Edinburgh Postpartum Depression (EPDS) and Breastfeeding Self-Efficacy (BSES) scales are standardized questionnaires to screen for postpartum depression and breastfeeding performance self-perception. On the other hand, Natural Language Processing (NLP) is a machine learning technique that analyses the human language to extract relevant and computer-interpretable information. In this work we proposed the application of an NLP toolchain that includes a typical preprocessing stage and the probabilistic topic modeling performed through the Latent Dirichlet Allocation (LDA) to find out the two most relevant topics within each of six study groups (low, medium, and high scores of BSES and EPDS). Each topic LDA-modeled consisted of 30-word/terms (tokens) which are organized in Venn diagrams, contrasting the mutually exclusive tokens within the low and high scores on each scale. Coherence and log-Perplexity topic modeling performance metrics, were computed. We found that LDA-models have distinguishable tokens between low and high scores of the BSES and EPDS. However, the most remarkable findings were two subset of tokens, one related to newborn care and another to newborn intake, respectively correlated to low and high postpartum depression risk according to EPDS.
Parkinson disease (PD) is a common neurodegenerative pathology, whose accurate diagnosis is still a challenge. PET imaging obtained with [18F]-fluorodeoxyglucose provides a metabolic pattern, highlighting the brain substructures related to PD, thus constituting a valuable diagnosis tool. Besides, it has been reported that incorporating MRI into the analysis enhances the performance of methods aiming to discriminate between healthy subjects and PD patients. In this research, a methodology is proposed that allows: to integrate structural and metabolic imaging information at specific substructures of interest; to spatially align both modalities; to normalize functional images and to extract the adequate biomarkers. Among structural parameters, compacity and tortuosity are proposed, while metabolic biomarkers are extracted from histogram analyses. The random forest algorithm is used for classification and feature selection tasks. The studied populations consisted of nine patients with PD diagnosis and 12 healthy controls. Structural biomarkers showed a small contribution to discriminate between groups, while metabolic biomarkers resulted in 85% (training) to 100% (final test) accuracies. The proposed methodology is promising to diagnose PD and can be extended to other movement disorders.
Computer vision systems are important for capturing environments, for facial recognition and as a way to scan objects for documenting and for manufacturing. One of the current challenges is to scan objects that change dynamically, whether rigid transformations or shape deformations. This paper presents a new system based on an RGB-D camera array, an array which is calibrated by means of a set of equations that relate the distance, angles and resolution of the cameras. The Iterative Closest Point algorithm is proposed for a fine alignment, as well with a process of reconstruction and elimination of noise by means of a Poisson distribution function. The system was exhaustively validated using two forms with different properties. When comparing the obtained result of the scan versus the real models by means of the distance of Hausdorff, errors of no more than 0.0045 mm were obtained. In addition, an experiment is performed by scanning the palm of the hand under deformations and movements. These results show that the system can scan static and non-static and dynamic forms, thereby demonstrating its usefulness for the reconstruction, analysis and manufacture of objects of different classes.
Lower limb prosthesis has the purpose of recovering mobility in amputees, giving autonomy to patients to do several activities. Mobility degree quantification and correct use of the prosthesis is necessary to reduce the risk of desertion. An adequate measurement of movements when patients are walking can help the physiotherapists evaluate the performance. For that reason, this work presents a new tracking method based on the extraction of texture and shape features that feed the retraining Random Forest classifier. The aim is to use a depth camera to track people with lower limb prosthesis when walking between parallel bars. Two experiments were performed with the proposed system: the first one under three patients with lower limb prostheses in order to apply the tracking algorithm. The second was carried out in three healthy control subjects with the purpose of validating the proposed algorithm and comparing the results with a motion capture system (MoCap). In this test the participants carried out two different activities; the results present errors from 3.3 to 4.9 mm according to the root mean square error. This suggests that the system can be used to track human joints under different conditions; however, it is necessary to solve the problem of occlusion artifacts by using human body models or by employing several depth cameras.
Chagas disease (American trypanosomiasis) is an endemic parasitic disease in some areas of Latin America, about 16-18 million people are infected with the etiology agent of Chagas disease , Trypanosoma cruzi, and is transmitted to humans through triatomine insects commonly known as kissing bugs. One of the standard laboratory diagnosis during acute phase of the disease is by direct visualization of the parasite, the most common methods is the visualization in blood smear stained with some colorant. Trypanosoma cruzi uses several strategies to survive in different hosts which involves various morphological, biochemical, and genetic changes. Trypanosoma cruzi displays distinct morphology changes, which have not been fully characterized. The objective of this work is the morphological characterization of shape structures on blood smears. We proposed a high resolution chain code algorithm in bi-dimensional curves, which allows to discretize the contour with greater approximation to its real shape, and consequently obtain features in a objective way.
An indirect method of tissue consistency measurement is proposed, based on intensity and texture features of conventional ultrasound (US) cervix images. Calibration and validation were carried out in five phantoms simulating different cervical firmness, as well as in short and long cervices. Several image features attributed to the histogram, the co–occurrence matrix and the run–length encoding matrix were extracted and analyzed to evaluate their ability to distinguish between degrees of phantoms’ firmness. The most indicative of firmness indices were selected by correlating their values with the phantoms’ elasticities determined through Young’s moduli. Also, a random forest classifier was implemented, allowing to identify the features that contribute the most to class separation between phantoms. Using both tests, six features were selected: mean, standard deviation, entropy, skewness and two RLE-matrix features. A 6–fold cross validation was used to evaluate the model, obtaining a 98.9±0.79% accuracy. Finally, a preliminary case study was conducted upon closed and opened cervical US images, classifying them between both groups using a random forest model, obtaining an 84.34% accuracy. The indicated tests show that intensity and texture features extracted from conventional US images provide indirect and less–invasive information than other methods regarding tissue consistency, and therefore may be used to measure changes in cervical firmness.
Abdominal electrocardiography (AECG) is an indirect method for obtaining a continuous reading of fetal heart rate and is widely used during pregnancy as a method for assessing fetal well-being. Information obtained by AECG is used for early identification of fetal risk and may help in the anticipation of future complications; however, improper interpretation of the AECG recordings, related with inter- and intra-individual variability, may lead to inadequate treatments that can cause the death of the fetus. A set of 33 indices (4 maternal, 5 temporals, 23 time-frequency and 1 non-linear), extracted from AECG recordings and maternal information, were tested with a Random Forest (RF) classification method for the identification of normal fetuses and fetuses with intrauterine growth restriction. Because RFs may perform poorly when confronted with a high number of features compared to the number of training data available, a Genetic Algorithm (GA) was used to select the minimum set of features that improves the outcome of the RF. The accuracy of the RF method using the 33 indices was of 60%. After a run of the GA, the best individual in the last generation had an accuracy value of 85% and reduced the number of used indices from 33 to 11.
Ultrasound (US) images are necessary in obstetrics because they provide the most important clinical parameters for fetal health assessment during the second and third trimesters: head circumference, biparietal diameter, abdominal circumference and femur length. These fetometric indices are helpful for gestational age and fetal weight estimation; they are also helpful for obstetricians to diagnose fetal development abnormalities. However, these indices are obtained manually, which provokes high intra and interobserver variability and lack of repeatability. A fully automatic method to segment and measure femur’s length is presented in this paper. The proposed methodology incorporates texture information and introduces a novel curvature analysis to adequately detect the femur. It consists on pre–processing US images with an anisotropic diffusion filter, followed by morphological operations and thresholding to isolate femur–candidate regions. A normalized metric composed of intensity, length, centroid position and entropy is assigned to each region in order to select the most probable candidate to be femur. This selected region is afterwards thinned to a one–pixel line, whose curvature is analyzed with an angle threshold criterion to accurately locate femur’s extrema. The method was tested on 64 US images (20 taken on the second and 44 on the third trimester of pregnancy); a correlation coefficient of 0.984 and an error of 1.016±2.764 mm were achieved between expert–obtained manual measures and automatically calculated indices. Results are consistent, outperform those reported previously by other authors and show a high correlation with measures obtained by experts; therefore, the developed method is suitable to be adapted for clinical use.
The population is aging as the years pass. There is an increase in life expectancy, but also a decrease in the quality of life for the presence of chronic degenerative diseases. Processing medical images can identify brain changes typical of Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) at an early stage. We propose a new method of segmentation technique using Mean Shift algorithm applying probabilistic maps and Support Vector Machine with Linear and Radial Basis Kernel for segmentation of the hippocampus on Magnetic Resonance Images (MRI). The similarity index of DICE for a 8 control subject was calculated obtaining a mean value of 0:7053±0:0996 using Linear kernel and 0:7275±0:1335 using RBF kernel compared with the manual segmentation made by radiologist.
Three dimensional ultrasound imaging has become the main modality for fetal health diagnostics, with extensive use in fetal brain imaging. According to the fetal position and the stage of development of the fetal skull, a specific plane of image acquisition is required. In most cases for a single plane of acquisition, the image quality is limited by the shadows produced by the skull. In this work a new method for registration of multiple views of 3D ultrasound of the fetal brain is reported, which results in improved imaging of the internal brain structures. In the initial stage, texture, intensity and edge features are used, with a support vector machine (SVM) for the segmentation of the skull in each of the 3D ultrasound views to be registered. The segmentation of each skull is modelled as a set of points with the centre determined with a Gaussian mixture model, where each point is assigned a probability of membership to a Gaussian determined by the posterior probability assigned by the SVM. Our method has shown improved results compared to intensity based registration, with a 52% reduction in the target registration error (TRE), and a 39% reduction in the TRE compared to feature based registration. These are encouraging results for the future development of an automatic method for registration and fusion of multiple views of 3D fetal ultrasound.
Ultrasound (US) images of the fetal brain provide the experts with valuable indicators of the fetal development. However as the skull thickens, it obstructs the transmission of the acoustic waves, which in turn occludes the anatomy behind the thickened fetal skull. A viable option to improve the visibility of the fetal brain, before complete calcification of the skull, is the calculation of a compounded image made of different views of the same anatomical plane. In this work we report a new method for the composition of ultrasound images based on the Weighted Mean of the pixels, from different views, which correspond to each position (x, y) in the final compounded image. A support vector machine (SVM) is used to calculate the weights of each pixel from a different view, based on intensity, entropy and variance features. We present the initial test results of our method on synthetic US images of a head phantom, contaminated with speckle noise; we report the signal to noise ratio (SNR) and the normalized mutual information (NMI), for different number of views (2, 3, and 5), and compare the results against images compounded using the Mean, Root Mean Square (RMS), and Geometrical Mean composition methods. With our scheme we were able to recover the occluded information to increase the NMI from 16% to 26%, representing a 58% improvement.
We present a discrete compactness (DC) index, together with a classification scheme, based both on the size and shape features extracted from brain volumes, to determine different aging stages: healthy controls (HC), mild cognitive impairment (MCI), and Alzheimer’s disease (AD). A set of 30 brain magnetic resonance imaging (MRI) volumes for each group was segmented and two indices were measured for several structures: three-dimensional DC and normalized volumes (NVs). The discrimination power of these indices was determined by means of the area under the curve (AUC) of the receiver operating characteristic, where the proposed compactness index showed an average AUC of 0.7 for HC versus MCI comparison, 0.9 for HC versus AD separation, and 0.75 for MCI versus AD groups. In all cases, this index outperformed the discrimination capability of the NV. Using selected features from the set of DC and NV measures, three support vector machines were optimized and validated for the pairwise separation of the three classes. Our analysis shows classification rates of up to 98.3% between HC and AD, 85% between HC and MCI, and 93.3% for MCI and AD separation. These results outperform those reported in the literature and demonstrate the viability of the proposed morphological indices to classify different aging stages.
KEYWORDS: Brain, Magnetic resonance imaging, Image segmentation, Neuroimaging, Alzheimer's disease, 3D modeling, 3D metrology, Feature extraction, Medical imaging, Pathology
Reported studies describing normal and abnormal aging based on anatomical MRI analysis do not consider morphological brain changes, but only volumetric measures to distinguish among these processes. This work presents a classification scheme, based both on size and shape features extracted from brain volumes, to determine different aging stages: healthy control (HC) adults, mild cognitive impairment (MCI), and Alzheimer's disease (AD). Three support vector machines were optimized and validated for the pair-wise separation of these three classes, using selected features from a set of 3D discrete compactness measures and normalized volumes of several global and local anatomical structures. Our analysis show classification rates of up to 98.3% between HC and AD; of 85% between HC and MCI and of 93.3% for MCI and AD separation. These results outperform those reported in the literature and demonstrate the viability of the proposed morphological indexes to classify different aging stages.
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