Feng Chung Wu

Prototype system for feature extraction, classification and study of medical images

MIAS 3.0 supports automatic feature extraction and medical image classification.The system was developed according to prototyping, a software engineering approach.Experts found that MIAS 3.0 meets the proposed requirements and is a promising tool.MIAS 3.0 experimental evaluation was conducted in 67 image fragments.A J48 classifier built from Amadasun and Haralick features was the best MIAS setting. Colonoscopy exam images are useful to identify diseases, such as the colorectal cancer, which is one of the most common cancers worldwide. Computational image analysis and machine learning techniques can assist experts to identify abnormalities in these images. In this work, we present and evaluate MIAS 3.0, which aims to help experts to study and analyze colon tissue images. To do so, the system initially extracts features from these images. Currently, Amadasum, Haralick and Laws texture descriptors are supported. Then, the described images are classified into normal or abnormal images. In this version, J48, nearest neighbor, backpropagation based on multilayer perceptron, naive Bayes, and support vector machine classification algorithms are implemented. MIAS was developed with open source technologies using a software engineering approach to improve flexibility and maintainability. In this work, MIAS was quantitatively assessed by its application in a set of 134 tissue image fragments. The classifiers built from this set were compared according to the cross-validation and contingency table strategies. Also, the system was qualitatively evaluated using 12 heuristics by twelve volunteers from Health and Exact Sciences. The issues found were categorized according to Rolf Molichs severity scale. As a result, the J48 classifier achieved the highest sensitivity (85.07%) and reasonable average error (18.68%). In the qualitative evaluation, 61.26% of the issues found were not considered serious. These assessments suggest that MIAS can be useful to assist domain experts with minimum knowledge in informatics to conduct more complete studies of medical images, by identifying patterns regarding different abnormalities.

Comparative study of the hyperbaric hyperoxygenation in ischemic colonic loops in rats

PURPOSE To analyze and to evaluate the effect of the hyperbaric hyperoxygenation in the mechanical resistance of ischemic colon of rats. METHODS Eighty rats, distributed in four groups of 20 animals in each one, were used. In group 1 (G1), the control group, ischemia was not caused. Group 2 was submitted to the lesser degree of ischemia. Group 3 was submitted to the intermediate degree of ischemia. In group 4, a bigger degree of intestinal ischemia was provoked. Each group was divided in two sub-groups of ten animals each: with hyperbaric chamber (CC) and without hyperbaric chamber (SC). The animals of the four CC subgroups were placed in an experimental hyperbaric chamber in order to inhale oxygen at 100%, at two Absolute Atmospheres, for 120 minutes, for a four-day consecutive period. The animals of the four SC subgroups were kept in environment air during the five days of the experiment. All animals have been submitted to the mechanical study of the intestinal loop by the pressure test of the rupture by liquid distension. The euthanasia occurred in the fifth post-operative day. RESULTS Considering the ischemia factor, the four groups were different among them (p=0.0001). There was no statistical difference between subgroups CC and SC (p=0.3461). CONCLUSION The hyperbaric oxygen-therapy did not present improvement on the induced ischemia in rats upright colic loop.

Estudo comparativo da hiperoxigenação hiperbárica em alças cólicas isquêmicas em ratos

PURPOSE: To analyze and to evaluate the effect of the hyperbaric hyperoxygenation in the mechanical resistance of ischemic colon of rats. METHODS: Eighty rats, distributed in four groups of 20 animals in each one, were used. In group 1 (G1), the control group, ischemia was not caused. Group 2 was submitted to the lesser degree of ischemia. Group 3 was submitted to the intermediate degree of ischemia. In group 4, a bigger degree of intestinal ischemia was provoked. Each group was divided in two sub-groups of ten animals each: with hyperbaric chamber (CC) and without hyperbaric chamber (SC). The animals of the four CC subgroups were placed in an experimental hyperbaric chamber in order to inhale oxygen at 100%, at two Absolute Atmospheres, for 120 minutes, for a four-day consecutive period. The animals of the four SC subgroups were kept in environment air during the five days of the experiment. All animals have been submitted to the mechanical study of the intestinal loop by the pressure test of the rupture by liquid distension. The euthanasia occurred in the fifth post-operative day. RESULTS: Considering the ischemia factor, the four groups were different among them (p=0,0001). There was no statistical difference between subgroups CC and SC (p=0,3461). CONCLUSION: The hyperbaric oxygen-therapy did not present improvement on the induced ischemia in rats upright colic loop.

Energia total de ruptura: um teste biomecânico para avaliação de material biológico com propriedade viscoelástica não linear

Purpose: Presentation of the Total Energy of Rupture biomechanical test to evaluate the intrinsic resistance of the rat’s left colon which presents a non-linear viscoelastic property. Methods: Implementation of Total Energy of Rupture test (ETR) and the Biomechanical Data Acquisition and Analysis System (SABI 2.0) based on physic-mechanical, computational and biomechanical concepts. Fifteen specimens of Wistar adults rat’s left colon where considered for experiments. Results: Using the TER biomechanical test it was possible calculate the accumulated total energy necessary to promote the specimens rupture during the mechanical trial. It was also possible to generate descriptive and statistics reports and graphics through the data acquisition and analysis automatization and management. Conclusion: Based on physic-mechanical, computational and biomechanical concepts, the Total Energy of Rupture test provides mathematical analysis of the rat’s left colon segment behaviour during the experiments, demonstrating to be a possible method to measure the intrinsic resistance of this biological material presenting non-linear viscoelastic property.Objetivo: Apresentacao do teste biomecânico Energia Total de Ruptura para o estudo da resistencia intrinseca de material biologico com propriedade viscoelastica nao-linear representado neste trabalho por segmento integro de colon descendente de rato. Metodos: Implementacao do teste biomecânico Energia Total de Ruptura e do Sistema de Aquisicao e Analise de dados Biomecânicos - SABI 2.0. Para esse fim, foram utilizados conceitos fisico-mecânicos, computacionais e biomecânicos e como corpos de teste, 15 especimes de colon descendente de ratos. Resultados: O teste biomecânico Energia Total de Ruptura permitiu o calculo da energia total acumulada necessaria para promover a ruptura dos corpos de prova durante os ensaios mecânicos. Por meio da automatizacao e gerenciamento da aquisicao e analise dos dados capturados foi possivel a geracao de graficos e relatorios descritivos e estatisticos. Conclusao: Fundamentado em conceitos fisico-mecânicos, computacionais e biomecânicos, o teste Energia Total de Ruptura pode proporcionar analise matematica do comportamento dos segmentos de colon descendente de ratos durante os ensaios, demonstrando ser um possivel metodo de medicao da resistencia intrinseca desse material biologico com propriedade viscoelastica nao-linear.

Comparative study of descendent colon rupture resistance considering traction force of rupture and total energy of rupture in rats

PURPOSE To compare total energy of rupture and traction force of rupture tests within a rupture resistance study of descendent colon of rats. METHODS Twelve descendent colon segments of rats were considered to perform the study. For each one of the specimens, total energy of rupture and traction force of rupture necessary to promote colic wall burst were evaluated through the biomechanical total energy of rupture test using the Biomechanical Data Acquisition and Analysis System, version 2.0. Average, standard deviation, standard error of average and coefficient of variation were considered for analysis of results. RESULTS Traction force of rupture average, standard deviation, standard error of average and coefficient of variation were 380.05 gf, 98.74, 28.5 e 25.98%, respectively while total energy of rupture presented average of 244.85 gf, standard deviation of 57.76, standard error of average of 16.67 and coefficient of variation of 23.59. CONCLUSION Although, total energy of rupture considered a larger number of attributes to its calculation related to non-linear viscoelastic materials, such as colic wall, it presented a smaller coefficient of variation when compared to traction force of rupture, thus demonstrating to constitute a possible parameter to analyze intestinal resistance of rats.

Metalearning for choosing feature selection algorithms in data mining: Proposal of a new framework

Abstract In Data Mining, during the preprocessing step, there is a considerable diversity of candidate algorithms to select important features, according to some criteria. This broad availability of algorithms that perform the Feature Selection task gives rise to the difficulty of choosing, a priori , between the algorithms at hand, the most promising one for a particular problem. In this paper, we present the proposal and evaluation of a new architecture for the recommendation of Feature Selection algorithms based on the use of Metalearning. Our framework is very flexible since the user can adapt it to its proper needs. This flexibility is one of the main advantages of our proposal over other approaches in the literature, which involve steps that cannot be adapted to the user’s local requirements. Furthermore, it combines several concepts of intelligent systems, including Machine Learning and Data Mining, with topics derived from expert systems, as user and data-driven knowledge, with meta-knowledge. This set of solutions coupled with leading-edge technologies allows our architecture to be integrated into any information system, which impact on the automation of services and in reducing human effort during the process. Regarding the Metalearning process, our framework considers several types of properties inherent to the data sets, as well as, Feature Selection algorithms based on many information, distance, dependence and consistency measures. The quality of the methods for Feature Selection was estimated according to a multicriteria performance measure, which guided the ranking process of these algorithms for the construction of data metabases. Proposed by the authors of this work, this multicriteria performance measure combines any three measurements on a single one, creating an interesting and powerful tool to evaluate not only FS algorithms but also to assess any context where it is necessary a combination to maximize a measure or minimize it. The recommendation models, represented by decision trees and induced from the training metabases, allowed us to see in what circumstances a Feature Selection algorithm outperforms the other and what aspects of the data present greater influence in determining the performance of these algorithms. Nevertheless, if the user wishes, any other learning algorithm may be used to induce the recommendation model. This versatility is another strong point of this proposal. Results show that with the characterization of data, through statistical, information and complexity measures, it is possible to reach an accuracy higher than 90%. Besides yielding recommendation models that are interpretable and robust to overfitting, the developed architecture is less computationally expensive than approaches recently proposed in the literature.

Protótipo de um sistema de gerenciamento de protocolos de câncer colorretal

OBJECTIVE: to develop a prototype of a Colorectal Cancer records management system, aiming to store an embracing structured dataset for later application of intelligent data analysis methods. MATERIAL AND METHOD: the prototype was developed in five stages: Colorectal Cancer domain analysis, definition of basic requirements (operational and informational), system design, system construction using open source technologies and system evaluation supported by domain experts. RESULTS AND DISCUSSION: during development, the prototype dataset structure, coherence of information structure, validity of functions implemented and the attendance to the basic requirements define, were evaluated by domain experts. CONCLUSION: according to the experts evaluation, the prototype development completed the first stage of the project of developing the system with a 100% satisfaction rate and is now being used into recording real data. In the next stage it will be finished and deployed the complete system, attending to security requirements and multiuser support

ADOÇÃO DE MEDIDAS DE QUALIDADE DE DADOS PARA O DESENVOLVIMENTO DE SISTEMAS BIOMÉDICOS

Laboratório de Bioinformática — Universidade Estadual do Oeste do Paraná, Foz do Iguaçu, Brasil Programa de Pós-Graduação em Engenharia de Sistemas Dinâmicos e Energéticos Universidade Estadual do Oeste do Paraná, Foz do Iguaçu, Brasil, {wilsonfoz, hueidianalee}@gmail.com Programa de Pós-Graduação em Ciências da Cirurgia — Faculdade de Ciências Médicas Universidade Estadual de Campinas, Campinas, Brasil

Feature Selection for Multi-label Learning: A Systematic Literature Review and Some Experimental Evaluations

AbstractFeature selection can remove non-important features from the data and promote better classifiers. This task, when applied to multi-label data where each instance is associated with a set of labels, supports emerging applications. Although multi-label data usually exhibit label relations, label dependence has been little studied in feature selection. We proposed two multi-label feature selection algorithms that consider label relations. These methods were experimentally competitive with traditional approaches. Moreover, this work conducted a systematic literature review, summarizing 74 related papers.

Symbolic Representation Based on Temporal Order Information for Time Series Classification

In the last decade symbolic representations approaches have been proposed for knowledge discovery in time series. However, the conventional symbolic methods ignore the temporal order of symbols, so this core feature of time series is lost. In this paper, to treat this problem we present a symbolic representation method to incorporate the temporal information in the symbols. The proposed method was evaluated on a decision tree classification using the Symbolic Aggregate Approximation and Equal Fixed Values Discretization approaches applied to 45 time series datasets that includes artificial and real-world data. The experimental results demonstrate the method effectiveness to improve the classification accuracy and the decision tree size for most datasets while preserving the temporal order information into symbolic representations.