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.

The usefulness of ultrasound in the classification of chronic liver disease

Chronic Liver Disease is a progressive, most of the time asymptomatic, and potentially fatal disease. In this paper, a semi-automatic procedure to stage this disease is proposed based on ultrasound liver images, clinical and laboratorial data. In the core of the algorithm two classifiers are used: a k nearest neighbor and a Support Vector Machine, with different kernels. The classifiers were trained with the proposed multi-modal feature set and the results obtained were compared with the laboratorial and clinical feature set. The results showed that using ultrasound based features, in association with laboratorial and clinical features, improve the classification accuracy. The support vector machine, polynomial kernel, outperformed the others classifiers in every class studied. For the Normal class we achieved 100% accuracy, for the chronic hepatitis with cirrhosis 73.08%, for compensated cirrhosis 59.26% and for decompensated cirrhosis 91.67%.

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.

A lightweight indoor localization model based on motley-keenan and COST

This paper presents a novel approach to WLAN propagation models for use in indoor localization. The major goal of this work is to eliminate the need for in situ data collection to generate the Fingerprinting map, instead, it is generated by using analytical propagation models such as: COST Multi-Wall, COST 231 average wall and Motley- Keenan. As Location Estimation Algorithms kNN (K-Nearest Neighbour) and WkNN (Weighted K-Nearest Neighbour) were used to determine the accuracy of the proposed technique. This work is based on analytical and measurement tools to determine which path loss propagation models are better for location estimation applications, based on Receive Signal Strength Indicator (RSSI).This study presents different proposals for choosing the most appropriate values for the models parameters, like obstacles attenuation and coefficients. Some adjustments to these models, particularly to Motley-Keenan, considering the thickness of walls, are proposed. The best found solution is based on the adjusted Motley-Keenan and COST models that allows to obtain the propagation loss estimation for several environments.Results obtained from two testing scenarios showed the reliability of the adjustments, providing smaller errors in the measured values values in comparison with the predicted values.

RSS and LEA adaptation for indoor location using fingerprinting

Fingerprinting is an indoor location technique, based on wireless networks, where data stored during the offline phase is compared with data collected by the mobile device during the online phase. In most of the real-life scenarios, the mobile node used throughout the offline phase is different from the mobile nodes that will be used during the online phase. This means that there might be very significant differences between the Received Signal Strength values acquired by the mobile node and the ones stored in the Fingerprinting Map. As a consequence, this difference between RSS values might contribute to increase the location estimation error. One possible solution to minimize these differences is to adapt the RSS values, acquired during the online phase, before sending them to the Location Estimation Algorithm. Also the internal parameters of the Location Estimation Algorithms, for example the weights of the Weighted k-Nearest Neighbour, might need to be tuned for every type of terminal. This paper focuses both approaches, using Direct Search optimization methods to adapt the Received Signal Strength and to tune the Location Estimation Algorithm parameters. As a result it was possible to decrease the location estimation error originally obtained without any calibration procedure.

Analysis and recognition of similar environmental sounds

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Informática

An unsupervised approach to feature discretization and selection

Many learning problems require handling high dimensional datasets with a relatively small number of instances. Learning algorithms are thus confronted with the curse of dimensionality, and need to address it in order to be effective. Examples of these types of data include the bag-of-words representation in text classification problems and gene expression data for tumor detection/classification. Usually, among the high number of features characterizing the instances, many may be irrelevant (or even detrimental) for the learning tasks. It is thus clear that there is a need for adequate techniques for feature representation, reduction, and selection, to improve both the classification accuracy and the memory requirements. In this paper, we propose combined unsupervised feature discretization and feature selection techniques, suitable for medium and high-dimensional datasets. The experimental results on several standard datasets, with both sparse and dense features, show the efficiency of the proposed techniques as well as improvements over previous related techniques.

Classificação e agregação automática de notícias desportivas

Mestrado em Engenharia Informática - Área de Especialização em Arquiteturas, Sistemas e Redes

Using data mining techniques to support breast cancer diagnosis

More than ever, there is an increase of the number of decision support methods and computer aided diagnostic systems applied to various areas of medicine. In breast cancer research, many works have been done in order to reduce false-positives when used as a double reading method. In this study, we aimed to present a set of data mining techniques that were applied to approach a decision support system in the area of breast cancer diagnosis. This method is geared to assist clinical practice in identifying mammographic findings such as microcalcifications, masses and even normal tissues, in order to avoid misdiagnosis. In this work a reliable database was used, with 410 images from about 115 patients, containing previous reviews performed by radiologists as microcalcifications, masses and also normal tissue findings. Throughout this work, two feature extraction techniques were used: the gray level co-occurrence matrix and the gray level run length matrix. For classification purposes, we considered various scenarios according to different distinct patterns of injuries and several classifiers in order to distinguish the best performance in each case described. The many classifiers used were Naïve Bayes, Support Vector Machines, k-nearest Neighbors and Decision Trees (J48 and Random Forests). The results in distinguishing mammographic findings revealed great percentages of PPV and very good accuracy values. Furthermore, it also presented other related results of classification of breast density and BI-RADS® scale. The best predictive method found for all tested groups was the Random Forest classifier, and the best performance has been achieved through the distinction of microcalcifications. The conclusions based on the several tested scenarios represent a new perspective in breast cancer diagnosis using data mining techniques.

Sistema de localização indoor com base no protocolo ZIGBEE

Actualmente, os sistemas de localização são uma área em forte expansão sendo que para espaços exteriores existe uma grande variedade de sistemas de localização enquanto que para espaços interiores as soluções são mais escassas. Este trabalho apresenta o estudo e implementação de um sistema de localização indoor baseado no protocolo ZigBee, utilizando a informação da intensidade de sinal recebido (RSSI - Received Signal Strength Indication). Para a realização deste projecto foi necessário iniciar uma pesquisa mais pormenorizada do protocolo ZigBee. O dispositivo móvel a ser localizado é o módulo XBee Serie 2 que se baseia no mesmo protocolo. Posto isto, foi necessário efectuar um estudo sobre sistemas de localização existentes e analisar as técnicas de localização utilizadas para ambientes interiores. Desta forma utiliza-se neste projecto uma técnica que consiste na análise de fingerprinting, onde é criado um mapa com os valores RSSI para diferentes coordenadas do espaço físico. As intensidades de sinal recebido são relativas a dispositivos XBee instalados em pontos fixos de referência. Para calcular a localização do dispositivo móvel é utilizado o algoritmo K-NN (K- Nearest Neighbors) que permite estimar a posição aproximada do dispositivo móvel. Por último é descrito todo o desenvolvimento do projecto assim como a apresentação e discussão de resultados.

Classificação de Documentos

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade No Lisboa para obtenção de grau de Mestre em Engenharia de Informática

Automated image tagging through tag propagation

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial Para obtenção do grau de Mestre em Engenharia Informática

Improvements on the KNN classifier

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Análise comparada da classificação “pixel a pixel” e orientada por objecto de uma imagem WorldView 2 de Maputo, Moçambique

O desenvolvimento das tecnologias associadas à Detecção Remota e aos Sistemas de Informação Geográfica encontram-se cada vez mais na ordem do dia. E, graças a este desenvolvimento de métodos para acelerar a produção de informação geográfica, assiste-se a um crescente aumento da resolução geométrica, espectral e radiométrica das imagens, e simultaneamente, ao aparecimento de novas aplicações com o intuito de facilitar o processamento e a análise de imagens através da melhoria de algoritmos para extracção de informação. Resultado disso são as imagens de alta resolução, provenientes do satélite WorldView 2 e o mais recente software Envi 5.0, utilizados neste estudo. O presente trabalho tem como principal objectivo desenvolver um projecto de cartografia de uso do solo para a cidade de Maputo, com recurso ao tratamento e à exploração de uma imagem de alta resolução, comparando as potencialidades e limitações dos resultados extraídos através da classificação “pixel a pixel”, através do algoritmo Máxima Verossimilhança, face às potencialidades e eventuais limitações da classificação orientada por objecto, através dos algoritmos K Nearest Neighbor (KNN) e Support Vector Machine (SVM), na extracção do mesmo número e tipo de classes de ocupação/uso do solo. Na classificação “pixel a pixel”, com a aplicação do algoritmo classificação Máxima Verosimilhança, foram ensaiados dois tipos de amostra: uma primeira constituída por 20 classes de ocupação/uso do solo, e uma segunda por 18 classes. Após a fase de experimentação, os resultados obtidos com a primeira amostra ficaram aquém das espectativas, pois observavam-se muitos erros de classificação. A segunda amostra formulada com base nestes erros de classificação e com o objectivo de os minimizar, permitiu obter um resultado próximo das espectativas idealizadas inicialmente, onde as classes de interesse coincidem com a realidade geográfica da cidade de Maputo. Na classificação orientada por objecto foram 4 as etapas metodológicas utilizadas: a atribuição do valor 5 para a segmentação e 90 para a fusão de segmentos; a selecção de 15 exemplos sobre os segmentos gerados para cada classe de interesse; bandas diferentemente distribuídas para o cálculo dos atributos espectrais e de textura; os atributos de forma Elongation e Form Factor e a aplicação dos algoritmos KNN e SVM. Confrontando as imagens resultantes das duas abordagens aplicadas, verificou-se que a qualidade do mapa produzido pela classificação “pixel a pixel” apresenta um nível de detalhe superior aos mapas resultantes da classificação orientada por objecto. Esta diferença de nível de detalhe é justificada pela unidade mínima do processamento de cada classificador: enquanto que na primeira abordagem a unidade mínima é o pixel, traduzinho uma maior detalhe, a segunda abordagem utiliza um conjunto de pixels, objecto, como unidade mínima despoletando situações de generalização. De um modo geral, a extracção da forma dos elementos e a distribuição das classes de interesse correspondem à realidade geográfica em si e, os resultados são bons face ao que é frequente em processamento semiautomático.