Sotiris Pavlopoulos

Multiple Image Watermarking Applied to Health Information Management

Information technology advances have brought forth new challenges in healthcare information management, due to the vast amount of medical data that needs to be efficiently stored, retrieved, and distributed, and the increased security threats that explicitly have to be addressed. The paper discusses the perspectives of digital watermarking in a range of medical data management and distribution issues, and proposes a complementary and/or alternative tool that simultaneously addresses medical data protection, archiving, and retrieval, as well as source and data authentication. The scheme imperceptibly embeds in medical images multiple watermarks conveying patients personal and examination data, keywords for information retrieval, the physicians digital signature for authentication, and a reference message for data integrity control. Experimental results indicate the efficiency and transparency of the scheme, which conforms to the strict requirements that apply to regions of diagnostic significance

Secure and efficient health data management through multiple watermarking on medical images

The landscape of healthcare delivery and medical data management has significantly changed over the last years, as a result of the significant advancements in information and communication technologies. Complementary and/or alternative solutions are needed to meet the new challenges, especially regarding security of the widely distributed sensitive medical information. Digital watermarking is a recently established research area with many applications; nevertheless, the potential of this technology to contribute value-added services to medical information management systems has only recently started to be realized by the research community. The paper presents a review of research efforts in the area of medical-oriented watermarking and proposes a wavelet-based multiple watermarking scheme; this scheme aims to address critical health information management issues, including origin and data authentication, protection of sensitive data, and image archiving and retrieval. In accordance with the strict limitations applying to medical images, the scheme allows the definition of a region of interest (ROI) whose diagnostic value is protected, since the only additional information embedded therein aims at integrity control. The robustness of the method is enhanced through a form of hybrid coding, which includes repetitive embedding of BCH encoded watermarks. The experimental results on different medical imaging modalities demonstrate the efficiency and transparency of the watermarking scheme.

A medical image watermarking scheme based on wavelet transform

A wavelet-based multiple watermarking scheme is proposed, which addresses the problems of medical confidentiality protection and both origin and data authentication. The scheme embeds multiple watermarks serving different purposes: a robust watermark containing the doctors digital signature for authentication, a caption watermark with patients personal and examination related data, and a fragile watermark for the purpose of data integrity control. Thus, the proposed added-value tool offers alternatives for different issues associated with medical data management and distribution. The experimental results demonstrate the efficiency of the watermarking scheme, which fulfills the strict requirements concerning the acceptable alterations of medical images.

Fuzzy neural network-based texture analysis of ultrasonic images

The efficacy of a novel fuzzy neural network classifier for the characterization of ultrasonic liver images based on texture analysis techniques is investigated. Classification features are extracted with the use of image texture analysis techniques such as fractal dimension texture analysis, spatial gray-level dependence matrices, gray-level difference statistics, gray-level run-length statistics, and first-order gray-level parameters. These features are fed to a neural network classifier based on geometrical fuzzy sets. Starting from the construction of the Voronoi diagram of the training patterns, an aggregation of Voronoi regions is performed, leading to the identification of larger regions belonging exclusively to one of the pattern classes. The resulting scheme is a constructive algorithm that defines fuzzy clusters of patterns. Based on observations concerning the grade of membership of the training patterns to the created regions, decision probabilities are computed through which the final classification is performed.

SMARTDIAB: A Communication and Information Technology Approach for the Intelligent Monitoring, Management and Follow-up of Type 1 Diabetes Patients

SMARTDIAB is a platform designed to support the monitoring, management, and treatment of patients with type 1 diabetes mellitus (T1DM), by combining state-of-the-art approaches in the fields of database (DB) technologies, communications, simulation algorithms, and data mining. SMARTDIAB consists mainly of two units: 1) the patient unit (PU); and 2) the patient management unit (PMU), which communicate with each other for data exchange. The PMU can be accessed by the PU through the internet using devices, such as PCs/laptops with direct internet access or mobile phones via a Wi-Fi/General Packet Radio Service access network. The PU consists of an insulin pump for subcutaneous insulin infusion to the patient and a continuous glucose measurement system. The aforementioned devices running a user-friendly application gather patients related information and transmit it to the PMU. The PMU consists of a diabetes data management system (DDMS), a decision support system (DSS) that provides risk assessment for long-term diabetes complications, and an insulin infusion advisory system (IIAS), which reside on a Web server. The DDMS can be accessed from both medical personnel and patients, with appropriate security access rights and front-end interfaces. The DDMS, apart from being used for data storage/retrieval, provides also advanced tools for the intelligent processing of the patients data, supporting the physician in decision making, regarding the patients treatment. The IIAS is used to close the loop between the insulin pump and the continuous glucose monitoring system, by providing the pump with the appropriate insulin infusion rate in order to keep the patients glucose levels within predefined limits. The pilot version of the SMARTDIAB has already been implemented, while the platforms evaluation in clinical environment is being in progress.

Using key performance indicators as knowledge-management tools at a regional health-care authority level

The advantages of the introduction of information and communication technologies in the complex health-care sector are already well-known and well-stated in the past. It is, nevertheless, paradoxical that although the medical community has embraced with satisfaction most of the technological discoveries allowing the improvement in patient care, this has not happened when talking about health-care informatics. Taking the above issue of concern, our work proposes an information model for knowledge management (KM) based upon the use of key performance indicators (KPIs) in health-care systems. Based upon the use of the balanced scorecard (BSC) framework (Kaplan/Norton) and quality assurance techniques in health care (Donabedian), this paper is proposing a patient journey centered approach that drives information flow at all levels of the day-to-day process of delivering effective and managed care, toward information assessment and knowledge discovery. In order to persuade health-care decision-makers to assess the added value of KM tools, those should be used to propose new performance measurement and performance management techniques at all levels of a health-care system. The proposed KPIs are forming a complete set of metrics that enable the performance management of a regional health-care system. In addition, the performance framework established is technically applied by the use of state-of-the-art KM tools such as data warehouses and business intelligence information systems. In that sense, the proposed infrastructure is, technologically speaking, an important KM tool that enables knowledge sharing amongst various health-care stakeholders and between different health-care groups. The use of BSC is an enabling framework toward a KM strategy in health care.

Designing and implementing the transition to a fully digital hospital

The increase in the number of examinations performed in modern healthcare institutions in conjunction with the range of imaging modalities available today have resulted in a tremendous increase in the number of medical images generated and has made the need for a dedicated system able to acquire, distribute, and store medical image data very attractive. Within the framework of the Hellenic R&D program, we have designed and implemented a picture archiving and communication system for a high-tech cardiosurgery hospital in Greece. The system is able to handle in a digital form images produced from ultrasound, X-ray angiography, /spl gamma/-camera, chest X-rays, as well as electrocardiogram signals. Based on the adoption of an open architecture highly relying on the DICOM standard, the system enables the smooth transition from the existing procedures to a fully digital operation mode and the integration of all existing medical equipment to the new central archiving system.

Using decision tree algorithms as a basis for a heart sound diagnosis decision support system

Decision trees algorithms were used with promising results in various critical problems, concerning heart sound diagnosis. In general this diagnostic problem can be divided in many sub problems, each one dealing either with one morphological characteristic of the heart sound or with difficult to distinguish heart diseases. The sub problems of the discrimination of aortic stenosis from mitral regurgitation and the discrimination between the second heart sound split, opening snap and third heart sound, are used as case studies. Using signal-processing methods, we extracted the heart sound feature vector. Relevance analysis was performed using the uncertainty coefficient. Then for each heart sound diagnosis sub problem, a specific decision tree (DT) was constructed. decision tree pruning was also investigated. Finally, a general decision support system architecture for the heart sound diagnosis problem, is proposed. The partial diagnosis, given by these DT, can be combined using arbitration rules to give the final diagnosis. These rules can be implemented by another DT, or can be based on different methods, algorithms, or even on expert knowledge. All these can lead to an integrated decision support system architecture for heart sound diagnosis.

A Multiple Watermarking Scheme Applied to Medical Image Management

Digital watermarking is a recently emerged research area, which has the potential of providing alternative/complementary solutions for a number of issues relating to medical data management and distribution. The present paper aims to reveal the perspectives of digital watermarking in health information systems, and proposes a wavelet-based multiple watermarking scheme that addresses the issues of medical data protection, archiving, and retrieval, as well as of origin and data authentication. The scheme applies multiple watermarking in medical images, and embeds in them the physicians digital signature, patients personal and examination data, keywords for image retrieval, and a reference watermark for the purpose of data integrity control. The experimental results demonstrate the efficiency and transparency of the watermarking scheme, which conforms to the strict limitations that apply to regions of diagnostic significance.

Iterative Image Reconstruction for Clinical PET Using Ordered Subsets, Median Root Prior, and a Web-Based Interface

PURPOSE The development, implementation and validation of simple, flexible and efficient iterative image reconstruction (IIR) methods for their take-up in routine clinical positron emission tomography (PET) static or dynamic studies. PROCEDURES The ordered subsets (OS) technique applied for the acceleration of the maximum likelihood expectation maximization (MLEM) IIR algorithm is here extended to include the weighted least-squares (WLS), image space reconstruction algorithm (ISRA) and the space alternating generalized EM (SAGE). The median root prior (MRP) has been successfully applied as a Bayesian regularization to control the noise level in the reconstructed images. All methods are implemented on distributed Pentium systems and tested using simulated PET data from a brain phantom. A Javascript is used for the initiation of the reconstruction. RESULTS Taking into consideration the image quality and the time required for the reconstruction, the MRP-OSEM (ordered subsets expectation maximization) seems to provide best results after four to eight iterations, with four subsets and a MRP coefficient of 0.2-0.4. Iterative reconstruction of the transmission images with OS-acceleration and MRP regularization with subsequent calculation of the attenuation correction factors (ACFs) is shown to effectively remove streak artifacts in the emission images, especially along paths of high attenuation. CONCLUSIONS An efficient implementation using distributed processing principles and a web-based interface allows the reconstruction of one frame (with 63 cross-section slices) from a dynamic determination in few minutes. This work showed that regular PC systems can provide fast execution and produce results in clinically meaningful times. This eradicates the argument of the computational burden of the method that prevented the extensive use of IIR in todays modern PET systems.