Wener Borges de Sampaio

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Breast cancer occurs with high frequency among the world's population and its effects impact the patients' perception of their own sexuality and their very personal image. This work presents a computational methodology that helps specialists detect breast masses in mammogram images. The first stage of the methodology aims to improve the mammogram image.(More)
OBJECTIVE The present work has the objective of developing an automatic methodology for the detection of lung nodules. METHODOLOGY The proposed methodology is based on image processing and pattern recognition techniques and can be summarized in three stages. In the first stage, the extraction and reconstruction of the pulmonary parenchyma is carried out(More)
Breast cancer shows high frequency and its psychological effects affect the female’s perception of sexuality and their personal image. The mammographic images processing has contributed to the detection and diagnosis of breast nodules, and contributes as an important tool, reducing the diagnosis uncertainty. This work presents a computational methodology(More)
Breast cancer is the second commonest type of cancer in the world, and the commonest among women, corresponding to 22% of the new cases every year. This work presents a new computational methodology, which helps the specialists in the detection of breast masses based on the breast density. The proposed methodology is divided into stages with the objective(More)
This paper presents a methodology for automatic segmentation of masses in digital mammograms based on two principles: thresholding and evolutionary algorithm. As the staring point of the particles of the swarm, we used Otsu. Then, we applied the Particle Swarm Optimization (PSO) to optimize, evolutionarily, the search for the global maximum of the(More)
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