Modelo de data mining para detecção de tumores em exames de rastreio

Dissertação para obtenção do grau de Mestre em Engenharia Informática

PHYSQuiz - Aplicação computacional para formação contínua em Física Radiológica

Mestrado em Engenharia de Computação e Instrumentação Médica

Microcalcification evaluation in computer assisted diagnosis for digital mammography

In order to develop applications for z;isual interpretation of medical images, the early detection and evaluation of microcalcifications in digital mammograms is verg important since their presence is often associated with a high incidence of breast cancers. Accurate classification into benign and malignant groups would help improve diagnostic sensitivity as well as reduce the number of unnecessa y biopsies. The challenge here is the selection of the useful features to distinguish benign from malignant micro calcifications. Our purpose in this work is to analyse a microcalcification evaluation method based on a set of shapebased features extracted from the digitised mammography. The segmentation of the microcalcifications is performed using a fixed-tolerance region growing method to extract boundaries of calcifications with manually selected seed pixels. Taking into account that shapes and sizes of clustered microcalcifications have been associated with a high risk of carcinoma based on digerent subjective measures, such as whether or not the calcifications are irregular, linear, vermiform, branched, rounded or ring like, our efforts were addressed to obtain a feature set related to the shape. The identification of the pammeters concerning the malignant character of the microcalcifications was performed on a set of 146 mammograms with their real diagnosis known in advance from biopsies. This allowed identifying the following shape-based parameters as the relevant ones: Number of clusters, Number of holes, Area, Feret elongation, Roughness, and Elongation. Further experiments on a set of 70 new mammogmms showed that the performance of the classification scheme is close to the mean performance of three expert radiologists, which allows to consider the proposed method for assisting the diagnosis and encourages to continue the investigation in the sense of adding new features not only related to the shape

Automatic classification of breast density

A recent trend in digital mammography is computer-aided diagnosis systems, which are computerised tools designed to assist radiologists. Most of these systems are used for the automatic detection of abnormalities. However, recent studies have shown that their sensitivity is significantly decreased as the density of the breast increases. This dependence is method specific. In this paper we propose a new approach to the classification of mammographic images according to their breast parenchymal density. Our classification uses information extracted from segmentation results and is based on the underlying breast tissue texture. Classification performance was based on a large set of digitised mammograms. Evaluation involves different classifiers and uses a leave-one-out methodology. Results demonstrate the feasibility of estimating breast density using image processing and analysis techniques

Archivo docente en imágenes del seno: mamografía digital. Fundación santa fe de Bogotá.

Se conformó el primer archivo docente digital de patología específica del seno en la modalidad de mamografía a nivel nacional, el cuál permitirá el entrenamiento de radiólogos y residentes de radiología según el sistema de lectura BI-RADS, buscando la unificación de criterios y mejoría de las competencias en la interpretación de las imágenes con la finalidad de aumentar la detección temprana del carcinoma de seno

An automatic correction method for the heel effect in digitized mammography images

The most significant radiation field nonuniformity is the well-known Heel effect. This nonuniform beam effect has a negative influence on the results of computer-aided diagnosis of mammograms, which is frequently used for early cancer detection. This paper presents a method to correct all pixels in the mammography image according to the excess or lack on radiation to which these have been submitted as a result of the this effect. The current simulation method calculates the intensities at all points of the image plane. In the simulated image, the percentage of radiation received by all the points takes the center of the field as reference. In the digitized mammography, the percentages of the optical density of all the pixels of the analyzed image are also calculated. The Heel effect causes a Gaussian distribution around the anode-cathode axis and a logarithmic distribution parallel to this axis. Those characteristic distributions are used to determine the center of the radiation field as well as the cathode-anode axis, allowing for the automatic determination of the correlation between these two sets of data. The measurements obtained with our proposed method differs on average by 2.49 mm in the direction perpendicular to the anode-cathode axis and 2.02 mm parallel to the anode-cathode axis of commercial equipment. The method eliminates around 94% of the Heel effect in the radiological image and the objects will reflect their x-ray absorption. To evaluate this method, experimental data was taken from known objects, but could also be done with clinical and digital images.

Técnicas neuronais de agrupamentos aplicadas à detecção de anomalias em mamogramas digitais

This work proposes the development of an intelligent system for analysis of digital mammograms, capable to detect and to classify masses and microcalcifications. The digital mammograms will be pre-processed through techniques of digital processing of images with the purpose of adapting the image to the detection system and automatic classification of the existent calcifications in the suckles. The model adopted for the detection and classification of the mammograms uses the neural network of Kohonen by the algorithm Self Organization Map - SOM. The algorithm of Vector quantization, Kmeans it is also used with the same purpose of the SOM. An analysis of the performance of the two algorithms in the automatic classification of digital mammograms is developed. The developed system will aid the radiologist in the diagnosis and accompaniment of the development of abnormalities

Sistema auxiliar na análise de mamogramas digitais

This work proposes the development of a Computer System for Analysis of Mammograms SCAM, that aids the doctor specialist in the identification and analysis of existent lesions in digital mammograms. The computer system for digital mammograms processing will make use of a group of techniques of Digital Image Processing (DIP), with the purpose of aiding the medical professional to extract the information contained in the mammogram. This system possesses an interface of easy use for the user, allowing, starting from the supplied mammogram, a group of processing operations, such as, the enrich of the images through filtering techniques, the segmentation of areas of the mammogram, the calculation the area of the lesions, thresholding the lesion, and other important tools for the medical professional's diagnosis. The Wavelet Transform will used and integrated into the computer system, with the objective of allowing a multiresolution analysis, thus supplying a method for identifying and analyzing microcalcifications

Influência da terapêutica hormonal prévia sobre os indicadores de prognóstico do câncer de mama em mulheres na pós-menopausa

OBJETIVO: avaliar a influência da terapêutica hormonal (TH) prévia sobre alguns indicadores de prognóstico do câncer de mama, em pacientes na pós-menopausa. MÉTODOS: estudo transversal por meio da aplicação de questionários e levantamento de prontuários. Foram entrevistadas 157 pacientes com diagnóstico de câncer de mama na pós-menopausa, registrando-se dados clínicos, antecedentes pessoais e familiares, uso de TH e mamografias. Nos prontuários foram obtidas informações sobre o câncer de mama quanto ao diâmetro do tumor, tipo de cirurgia e estudo imuno-histoquímico. Para a estatística empregou-se ANOVA e teste do chi2. RESULTADOS: 38,2% das pacientes eram ex-usuárias de TH e 61,8% não usuárias. O tempo médio de uso da TH foi de 3,7±3,6 anos. As ex-usuárias eram de menor faixa etária e com menor tempo de menopausa quando comparadas às não usuárias (p<0,05). Constatou-se que 26,8% das pacientes apresentavam antecedentes familiares de câncer de mama, em ambos os grupos. Entre as ex-usuárias de TH, 43,3% foram submetidas a mamografias prévias, ao passo que entre as não usuárias, apenas 11,3% (p<0,001). O diâmetro médio do tumor foi menor entre as ex-usuárias de TH (2,3±1,1 cm), com predomínio de quadrantectomias (60%), quando comparadas as não usuárias (3,3±1,5 cm e 32%, respectivamente) (p<0,001). No estudo imuno-histoquímico, observou-se correlação positiva entre a presença de receptores de estrogênio e progesterona positivos e o uso de TH (p<0,001). Não houve correlação entre TH e c-erbB-2 e p53. CONCLUSÃO: nesta casuística, as mulheres na pós-menopausa que usaram TH prévia ao diagnóstico de câncer de mama apresentaram indicadores de prognóstico mais favoráveis quando comparadas às não usuárias.

Fatores associados a não realização da mamografia e do exame clínico das mamas: um estudo de base populacional em Campinas, São Paulo, Brasil

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

Efeito do raloxifeno sobre a densidade mamográfica em mulheres na pós-menopausa

PURPOSE: to evaluate changes in mammographic breast density in postmenopausal women using raloxifene. METHODS: in this clinical trial, 80 women (mean age=61.1 years) were studied prospectively. Forty patients received 60 mg/day raloxifene, and 40 women comprised the non-treated group (control), paired by age and time of menopause. The treated group was composed of patients with osteoporosis of the lumbar spine. Those with history of breast surgery and users of hormone therapy up to six months prior to the study were excluded. The breast density was assessed qualitatively (subjective) and quantitatively (objective) in two moments, initial and final, after a 6-month follow-up. The 320 mammograms (craniocaudal and oblique) were interpreted qualitatively by the Breast Imaging Reporting and Data System (BI-RADS) classification and quantitatively by digital scanning and computer-assisted segmentation. For statistical analysis t-test, Wilcoxon Mann-Whitney, Spearman correlation and the kappa index were used. RESULTS: on the initial statistical comparison, the groups were considered homogenous for the variables: analyzed age, time of menopause, parity, breast feeding, previous hormonal therapy and body mass index. Baseline breast density, by qualitative and quantitative methods, correlated negatively with the age in both groups (p<0.05). Concerning the other variables, there was no correlation. After six months, no alteration was observed in the mammographic breast density in 38 women of raloxifene group and 38 of the control group, by qualitative method. However, by quantitative method, no alteration was observed in 30 women of the raloxifene group and 27 controls (p>0.05). It was observed a weak agreement rate (kappa=0.25) between the BI-RADS classification and digital scanning/computer-assisted segmentation. CONCLUSIONS: in post-menopausal women with osteoporosis, submitted to raloxifene treatment for six months, no alterations were observed on the mammographic breast density.

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.

Otimização de imagens de mamografia computadorizada

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Um método para melhoria de qualidade de imagens médicas utilizando a transformada wavelet

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Avaliação dos achados mamográficos classificados na categoria 4 do sistema BI_RADS® e sua correlação histopatológia

Pós-graduação em Ginecologia, Obstetrícia e Mastologia - FMB