Helder de Oliveira

Method for simulating dose reduction in digital mammography using the Anscombe transformation.

Purpose: This work proposes an accurate method for simulating dose reduction in digital mammography starting from a clinical image acquired with a standard dose. Methods: The method developed in this work consists of scaling a mammogram acquired at the standard radiation dose and adding signal-dependent noise. The algorithm accounts for specific issues relevant in digital mammography images, such as anisotropic noise, spatial variations in pixel gain, and the effect of dose reduction on the detective quantum efficiency. The scaling process takes into account the linearity of the system and the offset of the detector elements. The inserted noise is obtained by acquiring images of a flat-field phantom at the standard radiation dose and at the simulated dose. Using the Anscombe transformation, a relationship is created between the calculated noise mask and the scaled image, resulting in a clinical mammogram with the same noise and gray level characteristics as an image acquired at the lower-radiation dose. Results: The performance of the proposed algorithm was validated using real images acquired with an anthropomorphic breast phantom at four different doses, with five exposures for each dose and 256 nonoverlapping ROIs extracted from each image and with uniform images. The authors simulated lower-dose images and compared these with the real images. The authors evaluated the similarity between the normalized noise power spectrum (NNPS) and power spectrum (PS) of simulated images and real images acquired with the same dose. The maximum relative error was less than 2.5% for every ROI. The added noise was also evaluated by measuring the local variance in the real and simulated images. The relative average error for the local variance was smaller than 1%. Conclusions: A new method is proposed for simulating dose reduction in clinical mammograms. In this method, the dependency between image noise and image signal is addressed using a novel application of the Anscombe transformation. NNPS, PS, and local noise metrics confirm that this method is capable of precisely simulating various dose reductions.

Distribution of heavy metals in the geochemical phases of sediments from the Tietê River, Brazil

Abstract The fractionation of heavy metals in riverbed sediments along the Tietê River basin, a highly polluted river in southeast region of Brazil, was investigated using a four-step sequential extraction procedure, in order to determine the concentration and distribution of Cu, Co, Cr, Cd, Zn, Ni and Pb, in the river bottom sediments, related to the potential mobility of geochemical phases. Around the metropolitan area of the City of São Paulo, which has 25 million people, the pollution is associated with municipal wastes and industrial effluents. Potentials of high mobility were observed for Cu and Zn along the entire river basin, associated mainly to the organic fraction, while at the Pirapora and Anhembi stations (upper and middle parts of the basin, respectively) the reactive forms of Ni and Pb were more associated to the Fe and Mn oxides. Near the mouth of the Tietê River, no significant contaminations caused by Cr and Ni in riverbed sediments was verified confirming the presence of these metals in the residual or lithogenous phase.

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

The quality of clinical x-ray images is closely related to the radiation dose used in the imaging study. The general principle for selecting the radiation is ALARA (“as low as reasonably achievable”). The practical optimization, however, remains challenging. It is well known that reducing the radiation dose increases the quantum noise, which could compromise the image quality. In order to conduct studies about dose reduction in mammography, it would be necessary to acquire repeated clinical images, from the same patient, with different dose levels. However, such practice would be unethical due to radiation related risks. One solution is to simulate the effects of dose reduction in clinical images. This work proposes a new method, based on the Anscombe transformation, which simulates dose reduction in digital mammography by inserting quantum noise into clinical mammograms acquired with the standard radiation dose. Thus, it is possible to simulate different levels of radiation doses without exposing the patient to new levels of radiation. Results showed that the achieved quality of simulated images generated with our method is the same as when using other methods found in the literature, with the novelty of using the Anscombe transformation for converting signal-independent Gaussian noise into signal-dependent quantum noise.

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

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.

Use of Wavelet Multiresolution Analysis to Reduce Radiation Dose in Digital Mammography

This paper investigates the use of a wavelet multiresolution analysis to reduce noise in mammographic images acquired with low levels of radiation dose. We studied the use of a wavelet denoising technique to filter the quantum noise that is incorporated in mammographic images when the radiation dose is reduced. Results were obtained by denoising a set of mammographic images acquired with different levels of radiation exposure, using an anthropomorphic breast phantom. Parameters of the algorithm were adjusted to provide more efficient reduction of noise without blurring or insertion of artifacts. We used the Anscombe transformation before denoising to convert the Poisson signal-correlated noise into an approximately additive white Gaussian noise. Evaluation of denoising performance were conducted by comparing image quality indexes between mammograms acquired with normal radiation dose and those acquired at lower doses levels, after denoising by the proposed technique.

Feasibility Study of Dose Reduction in Digital Breast Tomosynthesis Using Non-Local Denoising Algorithms

The main purpose of this work is to study the ability of denoising algorithms to reduce the radiation dose in Digital Breast Tomosynthesis (DBT) examinations. Clinical use of DBT is normally performed in “combo-mode”, in which, in addition to DBT projections, a 2D mammogram is taken with the standard radiation dose. As a result, patients have been exposed to radiation doses higher than used in digital mammography. Thus, efforts to reduce the radiation dose in DBT examinations are of great interest. However, a decrease in dose leads to an increased quantum noise level, and related decrease in image quality. This work is aimed at addressing this problem by the use of denoising techniques, which could allow for dose reduction while keeping the image quality acceptable. We have studied two “state of the art” denoising techniques for filtering the quantum noise due to the reduced dose in DBT projections: Non-local Means (NLM) and Block-matching 3D (BM3D). We acquired DBT projections at different dose levels of an anthropomorphic physical breast phantom with inserted simulated microcalcifications. Then, we found the optimal filtering parameters where the denoising algorithms are capable of recovering the quality from the DBT images acquired with the standard radiation dose. Results using objective image quality assessment metrics showed that BM3D algorithm achieved better noise adjustment (mean difference in peak signal to noise ratio < 0.1dB) and less blurring (mean difference in image sharpness ~ 6%) than the NLM for the projections acquired with lower radiation doses.

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

This paper proposes a method to reduce the number of false-positives (FP) in a computer-aided detection (CAD) scheme for automated detection of architectural distortion (AD) in digital mammography. AD is a subtle contraction of breast parenchyma that may represent an early sign of breast cancer. Due to its subtlety and variability, AD is more difficult to detect compared to microcalcifications and masses, and is commonly found in retrospective evaluations of false-negative mammograms. Several computer-based systems have been proposed for automated detection of AD in breast images. The usual approach is automatically detect possible sites of AD in a mammographic image (segmentation step) and then use a classifier to eliminate the false-positives and identify the suspicious regions (classification step). This paper focus on the optimization of the segmentation step to reduce the number of FPs that is used as input to the classifier. The proposal is to use statistical measurements to score the segmented regions and then apply a threshold to select a small quantity of regions that should be submitted to the classification step, improving the detection performance of a CAD scheme. We evaluated 12 image features to score and select suspicious regions of 74 clinical Full-Field Digital Mammography (FFDM). All images in this dataset contained at least one region with AD previously marked by an expert radiologist. The results showed that the proposed method can reduce the false positives of the segmentation step of the CAD scheme from 43.4 false positives (FP) per image to 34.5 FP per image, without increasing the number of false negatives.

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

This paper presents a new local micro-pattern texture descriptor for the detection of Architectural Distortion (AD) in digital mammography images. AD is a subtle contraction of breast parenchyma that may represent an early sign of breast cancer. Due to its subtlety and variability, AD is more difficult to detect compared to microcalcifications and masses, and is commonly found in retrospective evaluations of false-negative mammograms. Several computer-based systems have been proposed for automatic detection of AD, but their performance are still unsatisfactory. The proposed descriptor, Local Mapped Pattern (LMP), is a generalization of the Local Binary Pattern (LBP), which is considered one of the most powerful feature descriptor for texture classification in digital images. Compared to LBP, the LMP descriptor captures more effectively the minor differences between the local image pixels. Moreover, LMP is a parametric model which can be optimized for the desired application. In our work, the LMP performance was compared to the LBP and four Haralicks texture descriptors for the classification of 400 regions of interest (ROIs) extracted from clinical mammograms. ROIs were selected and divided into four classes: AD, normal tissue, microcalcifications and masses. Feature vectors were used as input to a multilayer perceptron neural network, with a single hidden layer. Results showed that LMP is a good descriptor to distinguish AD from other anomalies in digital mammography. LMP performance was slightly better than the LBP and comparable to Haralicks descriptors (mean classification accuracy = 83%).

Influence of rainfall recharge on suspended sediment yields in the Piracicaba drainage basin in southeastern Brazil

ABSTRACT A study on fine-suspended sediment (FSS) yield dynamics was performed in the Piracicaba River drainage basin in the state of São Paulo, Brazil, during an entire hydrological year, and a set of nine flood hydrographs that encompass wet and dry seasons were analysed. The average-specific sediment transport was 88 t km−2 y−1. Hydrograph analysis verified that the lowest sediment yield, totalling 313 t, was related to the driest season of the hydrological year; whereas, the highest yield, amounting to 196,000 t, was related to the season of heaviest rainfall. Both unit hydrographs lasted 10 consecutive days, yet they were obtained under adverse hydrological conditions, with accumulated precipitation in the dry and wet seasons of 14 and 319 mm, respectively, which likely gave rise to different soil saturation levels and changes in surface runoff. This increase in FSS yield, which is greater than 600-fold, is possibly related not only to the different hydrological conditions, but also to the use and occupation of the soil in this agricultural region, where over 70% of the surface area is dedicated to growing pastures and sugar cane.