Classification of masses in mammographic image using wavelet domain features and polynomial classifier

Breast cancer is the most common cancer among women. In CAD systems, several studies have investigated the use of wavelet transform as a multiresolution analysis tool for texture analysis and could be interpreted as inputs to a classifier. In classification, polynomial classifier has been used due to the advantages of providing only one model for optimal separation of classes and to consider this as the solution of the problem. In this paper, a system is proposed for texture analysis and classification of lesions in mammographic images. Multiresolution analysis features were extracted from the region of interest of a given image. These features were computed based on three different wavelet functions, Daubechies 8, Symlet 8 and bi-orthogonal 3.7. For classification, we used the polynomial classification algorithm to define the mammogram images as normal or abnormal. We also made a comparison with other artificial intelligence algorithms (Decision Tree, SVM, K-NN). A Receiver Operating Characteristics (ROC) curve is used to evaluate the performance of the proposed system. Our system is evaluated using 360 digitized mammograms from DDSM database and the result shows that the algorithm has an area under the ROC curve Az of 0.98 ± 0.03. The performance of the polynomial classifier has proved to be better in comparison to other classification algorithms. © 2013 Elsevier Ltd. All rights reserved.

Desenvolvimento de uma ferramenta computacional de apoio ao ensino de sistemas eletrônicos digitais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Quantificação de fibrose e enfisema em pacientes com paracoccidioidomicose (PCM) pulmonar, através de imagens de tomografia computadorizada de alta resolução (TCAR)

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Mudanças morfológicas na evolução do megaleque do Taquari: uma análise com base em dados orbitais

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Avaliação da influência de diferentes materiais oclusais na confecção de próteses fixas implantossuportadas unitárias com diferentes diâmetros dos implantes osseointegrados: estudo pelo método dos elementos finitos tridimensionais

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Geotecnologias aplicadas à caracterização e mapeamento das alterações da cobertura vegetal intra-urbana e da expansão urbana da cidade de Rio Claro (SP)

Pós-graduação em Geografia - IGCE

Influência de diferentes pilares e materiais de recobrimento estético na manutenção da pré-carga do parafuso de retenção de coroas implantosuportadas

Pós-graduação em Odontologia - FOA

Avaliação da adaptação marginal de restaurações cerâmicas inlay após a cimentação

Pós-graduação em Odontologia Restauradora - ICT

Método para extração e caracterização de lesões de pele usando difusão anisotrópica, crescimento de regiões, watersheds e contornos ativos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Geotecnologias digitais no ensino médio: avaliação prática de seu potencial

Pós-graduação em Geografia - IGCE

Retificador entrelaçado boost, no modo de condução descontínua, com técnica de correção da corrente de entrada e elevado fator de potência, para aplicação em sistema trólebus

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estudo das características morfométricas de diferentes regiões do intestino delgado e índices zootécnicos em quatro linhagens de frangos de corte

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Development of a CAD system for automatic classification of microcalcifications based on FPGA

This paper presents a Computer Aided Diagnosis (CAD) system that automatically classifies microcalcifications detected on digital mammograms into one of the five types proposed by Michele Le Gal, a classification scheme that allows radiologists to determine whether a breast tumor is malignant or not without the need for surgeries. The developed system uses a combination of wavelets and Artificial Neural Networks (ANN) and is executed on an Altera DE2-115 Development Kit, a kit containing a Field-Programmable Gate Array (FPGA) that allows the system to be smaller, cheaper and more energy efficient. Results have shown that the system was able to correctly classify 96.67% of test samples, which can be used as a second opinion by radiologists in breast cancer early diagnosis. (C) 2013 The Authors. Published by Elsevier B.V.

Optimum-Path Forest Applied for Breast Masses Classification

In Computer-Aided Diagnosis-based schemes in mammography analysis each module is interconnected, which directly affects the system operation as a whole. The identification of mammograms with and without masses is highly needed to reduce the false positive rates regarding the automatic selection of regions of interest for further image segmentation. This study aims to evaluate the performance of three techniques in classifying regions of interest as containing masses or without masses (without clinical findings), as well as the main contribution of this work is to introduce the Optimum-Path Forest (OPF) classifier in this context, which has never been done so far. Thus, we have compared OPF against with two sorts of neural networks in a private dataset composed by 120 images: Radial Basis Function and Multilayer Perceptron (MLP). Texture features have been used for such purpose, and the experiments have demonstrated that MLP networks have been slightly better than OPF, but the former is much faster, which can be a suitable tool for real-time recognition systems.

Projeto de conjunto redutor de velocidade utilizando engrenagem sem-fim

This work is initially based in give a solution to a problem consisting of lifting a load in a warehouse focusing specifically on the solution´s project and comparison of the results obtained following the sequence of the book and comparing these results with the finite elements simulation based on the 3D components modeling. Starting from that was realized the project of the worm gear reducer to solve the problem and makes the work easier. The project consisted basically of the study, project itself and simulation by software of a worm gear reducer and projects steps, starting with the initial problem conditions (to lifting a load up to an specific height at a given time) following all the reducer project sequence, starting by the preliminary draft and electric motor selection using iterative process, material selection, worm gear dimensioning, axles, keyways, bearings and coupling. After that was performed the three dimensional modeling of the components using SolidWorks software and simulating these components using Ansys software. The results show the importance of the CAD in terms of improving project development speed and reducing costs with prototypes