OBJECTIVES: Long-term breast cancer survivors (BCS) constitute a complex group of patients, whose number is estimated to continue rising, such that, a dedicated long-term clinical follow-up is necessary. MATERIALS AND METHODS: A dynamic time warping-based unsupervised clustering methodology is presented in this article for the identification of temporal patterns in the care trajectories of 6214 female BCS of a large longitudinal retrospective cohort of Spain. The extracted care-transition patterns are graphically represented using directed network diagrams with aggregated patient and time information. A control group consisting of 12 412 females without breast cancer is also used for comparison. RESULTS: The use of radiology and hospital admission are explored as patterns of special interest. In the generated networks, a more intense and complex use of certain healthcare services (eg, radiology, outpatient care, hospital admission) is shown and quantified for the BCS. Higher mortality rates and numbers of comorbidities are observed in various transitions and compared with non-breast cancer. It is also demonstrated how a wealth of patient and time information can be revealed from individual service transitions. DISCUSSION: The presented methodology permits the identification and descriptive visualization of temporal patterns of the usage of healthcare services by the BCS, that otherwise would remain hidden in the trajectories. CONCLUSION: The results could provide the basis for better understanding the BCS' circulation through the health system, with a view to more efficiently predicting their forthcoming needs and thus designing more effective personalized survivorship care plans.

MOTIVATION: Incorporating the temporal dimension into multimorbidity studies has shown to be crucial for achieving a better understanding of the disease associations. Furthermore, due to the multifactorial nature of human disease, exploring disease associations from different perspectives can provide a holistic view to support the study of their aetiology. RESULTS: In this work, a temporal systems-medicine approach is proposed for identifying time-dependent multimorbidity patterns from patient disease trajectories, by integrating data from electronic health records with genetic and phenotypic information. Specifically, the disease trajectories are clustered using an unsupervised algorithm based on dynamic time warping and three disease similarity metrics: clinical, genetic and phenotypic. An evaluation method is also presented for quantitatively assessing, in the different disease spaces, both the cluster homogeneity and the respective similarities between the associated diseases within individual trajectories. The latter can facilitate exploring the origin(s) in the identified disease patterns. The proposed integrative methodology can be applied to any longitudinal cohort and disease of interest. In this article, prostate cancer is selected as a use case of medical interest to demonstrate, for the first time, the identification of temporal disease multimorbidities in different disease spaces. AVAILABILITY AND IMPLEMENTATION: https://gitlab.com/agiannoula/diseasetrajectories. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: The study of comorbidities is a major priority due to their impact on life expectancy, quality of life and healthcare cost. The availability of electronic health records (EHRs) for data mining offers the opportunity to discover disease associations and comorbidity patterns from the clinical history of patients gathered during routine medical care. This opens the need for analytical tools for detection of disease comorbidities, including the investigation of their underlying genetic basis. Results: We present comoRbidity, an R package aimed at providing a systematic and comprehensive analysis of disease comorbidities from both the clinical and molecular perspectives. comoRbidity leverages from (i) user provided clinical data from EHR databases (the clinical comorbidity analysis) and (ii) genotype-phenotype information of the diseases under study (the molecular comorbidity analysis) for a comprehensive analysis of disease comorbidities. The clinical comorbidity analysis enables identifying significant disease comorbidities from clinical data, including sex and age stratification and temporal directionality analyses, while the molecular comorbidity analysis supports the generation of hypothesis on the underlying mechanisms of the disease comorbidities by exploring shared genes among disorders. The open-source comoRbidity package is a software tool aimed at expediting the integrative analysis of disease comorbidities by incorporating several analytical and visualization functions. Availability and implementation: https://bitbucket.org/ibi_group/comorbidity. Supplementary information: Supplementary data are available at Bioinformatics online.

In this paper, the blind restoration of a scene is investigated, when multiple degraded (blurred and noisy) acquisitions are available. An adaptive filtering technique is proposed, where the distorted images are filtered, classified and then fused based upon the classification decisions. Finite normal-density mixture (FNM) models are used to model the filtered outputs at each iteration. For simplicity, fixed number of Gaussian components (classes) is, initially, considered for each degraded frame and the selection of the optimal number of classes is performed according to the global relative entropy criterion. However, there exist cases where dynamically varying FNM models should be considered, where the optimal number of classes is selected according to the Akaike information criterion. The iterative application of classification and fusion, followed by optimal adaptive filtering, converges to a global enhanced representation of the original scene in only a few iterations. The proposed restoration method does not require knowledge of the point-spread-function support size or exact alignment of the acquired frames. Simulation results on synthetic and real data, using both fixed and dynamically varying FNM models, demonstrate its efficiency under both noisy and noise-free conditions.

The dynamic acoustic radiation force produced by two confocal ultrasound beams, can be used for imaging the acoustic properties of tissue. In this paper we investigate how nonlinear propagation in an attenuating medium can affect image quality. Specifically, the effects of the second harmonic on the image formation process are studied, by means of the system point-spread-function (PSF). Incorporation of tissue nonlinearities in the model and extraction of higher-harmonic information, indicates that a more localized radiation force distribution and reduced sidelobe effects can be achieved and this could improve image resolution.

In this paper, the case where multiple degraded (blurred and noisy) acquisitions of the same scene are available, is investigated. An efficient iterative deconvolution system is introduced, where the multisensor images are fused, based on the classification of each image in a predetermined number of classes that represent the components of a finite mixture of normal densities (FMN). The EM algorithm is utilized for the learning of the FMN model. The recursive employment of the classification and fusion processes, followed by an optimized adaptive filtering, converges to a global enhanced version of the original scene in only a few iterations. Experimental results establish the efficiency of the proposed scheme.

Various watermarking techniques have been proposed so far, aiming at the copyright protection of audio signals. Little effort has been made, however, in taking under consideration the spectrum of the watermark sequence itself and exploiting its frequency properties. An enhanced audio watermarking technique, based on correlation detection, is introduced in this paper, where high-frequency chaotic watermarks are multiplicatively embedded in the low frequencies of the DFT domain. A series of experiments have been conducted to demonstrate both detection reliability and robustness against attacks.

In this paper, the case where a low-resolution image is initially watermarked and the watermark is afterwards detected on a noisy interpolated version of the watermarked image is investigated, using a correlation detector. Polyphase decomposition is utilized at the detector side in order to enable the flexible formation of a fused image, which is appropriate for watermark detection. The optimal fused correlator, obtained by combining information from different image components, is derived through a statistical analysis of the correlation detector properties and employment of Lagrange multipliers. It is shown that it is always preferable to perform detection on a fused image rather than the original image. Experimental results establish the efficiency of the proposed scheme.

Considerable research has been conducted lately on attempting to integrate the dual processes of signal compression and perceptual coding, in order to achieve high signal quality at low bit rates, while ensuring, at the same time, copyright protection of the transmitted digital media. An unconventional data hiding technique leading to compressed forms of source video signals, is introduced in this paper, where both the audio information and the chrominance (I and Q) components of the video frames are hidden in the wavelet transform coefficients of the luminance (Y) component, on a frame-by-frame basis. Furthermore, the proposed scheme allows for a subsequent JPEG compression of each frame for additional bitrate reduction, while maintaining satisfying quality of the reconstructed audio and chrominance information.

The performance of watermarking schemes based on correlation detection is closely related to the frequency characteristics of the watermark sequence. In order to improve both detection reliability and robustness against attacks, embedding of watermarks with high-frequency spectrum, in the low frequencies of the DFT domain, is introduced in this paper and theoretical analysis of correlation based watermarking techniques with multiplicative embedding is performed. The proposed watermarking framework is successfully applied to audio signals, demonstrating its superiority with respect to both robustness and inaudibility. Experiments are conducted, in order to verify the validity of the theoretical analysis results.

A blind watermarking method for the copyright protection of sets of polygonal lines in vector graphics images and GIS data (elevation contour maps) is presented. The paper focuses mainly on the use of simple fusion rules for combining the detector outputs from each polygonal line in order to come up with a global detection result. Experimental comparison of the various fusion methods using both synthetic and real data (elevation maps) is provided.