M
Marcelo Zanchetta do Nascimento
Researcher at Universidade Federal do ABC
Publications - 90
Citations - 905
Marcelo Zanchetta do Nascimento is an academic researcher from Universidade Federal do ABC. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 13, co-authored 70 publications receiving 617 citations. Previous affiliations of Marcelo Zanchetta do Nascimento include University of São Paulo & Federal University of Uberlandia.
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Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm
TL;DR: A set of computational tools to aid segmentation and detection of mammograms that contained mass or masses in CC and MLO views and a method for detection and segmentation of masses using multiple thresholding, wavelet transform and genetic algorithm is employed in mammograms.
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Classification of masses in mammographic image using wavelet domain features and polynomial classifier
Marcelo Zanchetta do Nascimento,Alessandro Santana Martins,Leandro Alves Neves,Rodrigo Pereira Ramos,Edna Lúcia Flôres,Gilberto Arantes Carrijo +5 more
TL;DR: A system is proposed for texture analysis and classification of lesions in mammographic images and the performance of the polynomial classifier has proved to be better in comparison to other classification algorithms.
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LBP operators on curvelet coefficients as an algorithm to describe texture in breast cancer tissues
Daniel O. Tambasco Bruno,Marcelo Zanchetta do Nascimento,Rodrigo Pereira Ramos,Valério Ramos Batista,Leandro Alves Neves,Alessandro Santana Martins +5 more
TL;DR: The association of curvelet transform, local binary pattern and ANOVA with the PL classifier achieved higher AUC and AC values for all cases and may contribute to the diagnosis of breast tissues (mammographic and histopathological images).
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Computational normalization of H&E-stained histological images: Progress, challenges and future potential.
Thaina Aparecida Azevedo Tosta,Paulo Rogério de Faria,Leandro Alves Neves,Marcelo Zanchetta do Nascimento +3 more
TL;DR: A detailed study of the state of art of computational normalization of H&E-stained histological images, highlighting the main contributions and limitations of correlated works and possible directions for new methods are described.
Journal ArticleDOI
Texture extraction: An evaluation of ridgelet, wavelet and co-occurrence based methods applied to mammograms
TL;DR: This paper evaluates texture classification using features derived from co-occurrence matrices, wavelet and ridgelet transforms of mammographic images, and proposes a false positive reduction in computer-aided detection of masses.