Two-Dimensional EspEn: A New Approach to Analyze Image Texture by Irregularity.

Image processing has played a relevant role in various industries, where the main challenge is to extract specific features from images. Specifically, texture characterizes the phenomenon of the occurrence of a pattern along the spatial distribution, taking into account the intensities of the pixels for which it has been applied in classification and segmentation tasks. Therefore, several feature extraction methods have been proposed in recent decades, but few of them rely on entropy, which is a measure of uncertainty. Moreover, entropy algorithms have been little explored in bidimensional data. Nevertheless, there is a growing interest in developing algorithms to solve current limits, since Shannon Entropy does not consider spatial information, and SampEn2D generates unreliable values in small sizes. We introduce a proposed algorithm, EspEn (Espinosa Entropy), to measure the irregularity present in two-dimensional data, where the calculation requires setting the parameters as follows: m (length of square window), r (tolerance threshold), and ρ (percentage of similarity). Three experiments were performed; the first two were on simulated images contaminated with different noise levels. The last experiment was with grayscale images from the Normalized Brodatz Texture database (NBT). First, we compared the performance of EspEn against the entropy of Shannon and SampEn2D. Second, we evaluated the dependence of EspEn on variations of the values of the parameters m, r, and ρ. Third, we evaluated the EspEn algorithm on NBT images. The results revealed that EspEn could discriminate images with different size and degrees of noise. Finally, EspEn provides an alternative algorithm to quantify the irregularity in 2D data; the recommended parameters for better performance are m = 3, r = 20, and ρ = 0.7.

19p13.3 Deletion With Polyotia: A Case Report and Literature Review.

Mutations at chromosome 19 are rare, and reports in the literature are scarce and clinically variable. This chromosome has a high genetic density, and hence a given deletion can cause distinctive effects on body systems and, in addition, result in a characteristic phenotype.  We report the case of a patient who presented with distinctive signs and symptoms such as delayed psychomotor development, severe postnatal delay, dolichocephaly, polyotia, and ocular hypertelorism. Even though all cases with a chromosome 19 deletion do not present in the same way, they still share some clinical manifestations that should be considered, which prompted us to present a summary of the available literature on the subject. Additionally, to our knowledge, this is the first and only case with polyotia in its phenotype to be reported in Colombia to date.

Effects of Tau and Sampling Frequency on the Regularity Analysis of ECG and EEG Signals Using ApEn and SampEn Entropy Estimators.

Electrocardiography (ECG) and electroencephalography (EEG) signals provide clinical information relevant to determine a patient's health status. The nonlinear analysis of ECG and EEG signals allows for discovering characteristics that could not be found with traditional methods based on amplitude and frequency. Approximate entropy (ApEn) and sampling entropy (SampEn) are nonlinear data analysis algorithms that measure the data's regularity, and these are used to classify different electrophysiological signals as normal or pathological. Entropy calculation requires setting the parameters r (tolerance threshold), m (immersion dimension), and τ (time delay), with the last one being related to how the time series is downsampled. In this study, we showed the dependence of ApEn and SampEn on different values of τ, for ECG and EEG signals with different sampling frequencies (Fs), extracted from a digital repository. We considered four values of Fs (128, 256, 384, and 512 Hz for the ECG signals, and 160, 320, 480, and 640 Hz for the EEG signals) and five values of τ (from 1 to 5). We performed parametric and nonparametric statistical tests to confirm that the groups of normal and pathological ECG and EEG signals were significantly different (p < 0.05) for each F and τ value. The separation between the entropy values of regular and irregular signals was variable, demonstrating the dependence of ApEn and SampEn with Fs and τ. For ECG signals, the separation between the conditions was more robust when using SampEn, the lowest value of Fs, and τ larger than 1. For EEG signals, the separation between the conditions was more robust when using SampEn with large values of Fs and τ larger than 1. Therefore, adjusting τ may be convenient for signals that were acquired with different Fs to ensure a reliable clinical classification. Furthermore, it is useful to set τ to values larger than 1 to reduce the computational cost.

A vision-based approach for fall detection using multiple cameras and convolutional neural networks: A case study using the UP-Fall detection dataset.

The automatic recognition of human falls is currently an important topic of research for the computer vision and artificial intelligence communities. In image analysis, it is common to use a vision-based approach for fall detection and classification systems due to the recent exponential increase in the use of cameras. Moreover, deep learning techniques have revolutionized vision-based approaches. These techniques are considered robust and reliable solutions for detection and classification problems, mostly using convolutional neural networks (CNNs). Recently, our research group released a public multimodal dataset for fall detection called the UP-Fall Detection dataset, and studies on modality approaches for fall detection and classification are required. Focusing only on a vision-based approach, in this paper, we present a fall detection system based on a 2D CNN inference method and multiple cameras. This approach analyzes images in fixed time windows and extracts features using an optical flow method that obtains information on the relative motion between two consecutive images. We tested this approach on our public dataset, and the results showed that our proposed multi-vision-based approach detects human falls and achieves an accuracy of 95.64% compared to state-of-the-art methods with a simple CNN network architecture.

Recomendaciones para el uso adecuado de la densitometría ósea en el diagnóstico y tratamiento de la osteoporosis / Guidelines for the appropriate use of dual energy X-ray absorptiometry in the diagnosis and treatment of osteoporosis

La densitometría dual de doble energía de rayos x (DEXA) permite una medida directa y no invasiva de la densidad mineral ósea (BMD). El objetivo del estudio fue proporcionar recomendaciones para el uso apropiado de la densitometría en nuestro hospital para conseguir un diagnóstico certero y un tratamiento adecuado. Una comisión interdisciplinaria realizó una revisión sistemática de la bibliografía. Beneficios, daños y costes: El diagnóstico temprano de la osteoporosis a través de la densitometría ósea minimiza lesiones, mejora la calidad de vida y reduce el coste personal y social asociado a esta patología. Como inconvenientes tiene la exposición a radiaciones ionizantes y el coste. Los inconvenientes y el coste del uso apropiado de la DEXA son mínimos comparados con el coste de la osteoporosis. Recomendaciones: La densidad mineral ósea debe evaluarse sólo cuando sea necesario para el manejo clínico del paciente. DEXA es el mejor método para medir la densidad mineral ósea. Salvo que se sospeche una pérdida de masa ósea acelerada la DEXA no debe repetirse antes de los 2 años para monitorizar tratamientos. Las medidas y los informes de resultados deben estandarizarse (AU)