Mr. Helder Cesar Rodrigues de Oliveira Profile

Helder Cesar Rodrigues de Oliveira

Postdoctoral Fellow

Area of Expertise:

Image Processing , Digital Breast Tomosynthesis , Digital Mammography , Denoising , Computer-Aided Detection

Websites:

Personal Website

I have degree in Computer Science and masters in Electrical Engineering. Nowadays I'm fulltime PhD student at University of São Paulo in Brazil, where I work in a CAD system to detection of Architectural Distortion in Digital Breast Tomosynthesis images.

Proceedings Article | 22 May 2020 Paper

Transfer learning in deep convolutional neural networks for detection of architectural distortion in digital mammography

Arthur Costa, Helder C. Oliveira, Lucas Borges, Marcelo A. Vieira

Proceedings Volume 11513, 115130N (2020) https://doi.org/10.1117/12.2564348

KEYWORDS: Mammography, Digital mammography, Data modeling, Convolutional neural networks, Architectural distortion, Databases, Network architectures, Computer aided diagnosis and therapy, Breast, Visualization

Read Abstract +

Proceedings Article | 13 March 2019 Paper

Exploratory learning with convolutional autoencoder for discrimination of architectural distortion in digital mammography

Helder C. de Oliveira, Carlos F. Melo, Juliana Catani, Nestor de Barros, Marcelo da Costa Vieira

Proceedings Volume 10950, 109502F (2019) https://doi.org/10.1117/12.2513021

KEYWORDS: Digital mammography, Mammography, Architectural distortion, Data modeling, Breast cancer, Computer aided diagnosis and therapy, Breast, Computer-aided diagnosis, Digital imaging

Read Abstract +

Proceedings Article | 27 February 2018 Paper

Reduction of false-positives in a CAD scheme for automated detection of architectural distortion in digital mammography

Helder C. de Oliveira, Arianna Mencattini, Paola Casti, Eugenio Martinelli, Corrado di Natale, Juliana Catani, Nestor de Barros, Carlos F. E. Melo, Adilson Gonzaga, Marcelo A. Vieira

Proceedings Volume 10575, 105752P (2018) https://doi.org/10.1117/12.2293388

KEYWORDS: Image segmentation, Architectural distortion, Computer aided diagnosis and therapy, Volume rendering, Digital mammography, Mammography, Breast, Computing systems, Statistical analysis, Binary data

Read Abstract +

Proceedings Article | 3 March 2017 Paper

A new texture descriptor based on local micro-pattern for detection of architectural distortion in mammographic images

Helder C. de Oliveira, Diego Moraes, Gustavo Reche, Lucas Borges, Juliana Catani, Nestor de Barros, Carlos F. Melo, Adilson Gonzaga, Marcelo A. Vieira

Proceedings Volume 10134, 101342U (2017) https://doi.org/10.1117/12.2255516

KEYWORDS: Architectural distortion, Mammography, Feature extraction, Breast, Computing systems, Image classification, Computer-aided diagnosis, Machine vision, Computer vision technology, Breast cancer, Tissues, Neurons, Digital mammography, Binary data, Neural networks

Read Abstract +

Proceedings Article | 24 March 2016 Paper

Validation of no-reference image quality index for the assessment of digital mammographic images

Helder C. de Oliveira, Bruno Barufaldi, Lucas Borges, Salvador Gabarda, Predrag Bakic, Andrew D. Maidment, Homero Schiabel, Marcelo A. Vieira

Proceedings Volume 9787, 978713 (2016) https://doi.org/10.1117/12.2217229

KEYWORDS: Image quality, Digital imaging, Digital mammography, Mammography, Spatial resolution, Visual system, Breast, Signal to noise ratio, Image quality standards, Image analysis, Software development

Read Abstract +

To ensure optimal clinical performance of digital mammography, it is necessary to obtain images with high spatial resolution and low noise, keeping radiation exposure as low as possible. These requirements directly affect the interpretation of radiologists. The quality of a digital image should be assessed using objective measurements. In general, these methods measure the similarity between a degraded image and an ideal image without degradation (ground-truth), used as a reference. These methods are called Full-Reference Image Quality Assessment (FR-IQA). However, for digital mammography, an image without degradation is not available in clinical practice; thus, an objective method to assess the quality of mammograms must be performed without reference. The purpose of this study is to present a Normalized Anisotropic Quality Index (NAQI), based on the Rényi entropy in the pseudo-Wigner domain, to assess mammography images in terms of spatial resolution and noise without any reference. The method was validated using synthetic images acquired through an anthropomorphic breast software phantom, and the clinical exposures on anthropomorphic breast physical phantoms and patient’s mammograms. The results reported by this noreference index follow the same behavior as other well-established full-reference metrics, e.g., the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Reductions of 50% on the radiation dose in phantom images were translated as a decrease of 4dB on the PSNR, 25% on the SSIM and 33% on the NAQI, evidencing that the proposed metric is sensitive to the noise resulted from dose reduction. The clinical results showed that images reduced to 53% and 30% of the standard radiation dose reported reductions of 15% and 25% on the NAQI, respectively. Thus, this index may be used in clinical practice as an image quality indicator to improve the quality assurance programs in mammography; hence, the proposed method reduces the subjectivity inter-observers in the reporting of image quality assessment.

Proceedings Article | 18 March 2015 Paper

Method for inserting noise in digital mammography to simulate reduction in radiation dose

Lucas Borges, Helder C. de Oliveira, Polyana Nunes, Marcelo A. Vieira

Proceedings Volume 9412, 94125J (2015) https://doi.org/10.1117/12.2082257

KEYWORDS: Mammography, Interference (communication), Digital mammography, Sensors, Modulation transfer functions, Computer simulations, X-rays, Error analysis, Electroluminescence, X-ray imaging

Read Abstract +

Proceedings Article | 18 March 2015 Paper

Feasibility study of dose reduction in digital breast tomosynthesis using non-local denoising algorithms

Marcelo A. Vieira, Helder C. de Oliveira, Polyana Nunes, Lucas Borges, Predrag Bakic, Bruno Barufaldi, Raymond Acciavatti, Andrew D. Maidment

Proceedings Volume 9412, 94122C (2015) https://doi.org/10.1117/12.2082398

KEYWORDS: Digital breast tomosynthesis, Denoising, Image quality, Image quality standards, Reconstruction algorithms, Optical filters, Breast, Image filtering, Tomography, Signal to noise ratio

Read Abstract +

Showing 5 of 7 publications

Keywords/Phrases

Search In:

Publication Years