Filipe Janela

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In this paper, we propose a method based on a generalization of the multifractal detrended fluctuation analysis (MF-DFA) to the two-dimensionality, for the analysis of breast medical images, particularly grey scale images of mammograms. The existent generalization has been suitably applied in synthetic multifractal surfaces. However, when it is applied to(More)
This study proposes a methodology to support coding professionals in assigning ICD-9-CM codes to inpatient episodes. This subject has been predominantly addressed through the use of natural language processing methods, which show limited generalizability. To surpass this issue, this paper proposes a methodology entailing an adaptive data processing method(More)
Dynamic contrast-enhanced magnetic resonance (DCE-MR) of the breast is especially robust for the diagnosis of cancer in high-risk women due to its high sensitivity. Its specificity may be, however, compromised since several benign masses take up contrast agent as malignant lesions do. In this paper, we propose a novel method of 3D multifractal analysis to(More)
The recent advances in Magnetic Resonance Imaging gradient, regarding strength and computation speed, led to the development of Echo-Planar Imaging pulse-sequences with faster acquisition times. This kind of sequence is used in functional MRI and diffusion-weighted Magnetic Resonance Imaging and it presents more distortions than slower sequences. This work(More)
The aim of this work is to propose a multifractal analysis method for Multifractal Detrended Fluctuation analysis (MF-DFA) of Blood Oxygen Level Dependent (BOLD) functional Magnetic Resonance Imaging (fMRI). The fMRI signals exhibit a 1/f power spectrum, hence their structure has self-similarity and long memory, being usually successfully analyzed by(More)
This work proposes a multifractal analysis of the time series derived from ASL fMRI (Arterial Spin Labeling functional Magnetic Resonance Imaging) to detect brain activated regions in response to an unknown stimulus. In contrast to standard model-based activation analysis, no prior knowledge of the expected haemodynamic response has to be assumed for(More)
BACKGROUND EHR systems have high potential to improve healthcare delivery and management. Although structured EHR data generates information in machine-readable formats, their use for decision support still poses technical challenges for researchers due to the need to preprocess and convert data into a matrix format. During our research, we observed that(More)