Classification and staging of chronic liver disease from multimodal data

Chronic liver disease (CLD) is most of the time an asymptomatic, progressive, and ultimately potentially fatal disease. In this study, an automatic hierarchical procedure to stage CLD using ultrasound images, laboratory tests, and clinical records are described. The first stage of the proposed method, called clinical based classifier (CBC), discriminates healthy from pathologic conditions. When nonhealthy conditions are detected, the method refines the results in three exclusive pathologies in a hierarchical basis: 1) chronic hepatitis; 2) compensated cirrhosis; and 3) decompensated cirrhosis. The features used as well as the classifiers (Bayes, Parzen, support vector machine, and k-nearest neighbor) are optimally selected for each stage. A large multimodal feature database was specifically built for this study containing 30 chronic hepatitis cases, 34 compensated cirrhosis cases, and 36 decompensated cirrhosis cases, all validated after histopathologic analysis by liver biopsy. The CBC classification scheme outperformed the nonhierachical one against all scheme, achieving an overall accuracy of 98.67% for the normal detector, 87.45% for the chronic hepatitis detector, and 95.71% for the cirrhosis detector.

Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm

PURPOSE: Fatty liver disease (FLD) is an increasing prevalent disease that can be reversed if detected early. Ultrasound is the safest and ubiquitous method for identifying FLD. Since expert sonographers are required to accurately interpret the liver ultrasound images, lack of the same will result in interobserver variability. For more objective interpretation, high accuracy, and quick second opinions, computer aided diagnostic (CAD) techniques may be exploited. The purpose of this work is to develop one such CAD technique for accurate classification of normal livers and abnormal livers affected by FLD. METHODS: In this paper, the authors present a CAD technique (called Symtosis) that uses a novel combination of significant features based on the texture, wavelet transform, and higher order spectra of the liver ultrasound images in various supervised learning-based classifiers in order to determine parameters that classify normal and FLD-affected abnormal livers. RESULTS: On evaluating the proposed technique on a database of 58 abnormal and 42 normal liver ultrasound images, the authors were able to achieve a high classification accuracy of 93.3% using the decision tree classifier. CONCLUSIONS: This high accuracy added to the completely automated classification procedure makes the authors' proposed technique highly suitable for clinical deployment and usage.

Chronic liver disease staging classification based on ultrasound, clinical and laboratorial data

In this work the identification and diagnosis of various stages of chronic liver disease is addressed. The classification results of a support vector machine, a decision tree and a k-nearest neighbor classifier are compared. Ultrasound image intensity and textural features are jointly used with clinical and laboratorial data in the staging process. The classifiers training is performed by using a population of 97 patients at six different stages of chronic liver disease and a leave-one-out cross-validation strategy. The best results are obtained using the support vector machine with a radial-basis kernel, with 73.20% of overall accuracy. The good performance of the method is a promising indicator that it can be used, in a non invasive way, to provide reliable information about the chronic liver disease staging.

Diffuse liver disease classification from ultrasound surface characterization, clinical and laboratorial data

In this work liver contour is semi-automatically segmented and quantified in order to help the identification and diagnosis of diffuse liver disease. The features extracted from the liver contour are jointly used with clinical and laboratorial data in the staging process. The classification results of a support vector machine, a Bayesian and a k-nearest neighbor classifier are compared. A population of 88 patients at five different stages of diffuse liver disease and a leave-one-out cross-validation strategy are used in the classification process. The best results are obtained using the k-nearest neighbor classifier, with an overall accuracy of 80.68%. The good performance of the proposed method shows a reliable indicator that can improve the information in the staging of diffuse liver disease.

Machine Learning Algorithms at Myocardial Perfusion Imaging - a Preliminary Study

Introduction: A major focus of data mining process - especially machine learning researches - is to automatically learn to recognize complex patterns and help to take the adequate decisions strictly based on the acquired data. Since imaging techniques like MPI – Myocardial Perfusion Imaging on Nuclear Cardiology, can implicate a huge part of the daily workflow and generate gigabytes of data, there could be advantages on Computerized Analysis of data over Human Analysis: shorter time, homogeneity and consistency, automatic recording of analysis results, relatively inexpensive, etc.Objectives: The aim of this study relates with the evaluation of the efficacy of this methodology on the evaluation of MPI Stress studies and the process of decision taking concerning the continuation – or not – of the evaluation of each patient. It has been pursued has an objective to automatically classify a patient test in one of three groups: “Positive”, “Negative” and “Indeterminate”. “Positive” would directly follow to the Rest test part of the exam, the “Negative” would be directly exempted from continuation and only the “Indeterminate” group would deserve the clinician analysis, so allowing economy of clinician’s effort, increasing workflow fluidity at the technologist’s level and probably sparing time to patients. Methods: WEKA v3.6.2 open source software was used to make a comparative analysis of three WEKA algorithms (“OneR”, “J48” and “Naïve Bayes”) - on a retrospective study using the comparison with correspondent clinical results as reference, signed by nuclear cardiologist experts - on “SPECT Heart Dataset”, available on University of California – Irvine, at the Machine Learning Repository. For evaluation purposes, criteria as “Precision”, “Incorrectly Classified Instances” and “Receiver Operating Characteristics (ROC) Areas” were considered. Results: The interpretation of the data suggests that the Naïve Bayes algorithm has the best performance among the three previously selected algorithms. Conclusions: It is believed - and apparently supported by the findings - that machine learning algorithms could significantly assist, at an intermediary level, on the analysis of scintigraphic data obtained on MPI, namely after Stress acquisition, so eventually increasing efficiency of the entire system and potentially easing both roles of Technologists and Nuclear Cardiologists. In the actual continuation of this study, it is planned to use more patient information and significantly increase the population under study, in order to allow improving system accuracy.

Three-dimensional modeling of tongue during speech using MRI data

The tongue is the most important and dynamic articulator for speech formation, because of its anatomic aspects (particularly, the large volume of this muscular organ comparatively to the surrounding organs of the vocal tract) and also due to the wide range of movements and flexibility that are involved. In speech communication research, a variety of techniques have been used for measuring the three-dimensional vocal tract shapes. More recently, magnetic resonance imaging (MRI) becomes common; mainly, because this technique allows the collection of a set of static and dynamic images that can represent the entire vocal tract along any orientation. Over the years, different anatomical organs of the vocal tract have been modelled; namely, 2D and 3D tongue models, using parametric or statistical modelling procedures. Our aims are to present and describe some 3D reconstructed models from MRI data, for one subject uttering sustained articulations of some typical Portuguese sounds. Thus, we present a 3D database of the tongue obtained by stack combinations with the subject articulating Portuguese vowels. This 3D knowledge of the speech organs could be very important; especially, for clinical purposes (for example, for the assessment of articulatory impairments followed by tongue surgery in speech rehabilitation), and also for a better understanding of acoustic theory in speech formation.

Tomographic imaging with a scanning laser ophtalmoscop

Retinal imaging with a confocal scaning laser Ophthalmoscope (cSLO) involves scanning a small laser beam over the retina and constructing an image from the reflected light. By applying the confocal principle, tomographic images can be produced by measuring a sequence of slices at different depths. However, the thickness of such slices, when compared with the retinal thickness, is too large to give useful 3D retinal images, if no processing is done. In this work, a prototype cSLO was modified in terms hardware and software to give the ability of doing the tomographic measurements with the maximum theoretical axial resolution possible. A model eye was built to test the performance of the system. A novel algorithm has been developed which fits a double Gaussian curve to the axial intensity profiles generated from a stack of images slices. The underlying assumption is that the laser light has mainly been reflected by two structures in the retina, the internal limiting membrane and the retinal pigment epithelium. From the fitted curve topographic images and novel thickness images of the retina can be generated. Deconvolution algorithms have also been developed to improve the axial resolution of the system, using a theoretically predicted cSLO point spread function. The technique was evaluated using measurements made on a model eye, four normal eyes and seven eyes containing retinal pathology. The reproducibility, accuracy and physiological measurements obtained, were compared with available published data, and showed good agreement. The difference in the measurements when using a double rather than a single Gaussian model was also analysed.

An experimental study to compare the interface pressure and experience of healthy participants when lying still for 20 minutes in a supine position on two different imaging surfaces

Introduction: Pressure ulcers are a high cost, high volume issue for health and medical care providers, affecting patients’ recovery and psychological wellbeing. The current research of support surfaces on pressure as a risk factor in the development of pressure ulcers is not relevant to the specialised, controlled environment of the radiological setting. Method: 38 healthy participants aged 19-51 were placed supine on two different imaging surfaces. The XSENSOR pressure mapping system was used to measure the interface pressure. Data was acquired over a time of 20 minutes preceded by 6 minutes settling time to reduce measurement error. Qualitative information regarding participants’ opinion on pain and comfort was recorded using a questionnaire. Data analysis was performed using SPSS 22. Results: Data was collected from 30 participants aged 19 to 51 (mean 25.77, SD 7.72), BMI from 18.7 to 33.6 (mean 24.12, SD 3.29), for two surfaces, following eight participant exclusions due to technical faults. Total average pressure, average pressure for jeopardy areas (head, sacrum & heels) and peak pressure for jeopardy areas were calculated as interface pressure in mmHg. Qualitative data showed that a significant difference in experiences of comfort and pain was found in the jeopardy areas (P<0.05) between the two surfaces. Conclusion: A significant difference is seen in average pressure between the two surfaces. Pain and comfort data also show a significant difference between the surfaces, both findings support the proposal for further investigation into the effects of radiological surfaces as a risk factor for the formation of pressure ulcers.

Constrains on the structure of Maio Island (Cape Verde) by a three-dimensional gravity model: Imaging partially exhumed magma chambers

We propose a 3-D gravity model for the volcanic structure of the island of Maio (Cape Verde archipelago) with the objective of solving some open questions concerning the geometry and depth of the intrusive Central Igneous Complex. A gravity survey was made covering almost the entire surface of the island. The gravity data was inverted through a non-linear 3-D approach which provided a model constructed in a random growth process. The residual Bouguer gravity field shows a single positive anomaly presenting an elliptic shape with a NWSE trending long axis. This Bouguer gravity anomaly is slightly off-centred with the island but its outline is concordant with the surface exposure of the Central Igneous Complex. The gravimetric modelling shows a high-density volume whose centre of mass is about 4500 m deep. With increasing depth, and despite the restricted gravimetric resolution, the horizontal sections of the model suggest the presence of two distinct bodies, whose relative position accounts for the elongated shape of the high positive Bouguer gravity anomaly. These bodies are interpreted as magma chambers whose coeval volcanic counterparts are no longer preserved. The orientation defined by the two bodies is similar to that of other structures known in the southern group of the Cape Verde islands, thus suggesting a possible structural control constraining the location of the plutonic intrusions.

Visual function assessment in medical imaging research

Background - Medical image perception research relies on visual data to study the diagnostic relationship between observers and medical images. A consistent method to assess visual function for participants in medical imaging research has not been developed and represents a significant gap in existing research. Methods - Three visual assessment factors appropriate to observer studies were identified: visual acuity, contrast sensitivity, and stereopsis. A test was designed for each, and 30 radiography observers (mean age 31.6 years) participated in each test. Results - Mean binocular visual acuity for distance was 20/14 for all observers. The difference between observers who did and did not use corrective lenses was not statistically significant (P = .12). All subjects had a normal value for near visual acuity and stereoacuity. Contrast sensitivity was better than population norms. Conclusion - All observers had normal visual function and could participate in medical imaging visual analysis studies. Protocols of evaluation and populations norms are provided. Further studies are necessary to understand fully the relationship between visual performance on tests and diagnostic accuracy in practice.

Correction of spatial distortion in magnetic resonance imaging

Dissertation to Obtain the Degree of Master in Biomedical Engineering

Planning, operations and data handling of planetary science missions

Dissertação para obtenção do Grau de Mestre em Engenharia Física

Predicting Outcome after Cardiopulmonary Arrest in Therapeutic Hypothermia Patients: Clinical, Electrophysiological and Imaging Prognosticators

INTRODUCTION: Predicting outcome in comatose survivors of cardiac arrest is based on data validated by guidelines that were established before the era of therapeutic hypothermia. We sought to evaluate the predictive value of clinical, electrophysiological and imaging data on patients submitted to therapeutic hypothermia. MATERIALS AND METHODS: A retrospective analysis of consecutive patients receiving therapeutic hypothermia during years 2010 and 2011 was made. Neurological examination, somatosensory evoked potentials, auditory evoked potentials, electroencephalography and brain magnetic resonance imaging were obtained during the first 72 hours. Glasgow Outcome Scale at 6 months, dichotomized into bad outcome (grades 1 and 2) and good outcome (grades 3, 4 and 5), was defined as the primary outcome. RESULTS: A total of 26 patients were studied. Absent pupillary light reflex, absent corneal and oculocephalic reflexes, absent N20 responses on evoked potentials and myoclonic status epilepticus showed no false-positives in predicting bad outcome. A malignant electroencephalographic pattern was also associated with a bad outcome (p = 0.05), with no false-positives. Two patients with a good outcome showed motor responses no better than extension (false-positive rate of 25%, p = 0.008) within 72 hours, both of them requiring prolonged sedation. Imaging findings of brain ischemia did not correlate with outcome. DISCUSSION: Absent pupillary, corneal and oculocephalic reflexes, absent N20 responses and a malignant electroencephalographic pattern all remain accurate predictors of poor outcome in cardiac arrest patients submitted to therapeutic hypothermia. CONCLUSION: Prolonged sedation beyond the hypothermia period may confound prediction strength of motor responses.

Geospatial assessment of pest-induced forest damage through the use of UAV-bases NIR imaging and Gi-technology

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

DOTA-based Ga(III) and Gd(III) chelates for medical imaging (PET, SPECT and MRI)

PhD Thesis in Sciences Specialization in Chemistry