Julien Montagner

Comparison of some reversible watermarking methods in application to medical images

Several reversible watermarking schemes have been proposed for images of sensitive content, like medical imaging, for which any modification may affect their interpretation. In this work, we distinguish these methods according to the way watermark insertion is conducted: additive and substitutive. Some of these approaches have been tested on different sets of medical images issued from three distinct modalities: Magnetic Resonance Images, Positron Emission Tomography and Ultrasound Imaging. Comparison analysis has been conducted with respect to several aspects including data hiding capacity and image quality preservation. Experimental results show different limitations which depend on the watermark approach but also on image modality specificities.

Development framework for a patient-centered record

Patient records have been developed to support the physician-oriented medical activity scheme. One recommended yet rarely studied alternative, expected to improve healthcare, is the patient-centered record. We propose a development framework for such record, which includes domain-specific database models at the conceptual level, analyzing the fundamental role of complementary information destined to ensure proper patient understanding of related clinical situations. A patient-centered awareness field study of user requirements and medical workflow was carried out in three medical services and two technical units to identify the most relevant elements of the framework, and compared to the definitions of a theoretical approach. Three core data models - centered on the patient, medical personnel, and complementary patient information, corresponding to the determined set of entities, information exchanges and actors roles, constitute the technical recommendations of the development framework. An open source proof of concept prototype was developed to show the model feasibility. The resulting patient-centered record development framework implies particular medical personnel contributions to supply complementary information.

Medical image integrity control seeking into the detail of the tampering

In this paper, we propose a system which aims at verifying integrity of medical images. It not only detects and localizes alterations, but also seeks into the details of the image modification to understand what occurred. For that latter purpose, we developed an image signature which allows our system to approximate modifications by a simple model, a door function of similar dimensions. This signature is partly based on a linear combination of the DCT coefficients of pixel blocks. Protection data is attached to the image by watermarking. Whence, image integrity verification is conducted by comparing this embedded data to the recomputed one from the observed image. Experimental results with malicious image modification illustrate the overall performances of our system.

Quality Analysis of Sensors Data for Personal Health Records on Mobile Devices

Data collected by multiple physiological sensors are being increasingly used for wellness monitoring or disease management, within a pervasiveness context facilitated by the massive use of mobile devices. These abundant complementary raw data are challenging to understand and process, because of their voluminous and heterogeneous nature, as well as the data quality issues that could impede their utilization. This chapter examines the main data quality questions concerning six frequently used physiological sensors—glucometer, scale, blood pressure meter, heart rate meter, pedometer, and thermometer—as well as patient observations that may be associated to a given set of measurements. We discuss specific details that are either overlooked in the literature or avoided by data exploration and information extraction algorithms, but have significant importance to properly preprocess these data. Making use of different types of formalized knowledge, according to the characteristics of physiological measurement devices, relevant data handled by a Personal Health Record on a mobile device, are evaluated from a data quality perspective, considering data deficiencies factors, consequences, and reasons. We propose a general scheme for sensors data quality characterization adapted to a pervasive scenario.

Information quality measurement of medical encoding support based on usability

Medical encoding support systems for diagnoses and medical procedures are an emerging technology that begins to play a key role in billing, reimbursement, and health policies decisions. A significant problem to exploit these systems is how to measure the appropriateness of any automatically generated list of codes, in terms of fitness for use, i.e. their quality. Until now, only information retrieval performance measurements have been applied to estimate the accuracy of codes lists as quality indicator. Such measurements do not give the value of codes lists for practical medical encoding, and cannot be used to globally compare the quality of multiple codes lists. This paper defines and validates a new encoding information quality measure that addresses the problem of measuring medical codes lists quality. It is based on a usability study of how expert coders and physicians apply computer-assisted medical encoding. The proposed measure, named ADN, evaluates codes Accuracy, Dispersion and Noise, and is adapted to the variable length and content of generated codes lists, coping with limitations of previous measures. According to the ADN measure, the information quality of a codes list is fully represented by a single point, within a suitably constrained feature space. Using one scheme, our approach is reliable to measure and compare the information quality of hundreds of codes lists, showing their practical value for medical encoding. Its pertinence is demonstrated by simulation and application to real data corresponding to 502 inpatient stays in four clinic departments. Results are compared to the consensus of three expert coders who also coded this anonymized database of discharge summaries, and to five information retrieval measures. Information quality assessment applying the ADN measure showed the degree of encoding-support system variability from one clinic department to another, providing a global evaluation of quality measurement trends.

Information fusion for diagnosis coding support

All patient-related medical information during a hospital stay in France, has to be collected and coded in the compilation of medical units discharge documents, according to a standardized approach. The process of describing a patient disease in terms of appropriate diagnostic codes is nevertheless, a non-intuitive operation for the physician. As a consequence, coding errors, inaccuracies and missing data are frequent, leading to potentially severe economical upshots. A coding support system developed to improve medical coding results, integrates three information processing methodologies, using the outputs from various Hospital Information System applications. Each methodology generates partial heterogeneous information, with considerable semantic variety. In order to properly synthesize these outputs, information fusion is required to produce enriched contextualized information, presented to the physician as an ordered list of suggested codes. This paper explores two information fusion approaches: voting system and possibilistic. Both methods are tested on a database of 1,000 discharge summaries, to show the interest of information fusion in this context. Results show that fusion methods perform better in most of the cases than partial information extraction methods.

An iterative possibilistic image segmentation system: Application to breast cancer detection

A novel approach for digital mammograms segmentation is proposed. This approach aims to segment the mammograms using an iterative fusion process of information obtained from multiple sources of knowledge (contextual, image processing algorithm, a priori knowledge, etc). Initial Fuzzy Membership Maps (IFMMs) of different thematic classes are first estimated using available information. These IFMMs are then interpreted as Possibility Distribution Maps (PDMs), which represent the possibility for each analyzed pixel to be one of the different thematic classes in the considered image, these possibility values are then iteratively updated using contextual (spatial) information. An additional class called “Rejection” is used to manage ambiguity and to delay the segmentation operation until the establishment of high level possibility degrees for these pixels. The segmentation results are given as a thematic map as well as a confidence curve evaluating the segmentation result quality.