This paper presents a method to provide contrast enhancement in dense breast digitized images, which are difficult cases in testing of computer-aided diagnosis (CAD) schemes. Three techniques were developed, and data from each method were combined to provide a better result in relation to detection of clustered microcalcifications. Results obtained during the tests indicated that, by combining all the developed techniques, it is possible to improve the performance of a processing scheme designed to detect microcalcification clusters. It also allows operators to distinguish some of these structures in low-contrast images, which were not detected via conventional processing before the contrast enhancement. This investigation shows the possibility of improving CAD schemes for better detection of microcalcifications in dense breast images.

Considering the difficulties in finding good-quality images for the development and test of computer-aided diagnosis (CAD), this paper presents a public online mammographic images database free for all interested viewers and aimed to help develop and evaluate CAD schemes. The digitalization of the mammographic images is made with suitable contrast and spatial resolution for processing purposes. The broad recuperation system allows the user to search for different images, exams, or patient characteristics. Comparison with other databases currently available has shown that the presented database has a sufficient number of images, is of high quality, and is the only one to include a functional search system.

A new restoration methodology is proposed to enhance mammographic images through the improvement of contrast features and the simultaneous suppression of noise. Denoising is performed in the first step using the Anscombe transformation to convert the signal-dependent quantum noise into an approximately signal-independent Gaussian additive noise. In the Anscombe domain, noise is filtered through an adaptive Wiener filter, whose parameters are obtained by considering local image statistics. In the second step, a filter based on the modulation transfer function of the imaging system in the whole radiation field is applied for image enhancement. This methodology can be used as a preprocessing module for computer-aided detection (CAD) systems to improve the performance of breast cancer screening. A preliminary assessment of the restoration algorithm was performed using synthetic images with different levels of quantum noise. Afterward, we evaluated the effect of the preprocessing on the performance of a previously developed CAD system for clustered microcalcification detection in mammographic images. The results from the synthetic images showed an increase of up to 11.5 dB (p = 0.002) in the peak signal-to-noise ratio. Moreover, the mean structural similarity index increased up to 8.3 % (p < 0.001). Regarding CAD performance, the results suggested that the preprocessing increased the detectability of microcalcifications in mammographic images without increasing the false-positive rates. Receiver operating characteristic analysis revealed an average increase of 14.1 % (p = 0.01) in overall CAD performance when restored image sets were used.

This work proposes a method aimed at enhancing the contrast in dense breast images in mammography. It includes a new preprocessing technique, which uses information on the modulation transfer function (MTF) of the mammographic system in the whole radiation field. The method is applied to improve the efficiency of a computer-aided diagnosis (CAD) scheme. Seventy-five regions of interest (ROIs) from dense mammograms were acquired in two pieces of equipment (a CGR Senographe 500t and a Philips Mammodiagnost) and were digitized in a Lumiscan 50 laser scanner. A computational procedure determines the effective focal spot size in each region of interest from the measured focal spot in the center for a given mammographic equipment. Using computational simulation the MTF is then calculated for each field region. A procedure that enlarges the high-frequency portion of this function is applied and a convolution between the resulting new function and the original image is performed. Both original and enhanced images were submitted to a processing procedure for detecting clustered microcalcifications in order to compare the performance for dense breast images. ROIs were divided into four groups, two for each piece of equipment-one with clustered microcalcifications and another without microcalcifications. Our results show that in about 10% of the enhanced images more signals were detected when compared to the results for the original dense breast images. This is important because the usual processing techniques used in CAD schemes present poor results when applied to dense breast images. Since the MTF method is a well-recognized tool in the evaluation of radiographic systems, this new technique could be used to associate quality assurance procedures with the processing schemes employed in CAD for mammography.

This article presents a computer correction technique for radiographic digital images based on digitization process inaccuracy in pixel gray levels relative to the respective image optical densities. The technique consists of determining the digitizer characteristic curve by digitizing a step-wedge radiographic image with known optical densities. Calibration is done by determining the mathematical function that automatically adjusts the pixel gray levels to the expected values according to the manufacturer. Two laser film digitizers were investigated for which discrepancies between measured and expected pixel values were reported. For these cases, the proposed algorithm automatically adjusted the pixel gray levels of the digitized images. Using this method, the digital images show more accurate values of gray levels compared to the original radiographic image. In addition, it allows the development of uniform images databases.

Literature establishes safe limits on the exposure of the eyes to ultraviolet radiation, for the range of 180-400 nm, including spectrally weighted and the total ultraviolet radiant exposure. Most standards for sunglasses protection only require ultraviolet protection in the spectral range of 280-380 nm to ensure the limits for effective spectrally weighted radiant exposure. Calculations of these limits were performed for 27 Brazilian state capitals, and they led to a change in the upper UVA limit to 400 nm on the 2013 review of the Brazilian standard. Moreover, because the sunlight irradiance in Brazil is quite high, integration over the 280- to 400-nm range yields an ultraviolet radiant exposure that is an average of 49% greater than that for the 280- to 380-nm range. These conclusions suggest revision on the standards.

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The performance of a computer-aided dianosis (CAD) scheme is closely dependent on the database used for its development and tests. The scheme sensitivity can be reduced by 15% to 25%, with only 20% of changes in the database cases. Previously, we have developed a processing scheme in order to detect clustered mlcrocalcifications in digital mammograms, and we have tested such a procedure with two different databases. Further evaluations in developing a CAD scheme for mammography have indicated the need for more extensive investigation on the effects resulting from different characteristics of the images bank used for tests. Therefore, this work reports some results regarding such an investigation, with a further discussion over characteristics that can affect the performance of a CAD scheme. Copyright © 2001 by W.B. Saunders CompanyT HE PERFORMANCE of a computer-aided diagnosis (CAD) scheme is strongly dependent on the database used for testing the procedures. I In a previous work? we developed a computerized procedure as part of a mammography CAD scheme intended to detect clustered microcalcifications in digitized mammograms. The first tests with this scheme indicated an efficacy rate of 94% for a particular set of images. With the goal to study the effect of the characteristics of different sets of images on the performance of such a procedure, we have checked the results yielded from five different sets of mammograms. METHODSThe following sets of images were used for the investigation: (I) a set of actual mammograms, obtained from the archives of Hospital das Clfnicas . same set digitized by a LUl\lISCAN (Lumisys, Sunnyvale, CA) laser scanner, with 0.15 mm and 8 bits; (3) another set of mammograms obtained from that same hospital in 1995, and digitized in a UMAX UCI260-Pro, with the same resolutions as the first group; (4) a set of mammograms obtained from recent examinations performed at the Santa Casa hospital at S. Carlos (Brazil), only corresponding to dense breast cases and digitized by the LUl\lISCAN scanner, with the same resolutions as the second group above; and (5) a previously used set obtained off the internet from the National Expert and Training Center for Breast Cancer Screening (the University of Nijmegen, the Netherlands), also with the same resolutions as the second and fourth groups.The steps of the processing scheme for clusters detection were as follows: identification of regions of interest (ROIs); ROI segmentation; area-point transformation.' which converts each detected signal in a unique pixel; and microcalcification grouping, where clusters are identified and marked in the final image. Initially, the procedure was applied to all of the image sets with no changes in the processing parameters, in order to investigate the effect of the acquisition and digitization processes. Then, some parameters were changed according to the database characteristics under test in order to determine the best result possible for that set of images. Figure I illustrates the resultant images after some step...