISQUE, PIQE and BLEU scores, indicating that the proposed models achieved excellent performance. Further, the proposed deep learning approaches enable diagnosis in a lesser time and their hybrid architecture shows significant potential for supporting many intelligent diagnosis applications.

Maturing an Information Technology Privacy Program

2020 ◽  
pp. 125-143
Author(s):  
Mike Gregory ◽  
Cynthia Roberts
Keyword(s):  
Information Technology ◽  
Health Insurance ◽  
Health Information ◽  
Maturity Model ◽  
Protected Health Information ◽  
Healthcare Organizations ◽  
Privacy And Security ◽  
Security Standards ◽  
Financial Transactions ◽  
Administrative Simplification

The Health Insurance Portability and Accountability Act of 1996 (HIPAA) was initially enacted as an administrative simplification to standardize electronic transmission of common administrative and financial transactions. The program also calls for implementation specifications regarding privacy and security standards to protect the confidentiality and integrity of individually identifiable health information or protected health information. The Affordable Care Act further expanded many of the protective provisions set forth by HIPAA. Since its implementation, healthcare organizations around the nation have invested billions of dollars and have cycled through numerous program attempts aimed at meeting these standards. This chapter reviews the process taken by one organization to review the privacy policy in place utilizing a maturity model, identify deficiencies, and lead change in order to heighten the maturity of the system. The authors conclude with reflection related to effectiveness of the process as well as implications for practice.

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