Virtual Clinical Trials in PET Imaging for Improved Diagnosis of and Evaluation of Therapies for Cancer

Thesis (Ph.D.)--University of Washington, 2016-08

An analysis of the effects of preferential trade policies through the estimation of quantitative models: the case of Portugal.

Doutoramento em Economia.

Improvements on ICA mixture models for image pre-processing and segmentation

Today several different unsupervised classification algorithms are commonly used to cluster similar patterns in a data set based only on its statistical properties. Specially in image data applications, self-organizing methods for unsupervised classification have been successfully applied for clustering pixels or group of pixels in order to perform segmentation tasks. The first important contribution of this paper refers to the development of a self-organizing method for data classification, named Enhanced Independent Component Analysis Mixture Model (EICAMM), which was built by proposing some modifications in the Independent Component Analysis Mixture Model (ICAMM). Such improvements were proposed by considering some of the model limitations as well as by analyzing how it should be improved in order to become more efficient. Moreover, a pre-processing methodology was also proposed, which is based on combining the Sparse Code Shrinkage (SCS) for image denoising and the Sobel edge detector. In the experiments of this work, the EICAMM and other self-organizing models were applied for segmenting images in their original and pre-processed versions. A comparative analysis showed satisfactory and competitive image segmentation results obtained by the proposals presented herein. (C) 2008 Published by Elsevier B.V.

Morphological analysis of pitting corrosion in AISI 310S austenitic stainless steel after salt spray exposition

This paper aims to study evolution of increase, distribution and classification of pits in 310S austenitic stainless steels obtained in the state as-received and heat-treated under different exposure times in saline. This work applicability has been based on a technique development for morphologic characterization of localized corrosion associated with description aspects of shapes, size and population-specific parameters. Methodology has been consisted in the following steps: specimens preparation, corrosion tests via salt spray in different conditions, microstructural analysis, pits profiles analysis and images analysis, digital processing and image analysis in order to characterize the pits distribution, morphology and size. Results obtained in digital processing and profiles image analysis have been subjected to statistical analysis using median as parameter in the alloy as received and treated. The alloy as received displays the following morphology: hemispheric pits> transition region A> transition region B> irregular> conic. The pits amount in the treated alloy at each exposure time is: transition region B> hemispherical> transition region A> conic> irregular.

HYBRID AND INCREMENTAL FUZZY LEARNING FOR HUMAN SKIN DETECTION

In this paper, a framework for detection of human skin in digital images is proposed. This framework is composed of a training phase and a detection phase. A skin class model is learned during the training phase by processing several training images in a hybrid and incremental fuzzy learning scheme. This scheme combines unsupervised-and supervised-learning: unsupervised, by fuzzy clustering, to obtain clusters of color groups from training images; and supervised to select groups that represent skin color. At the end of the training phase, aggregation operators are used to provide combinations of selected groups into a skin model. In the detection phase, the learned skin model is used to detect human skin in an efficient way. Experimental results show robust and accurate human skin detection performed by the proposed framework.

A chemometric study on the analgesic activity of cannabinoid compounds using SDA, KNN and SIMCA methods

The supervised pattern recognition methods K-Nearest Neighbors (KNN), stepwise discriminant analysis (SDA), and soft independent modelling of class analogy (SIMCA) were employed in this work with the aim to investigate the relationship between the molecular structure of 27 cannabinoid compounds and their analgesic activity. Previous analyses using two unsupervised pattern recognition methods (PCA-principal component analysis and HCA-hierarchical cluster analysis) were performed and five descriptors were selected as the most relevants for the analgesic activity of the compounds studied: R (3) (charge density on substituent at position C(3)), Q (1) (charge on atom C(1)), A (surface area), log P (logarithm of the partition coefficient) and MR (molecular refractivity). The supervised pattern recognition methods (SDA, KNN, and SIMCA) were employed in order to construct a reliable model that can be able to predict the analgesic activity of new cannabinoid compounds and to validate our previous study. The results obtained using the SDA, KNN, and SIMCA methods agree perfectly with our previous model. Comparing the SDA, KNN, and SIMCA results with the PCA and HCA ones we could notice that all multivariate statistical methods classified the cannabinoid compounds studied in three groups exactly in the same way: active, moderately active, and inactive.

Inter-subband redundancy prediction using neural network for video coding

High performance video codec is mandatory for multimedia applications such as video-on-demand and video conferencing. Recent research has proposed numerous video coding techniques to meet the requirement in bandwidth, delay, loss and Quality-of-Service (QoS). In this paper, we present our investigations on inter-subband self-similarity within the wavelet-decomposed video frames using neural networks, and study the performance of applying the spatial network model to all video frames over time. The goal of our proposed method is to restore the highest perceptual quality for video transmitted over a highly congested network. Our contributions in this paper are: (1) A new coding model with neural network based, inter-subband redundancy (ISR) prediction for video coding using wavelet (2) The performance of 1D and 2D ISR prediction, including multiple levels of wavelet decompositions. Our result shows a short-term quality enhancement may be obtained using both 1D and 2D ISR prediction.

Evaluating SVM and MLDA in the extraction of discriminant regions for mental state prediction

Pattern recognition methods have been successfully applied in several functional neuroimaging studies. These methods can be used to infer cognitive states, so-called brain decoding. Using such approaches, it is possible to predict the mental state of a subject or a stimulus class by analyzing the spatial distribution of neural responses. In addition it is possible to identify the regions of the brain containing the information that underlies the classification. The Support Vector Machine (SVM) is one of the most popular methods used to carry out this type of analysis. The aim of the current study is the evaluation of SVM and Maximum uncertainty Linear Discrimination Analysis (MLDA) in extracting the voxels containing discriminative information for the prediction of mental states. The comparison has been carried out using fMRI data from 41 healthy control subjects who participated in two experiments, one involving visual-auditory stimulation and the other based on bimanual fingertapping sequences. The results suggest that MLDA uses significantly more voxels containing discriminative information (related to different experimental conditions) to classify the data. On the other hand, SVM is more parsimonious and uses less voxels to achieve similar classification accuracies. In conclusion, MLDA is mostly focused on extracting all discriminative information available, while SVM extracts the information which is sufficient for classification. (C) 2009 Elsevier Inc. All rights reserved.

Hyperplane navigation: A method to set individual scores in fMRI group datasets

Recent studies have demonstrated that spatial patterns of fMRI BOLD activity distribution over the brain may be used to classify different groups or mental states. These studies are based on the application of advanced pattern recognition approaches and multivariate statistical classifiers. Most published articles in this field are focused on improving the accuracy rates and many approaches have been proposed to accomplish this task. Nevertheless, a point inherent to most machine learning methods (and still relatively unexplored in neuroimaging) is how the discriminative information can be used to characterize groups and their differences. In this work, we introduce the Maximum Uncertainty Linear Discrimination Analysis (MLDA) and show how it can be applied to infer groups` patterns by discriminant hyperplane navigation. In addition, we show that it naturally defines a behavioral score, i.e., an index quantifying the distance between the states of a subject from predefined groups. We validate and illustrate this approach using a motor block design fMRI experiment data with 35 subjects. (C) 2008 Elsevier Inc. All rights reserved.

Classification System of Raman Spectra using Cluster Analysis to Diagnose Coronary Artery Lesions

The traditional methods employed to detect atherosclerotic lesions allow for the identification of lesions; however, they do not provide specific characterization of the lesion`s biochemistry. Currently, Raman spectroscopy techniques are widely used as a characterization method for unknown substances, which makes this technique very important for detecting atherosclerotic lesions. The spectral interpretation is based on the analysis of frequency peaks present in the signal; however, spectra obtained from the same substance can show peaks slightly different and these differences make difficult the creation of an automatic method for spectral signal analysis. This paper presents a signal analysis method based on a clustering technique that allows for the classification of spectra as well as the inference of a diagnosis about the arterial wall condition. The objective is to develop a computational tool that is able to create clusters of spectra according to the arterial wall state and, after data collection, to allow for the classification of a specific spectrum into its correct cluster.

Safety Profile of Trocar and Insufflation Needle Access Systems in Laparoscopic Surgery

BACKGROUND: The most common laparoscopic complications are associated with trocar insertion. The purpose of this study was to develop an objective method of evaluating the safety profile of various access devices used in laparoscopic surgery. STUDY DESIGN: In 20 swine, 6 bladed and 2 needle access devices were evaluated. A force profile was determined by measuring the force required to drive the trocar or needle through the fascia and into the peritoneum, at 0 and 10 mmHg. The amount Of tissue deformation, the length of blade exposed, and the duration of exposure were measured using a high-speed digital imaging system. RESULTS: The needle system without the sheath required the least driving force and had the most favorable force profile. In contrast, the bladed, nonretractable trocar system required a higher driving force and a rapid loss of resistance. Insertion under a pneumoperitoneum did not significantly alter the force profile of the various access devices except for the amount of tissue deformation. With the bladed system, the blade itself was exposed for an average of 0.5 to 1.0 seconds for a distance of 4.5 to 5.0 cm. In comparison, the needle system was exposed for 0.2 seconds for a distance of 1.8 cm. CONCLUSIONS: We developed a reproducible method of measuring the forces required to place the access systems, their pattern of resistance loss, and the characteristics of the blade exposure. These parameters may provide an adjunctive and objective measurement of safety, allowing for more direct comparison between various trocar designs. (J Am Coll Surg 2009;209:222-232. (C) 2009 by the American College of Surgeons)

Comparative study of Botox (R) injection treatment for upper eyelid retraction with 6-month follow-up in patients with thyroid eye disease in the congestive or fibrotic stage

Aim To compare morphometric data of the eyelid fissure and the levator muscle function (LF) before and up to 6 months after transcutaneous injection with five units of Botox (R) in patients with upper lid retraction (ULR) from congestive or fibrotic thyroid eye disease (TED). Methods Twenty-four patients with ULR from TED were submitted to transcutaneous injection of 5 units (0.1 ml) of Botox in one eye only. Patients were divided into two groups: 12 with congestive-stage TED (CG), and 12 with fibrotic-stage TED (FG). Bilateral lid fissure measurements using digital imaging and computer-aided analysis were taken at baseline and at regular intervals 2 weeks, 1 month, 3 months and 6 months after unilateral Botox injection. Mean values taken at different follow-up points were compared for the two groups. Results Most patients experienced marked improvement in ULR, with a mean reduction of 3.81 mm in FG and 3.05 mm in CG. The upper eyelid margin reflex distance, fissure height and total area of exposed interpalpebral fissure were significantly smaller during 1 month in CG and during 3 months in FG. Reduction in LF and in the difference between lateral and medial lid fissure measurements was observed in both groups. The treatment lasted significantly longer in FG than in CG. Conclusions A single 5-unit Botox injection improved ULR, reduced LF and produced an adequate lid contour in patients with congestive or fibrotic TED. The effect lasts longer in patients with fibrotic orbitopathy than in patients with congestive orbitopathy.

Prevention of toothbrushing abrasion of acid-softened enamel by CO(2) laser irradiation

Objective: The aim of the present study was to evaluate the effect of CO(2) laser irradiation (10.6 mu m) at 0.3 J/cm(2) (0.5 mu s; 226 Hz) on the resistance of softened enamel to toothbrushing abrasion, in vitro. Methods: Sixty human enamel samples were obtained, polished with silicon carbide papers and randomly divided into five groups (n = 12), receiving 5 different surface treatments: laser irradiation (L), fluoride (AmF/NaF gel) application (F), laser prior to fluoride (LF), fluoride prior to laser (FL), non-treated control (C). After surface treatment they were submitted to a 25-day erosive-abrasive cycle in 100 ml sprite light (90 s) and brushed twice daily with an electric toothbrush. Between the demineralization periods samples were immersed in supersaturated mineral solution. At the end of the experiments enamel surface loss was determined using a contact profilometer and morphological analysis was performed using scanning electron microscopy (SEM). For SEM analysis of demineralization pattern, cross-sectional cuts of cycled samples were prepared. The data were statistically analysed by one-way ANOVA model with subsequent pairwise comparison of treatments. Results: Abrasive surface loss was significantly lower in all laser groups compared to both control and fluoride groups (p < 0.0001 in all cases). Amongst the laser groups no significant difference was observed. Softened enamel layer underneath lesions was less pronounced in laser-irradiated samples. Conclusion: Irradiation of dental enamel with a CO(2) laser at 0.3 J/cm(2) (5 mu s, 226 Hz) either alone or in combination with amine fluoride gel significantly decreases toothbrushing abrasion of softened-enamel, in vitro. (C) 2011 Elsevier Ltd. All rights reserved.

Effect of image compression of digital lateral cephalograms on the reproducibility of cephalometric points

Objectives: To evaluate the influence of JPEG quality factors 100, 80 and 60 on the reproducibility of identification of cephalometric points on images of lateral cephalograms, compared with the Digital Imaging and Communications in Medicine (DICOM) format. Methods: The sample was composed of 30 images of digital lateral cephalograms obtained from 30 individuals (15 males and 15 females) on a phosphor plate system in DICOM format. The images were converted to JPEG with quality factors 100, 80 and 60 with the aid of software, adding up to 90 images. The 120 images (DICOM, JPEG 100, 80 and 60) were blinded and 12 cephalometric points were identified on each image by three calibrated orthodontists, using the x-y coordinate system, on a cephalometric software. Results: The results revealed that identification of cephalometric points was highly reproducible, except for the point Orbitale (Or) on the x-axis. The different file formats did not present a statistically significant difference. Conclusions: JPEG images of lateral cephalograms with quality factors 100, 80 and 60 did not present alterations in the reproducibility of identification of cephalometric points compared with the DICOM format. Good reproducibility was achieved for the 12 points, except for point Or on the x-axis. Dentomaxillofacial Radiology (2009) 38, 393-400. doi: 10.1259/dmfr/40996636

A methodology for analysis of sugarcane productivity trends - I. Analysis across districts

Historically, few articles have addressed the use of district level mill production data for analysing the effect of varietal change on sugarcane productivity trends. This appears to be due to lack of compiled district data sets and appropriate methods by which to analyse these data. Recently, varietal data on tonnes of sugarcane per hectare (TCH), sugar content (CCS), and their product, tonnes of sugar content per hectare (TSH) on a district basis, have been compiled. This study was conducted to develop a methodology for regular analysis of such data from mill districts to assess productivity trends over time, accounting for variety and variety x environment interaction effects for 3 mill districts (Mulgrave, Babinda, and Tully) from 1958 to 1995. Restricted maximum likelihood methodology was used to analyse the district level data and best linear unbiased predictors for random effects, and best linear unbiased estimates for fixed effects were computed in a mixed model analysis. In the combined analysis over districts, Q124 was the top ranking variety for TCH, and Q120 was top ranking for both CCS and TSH. Overall production for TCH increased over the 38-year period investigated. Some of this increase can be attributed to varietal improvement, although the predictors for TCH have shown little progress since the introduction of Q99 in 1976. Although smaller gains have been made in varietal improvement for CCS, overall production for CCS decreased over the 38 years due to non-varietal factors. Varietal improvement in TSH appears to have peaked in the mid-1980s. Overall production for TSH remained stable over time due to the varietal increase in TCH and the non-varietal decrease in CCS.