Computer-aided diagnostic schemes have been developed with the primary aim of supporting diagnostic accuracy in the investigation of mammography images. A robust mammograms database is a priority requirement to assist in testing the effectiveness of techniques associated with these schemes. However, such datasets–with information on radiological and biopsy reports, different types of findings and good quality images-are difficult to be available, mainly due to restrictions of different radiology centers and hospitals or limited to the research team involved. Aiming to overcome these issues, we are developing an alternative based on images of a structured breast phantom, previously developed in our group. Although having been validated in terms of their physical characteristics, the investigation on the images produced from this phantom exposure on a digital mammography system is an important task with regard to their appearance compared to actual breasts images. For this purpose, a software was developed for managing comparative tests between images from actual breasts and from this phantom, considering internal regions of interest. The intention is to evaluate how much the simulated and real images are confused according to the human visual perception of different observers, seeking to validate the use of this breast phantom for structuring a new mammography images database aimed at evaluation tests of CAD schemes in mammography. Results were promising showing a variation of 50-60% in the rate of correct answers among observers, indicating a reasonable level of confusion between the two types of images.
Photometers use correction filters to adjust spectral responsivity of sensors so that the combined spectral responsivity approximates the responsivity of the human eye V(λ). However, the combination of these components is hardware based, and the quality of the photometer depends on this combination. We propose a meter that uses an RGB sensor, a LED and an artificial neural network that transforms the output of the sensor into luminous transmittance, without the need of a filter. The ANN was trained and validated with two different spectra datasets and generated results with error values below 3%. The methodology presents an option for a meter with calibration that depends only on a software. This allows the development of a low cost and compact photometer.
Cases of breast cancer have increased substantially each year. However, radiologists are subject to subjectivity and failures of interpretation which may affect the final diagnosis in this examination. The high density features in breast tissue are important factors related to these failures. Thus, among many functions some CADx (Computer-Aided Diagnosis) schemes are classifying breasts according to the predominant density. In order to aid in such a procedure, this work attempts to describe automated software for classification and statistical information on the percentage change in breast tissue density, through analysis of sub regions (ROIs) from the whole mammography image. Once the breast is segmented, the image is divided into regions from which texture features are extracted. Then an artificial neural network MLP was used to categorize ROIs. Experienced radiologists have previously determined the ROIs density classification, which was the reference to the software evaluation. From tests results its average accuracy was 88.7% in ROIs classification, and 83.25% in the classification of the whole breast density in the 4 BI-RADS density classes – taking into account a set of 400 images. Furthermore, when considering only a simplified two classes division (high and low densities) the classifier accuracy reached 93.5%, with AUC = 0.95.
KEYWORDS: Spatial resolution, Breast, Digital breast tomosynthesis, Digital imaging, Image quality, Computer simulations, Breast cancer, 3D image processing, Signal to noise ratio, Interference (communication), 3D modeling, Reconstruction algorithms
Digital breast tomosynthesis (DBT) has been shown to be an effective imaging tool for breast cancer diagnosis as it provides three-dimensional images of the breast with minimal tissue overlap. The quality of the reconstructed image depends on many factors that can be assessed using uniform or realistic phantoms. In this paper, we created four models of phantoms using an anthropomorphic software breast phantom and compared four methods to evaluate the gray scale response in terms of the contrast, noise and detectability of adipose and glandular tissues binarized according to phantom ground truth. For each method, circular regions of interest (ROIs) were selected with various sizes, quantity and positions inside a square area in the phantom. We also estimated the percent density of the simulated breast and the capability of distinguishing both tissues by receiver operating characteristic (ROC) analysis. Results shows a sensitivity of the methods to the ROI size, placement and to the slices considered.
Automatic exposure control (AEC) is used in mammography to obtain acceptable radiation dose and adequate image quality regardless of breast thickness and composition. Although there are physics methods for assessing the AEC, it is not clear whether mammography systems operate with optimal dose and image quality in clinical practice. In this work, we propose the use of a normalized anisotropic quality index (NAQI), validated in previous studies, to evaluate the quality of mammograms acquired using AEC. The authors used a clinical dataset that consists of 561 patients and 1,046 mammograms (craniocaudal breast views). The results show that image quality is often maintained, even at various radiation levels (mean NAQI = 0.14 ± 0.02). However, a more careful analysis of NAQI reveals that the average image quality decreases as breast thickness increases. The NAQI is reduced by 32% on average, when the breast thickness increases from 31 to 71 mm. NAQI also decreases with lower breast density. The variation in breast parenchyma alone cannot fully account for the decrease of NAQI with thickness. Examination of images shows that images of large, fatty breasts are often inadequately processed. This work shows that NAQI can be applied in clinical mammograms to assess mammographic image quality, and highlights the limitations of the automatic exposure control for some images.
KEYWORDS: Breast, Elastography, Breast cancer, Mammography, Color imaging, Databases, Ultrasonography, Diagnostics, Visualization, RGB color model, Visual analytics, Cancer
Breast elastography is a new sonographic technique that provides additional information to evaluate tissue stiffness. However, interpreting breast elastography images can vary depending on the radiologist. In order to provide quantitative and less subjective data regarding the stiffness of a lesion, we developed a tool to measure the amount of hard area in a lesion from the 2D image. The database consisted of 78 patients with 83 breast lesions (31 malignant and 52 benign). Two radiologists and one resident manually drew the contour of the lesions in B-mode ultrasound images and the contour was mapped in the elastography image. By using the system proposed, the radiologists obtained a very good diagnostic agreement among themselves (kappa = 0.86), achieving the same sensitivity and specificity (80.7 and 88.5, respectively), and an AUC of 0.883 for Radiologist 1 and 0.892 for Radiologist 2. The Resident had less interobserver agreement, as well as lower specificity and AUC, which may be related to less experience. Furthermore, the radiologists had an agreement with the tool used in the automatic method higher than 90%. Thus, the method developed was useful in aiding the diagnosis of breast lesions in strain elastography, minimizing its subjectivity.
Cone Beam Computed Tomography (CBCT), a kind of face and neck exams can be opportunity to identify, as an incidental finding, calcifications of the carotid artery (CACA). Given the similarity of the CACA with calcification found in several x-ray exams, this work suggests that a similar technique designed to detect breast calcifications in mammography images could be applied to detect such calcifications in CBCT. The method used a 3D version of the calcification detection technique [1], based on a signal enhancement using a convolution with a 3D Laplacian of Gaussian (LoG) function followed by removing the high contrast bone structure from the image. Initial promising results show a 71% sensitivity with 0.48 false positive per exam.
KEYWORDS: Mammography, Breast, Digital mammography, Breast cancer, Image quality, Tissues, Image analysis, Computer aided diagnosis and therapy, Digital imaging, Data acquisition
Numerous breast phantoms have been developed to be as realistic as possible to ensure the accuracy of image quality analysis, covering a greater range of applications. In this study, we simulated three different densities of the breast parenchyma using paraffin gel, acrylic plates and PVC films. Hydroxyapatite was used to simulate calcification clusters. From the images acquired with a GE Senographe DR 2000D mammography system, we selected 68 regions of interest (ROIs) with and 68 without a simulated calcification cluster. To validate the phantom simulation, we selected 136 ROIs from the University of South Florida’s Digital Database for Screening Mammography (DDSM). Seven trained observers performed two observer experiments by using a high-resolution monitor Barco mod. E-3620. In the first experiment, the observers had to distinguish between real or phantom ROIs (with and without calcification). In the second one, the observers had to indicate the ROI with calcifications between a pair of ROIs. Results from our study show that the hydroxyapatite calcifications had poor contrast in the simulated breast parenchyma, thus observers had more difficulty in identifying the presence of calcification clusters in phantom images. Preliminary analysis of the power spectrum was conducted to investigate the radiographic density and the contrast thresholds for calcification detection. The values obtained for the power spectrum exponent (β) were comparable with those found in the literature.
KEYWORDS: Image quality, Digital imaging, Digital mammography, Mammography, Spatial resolution, Visual system, Breast, Signal to noise ratio, Image quality standards, Image analysis, Software development
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.
In this work some segmentation techniques are evaluated by using a simple centroid-based classification system regarding breast mass delineation in digital mammography images. The aim is to determine the best one for future CADx developments. Six techniques were tested: Otsu, SOM, EICAMM, Fuzzy C-Means, K-Means and Level-Set. All of them were applied to segment 317 mammography images from DDSM database. A single compact set of attributes was extracted and two centroids were defined, one for malignant and another for benign cases. The final classification was based on proximity with a given centroid and the best results were presented by the Level-Set technique with a 68.1% of Accuracy, which indicates this method as the most promising for breast masses segmentation aiming a more precise interpretation in schemes CADx.
KEYWORDS: Tissues, Breast, Computer simulations, Absorption, X-rays, Signal attenuation, Digital mammography, Mammography, X-ray imaging, Monte Carlo methods
This work proposes a simulation model involving subtraction of digital mammography images obtained at different X-ray
beam levels of energy to aid the detection of breast malignant lesions. Absorption coefficients behavior of 3 main
structures of clinical interest – adipose tissue, fiber glandular tissue and the typical carcinoma – as a function of the beam
energy from a Mo X-ray tube was the basis to develop a computer simulation of the possible acquired images. The
simulation has considered a typical compressed breast with 4.5cm in thickness, and variations of the carcinoma and
glandular tissues thicknesses - 0.4 up to 2.0cm and 4.1 to 2.5cm, respectively - were evaluated as a function of the
photons mean energy - 14 up to 25 keV, in the typical mammography energy range. Results have shown that: (a) if the
carcinoma thickness is over 0.4cm, its detection may be feasible even masked by fiber tissue with exposures in the range
of 19 to 25 keV; (b) for masked carcinoma with thickness in the range of 0.4-2.0cm, the proposed procedure can enhance
it in the image resulting from the digital subtraction between images obtained at 14 and at 22 keV. Therefore such results
indicate that this simulation procedure can be a useful tool in aiding the identification of possible missed malignant
lesions which could not be detected in the typical exam, mainly considering dense breasts.
The task of identifying the malignancy of nodular lesions on mammograms becomes quite complex due to overlapped structures or even to the granular fibrous tissue which can cause confusion in classifying masses shape, leading to unnecessary biopsies. Efforts to develop methods for automatic masses detection in CADe (Computer Aided Detection) schemes have been made with the aim of assisting radiologists and working as a second opinion. The validation of these methods may be accomplished for instance by using databases with clinical images or acquired through breast phantoms. With this aim, some types of materials were tested in order to produce radiographic phantom images which could characterize a good enough approach to the typical mammograms corresponding to actual breast nodules. Therefore different nodules patterns were physically produced and used on a previous developed breast phantom. Their characteristics were tested according to the digital images obtained from phantom exposures at a LORAD M-IV mammography unit. Two analysis were realized the first one by the segmentation of regions of interest containing the simulated nodules by an automated segmentation technique as well as by an experienced radiologist who has delineated the contour of each nodule by means of a graphic display digitizer. Both results were compared by using evaluation metrics. The second one used measure of quality Structural Similarity (SSIM) to generate quantitative data related to the texture produced by each material. Although all the tested materials proved to be suitable for the study, the PVC film yielded the best results.
A large number of breast phantoms have been developed for conducting quality tests, characterization of imaging systems and computer aided diagnosis schemes, dosimetry and image perception. The realism of these phantoms is important for ensuring the accuracy of results and a greater range of applications. In this work, a developed phantom is considered proposing the use of PVC films for simulation of nodules inserted in the breast parenchyma designed for classification between malignant and benign signals according to the BI-RADS® standard. The investigation includes analysis of radiographic density, mass shape and its corresponding contour outlined by experienced radiologists. The material was cut based on lesions margins found in 44 clinical cases, which were divided between circumscribed and spiculated structures. Tests were performed to check the ability of the specialists in distinguishing the contour compared to actual cases while the shapes accuracy was determined quantitatively by evaluation metrics. Results showed the applicability of the chosen material creating image radiological patterns very similar to the actual ones.
Routine performance of basic test procedures and dose measurements are essential for assuring high quality of mammograms. International guidelines recommend that breast care providers ascertain that mammography systems produce a constant high quality image, using as low a radiation dose as is reasonably achievable. The main purpose of this research is to develop a framework to monitor radiation dose and image quality in a mixed breast screening and diagnostic imaging environment using an automated tracking system. This study presents a module of this framework, consisting of a computerized system to measure the image quality of the American College of Radiology mammography accreditation phantom. The methods developed combine correlation approaches, matched filters, and data mining techniques. These methods have been used to analyze radiological images of the accreditation phantom. The classification of structures of interest is based upon reports produced by four trained readers. As previously reported, human observers demonstrate great variation in their analysis due to the subjectivity of human visual inspection. The software tool was trained with three sets of 60 phantom images in order to generate decision trees using the software WEKA (Waikato Environment for Knowledge Analysis). When tested with 240 images during the classification step, the tool correctly classified 88%, 99%, and 98%, of fibers, speck groups and masses, respectively. The variation between the computer classification and human reading was comparable to the variation between human readers. This computerized system not only automates the quality control procedure in mammography, but also decreases the subjectivity in the expert evaluation of the phantom images.
Due to the high incidence rate of breast cancer in women, many procedures have been developed to assist the diagnosis and early detection. Currently, ultrasonography has proved as a useful tool in distinguishing benign and malignant masses. In this context, the computer-aided diagnosis schemes have provided to the specialist a second opinion more accurately and reliably, minimizing the visual subjectivity between observers. Thus, we propose the application of an automatic detection method based on the use of the technique of active contour in order to show precisely the contour of the lesion and provide a better understanding of their morphology. For this, a total of 144 images of phantoms were segmented and submitted to morphological operations of opening and closing for smoothing the edges. Then morphological features were extracted and selected to work as input parameters for the neural classifier Multilayer Perceptron which obtained 95.34% correct classification of data and Az of 0.96.
A major difficulty in the interpretation of mammographic images is the low contrast and, in the case of early detection of breast cancer, the reduced size of the features of malignancy on findings such as microcalcifications. Furthermore, image assessment is subject to significant reliance of the capacity of observation of the expert that will perform it, compromising the final diagnosis accuracy. Thinking about this aspect, this study evaluated the subjectivity of visual inspection to assess the contrast-detail in mammographic images. For this, we compared the human readings of images generated with the CDMAM phantom performed by four observers, enabling to determining a threshold of contrast visibility in each diameter disks present in the phantom. These thresholds were compared graphically and by statistical measures allowing us to build a strategy for use of contrast and detail (dimensions) as parameters of quality in mammography.
With the absolute prevalence of digital images in mammography several new tools became available for radiologist; such as CAD schemes, digital zoom and contrast alteration. This work focuses in contrast variation and how the radiologist reacts to these changes when asked to evaluated image quality. Three contrast enhancing techniques were used in this study: conventional equalization, CCB Correction [1] – a digitization correction – and value subtraction. A set of 100 images was used in tests from some available online mammographic databases. The tests consisted of the presentation of all four versions of an image (original plus the three contrast enhanced images) to the specialist, requested to rank each one from the best up to worst quality for diagnosis. Analysis of results has demonstrated that CCB Correction [1] produced better images in almost all cases. Equalization, which mathematically produces a better contrast, was considered the worst for mammography image quality enhancement in the majority of cases (69.7%). The value subtraction procedure produced images considered better than the original in 84% of cases. Tests indicate that, for the radiologist’s perception, it seems more important to guaranty full visualization of nuances than a high contrast image. Another result observed is that the “ideal” scanner curve does not yield the best result for a mammographic image. The important contrast range is the middle of the histogram, where nodules and masses need to be seen and clearly distinguished.
In a continuing work of establishing safe limits for UV protection on sunglasses, we have estimated the incident UV radiation for the 280 nm – 400 nm range for 5500 locations in Brazil. Current literature establishes safe limits regarding ultraviolet radiation exposure in the spectral region 180nm–400nm for weighted and unweighted UV radiant exposure. British Standard BSEN1836(2005) and American Standard ANZI Z80.3(2009) require the UV protection in the spectral range 280nm–380nm, and The Brazilian Standard for sunglasses protection, NBR15111(20013), currently requires protection for the 280nm – 400nm range as established by literature. However, none of them take into account the total (unweighted) UVA radiant exposure.Calculations of these limits have been made for 5500 Brazilian locations which included the geographic position of the city; altitude, inclination angle of the Earth; typical atmospheric data (ozone column; water vapor and others) as well as scattering from concrete, grass, sand, water, etc.. Furthermore, regarding UV safety for the ocular media, the resistance to irradiance test required on this standard of irradiating the lenses for 25 continuous hours with a 450W sunlight simulator leads to a correspondence of 26 hours and 10 minutes of continuous exposure to the Sun. Moreover, since the sun irradiance in Brazil is quite large, integrations made for the 280-400 nm range shows an average of 45% of greater ultraviolet radiant exposure than for the 280-380 nm range. Suggestions on the parameters of these tests are made in order to establish safe limits according to the UV irradiance in Brazil.
The International Commission on Non-Ionizing Radiation Protection (ICNIRP) establishes that the safe limits regarding ultraviolet radiation exposure in the spectral region 180nm–400nm incident upon the unprotected eye(s) should not exceed 30 Jm-2 effective spectrally weighted (spectral weighting factors are provided by ICNIRP); and the total (unweighted) ultraviolet radiant exposure in the spectral region 315nm–400nm should not exceed 104 Jm-2. However, it should be considered that the spectral range from 180nm–280nm does not reach the surface of the Earth, since it is absorbed by the ozone layer of the atmosphere. The Brazilian Standard for sunglasses protection, NBR15111(2004), as well as the British Standard BSEN1836(2005) and American Standard ANZI Z80.3(2009), requires the UV protection in the spectral range 280nm–380nm, but does not take into account the total (unweighted) UVA radiant exposure. These limits are discussed in this work and calculations have been made for 27 state capitals of Brazil to understand the limits that should be involved in order to protect the eyes of the Brazilian population. These calculations and considerations may be extended to other countries as well. As a conclusion, we show that the upper limit for the UVA protection of 400nm should be included in the Brazilian standard, as well as the irradiance limits. Furthermore, the parameters for the resistance to irradiance test required on the Brazilian standard are also discussed herein as well the significance of this test. We show that the test should be performed by the sun simulator for a longer period than currently required.
Ultrasound (US) is a useful diagnostic tool to distinguish benign from malignant breast masses, providing more detailed evaluation in dense breasts. Due to the subjectivity in the images interpretation, computer-aid diagnosis (CAD) schemes have been developed, increasing the mammography analysis process to include ultrasound images as complementary exams. As one of most important task in the evaluation of this kind of images is the mass detection and its contours interpretation, automated segmentation techniques have been investigated in order to determine a quite suitable procedure to perform such an analysis. Thus, the main goal in this work is investigating the effect of some processing techniques used to provide information on the determination of suspicious breast lesions as well as their accurate boundaries in ultrasound images. In tests, 80 phantom and 50 clinical ultrasound images were preprocessed, and 5 segmentation techniques were tested. By using quantitative evaluation metrics the results were compared to a reference image delineated by an experienced radiologist. A self-organizing map artificial neural network has provided the most relevant results, demonstrating high accuracy and low error rate in the lesions representation, corresponding hence to the segmentation process for US images in our CAD scheme under tests.
Mammogram acquisition in digital format is one of the most relevant steps for image processing in computer-aided detection schemes for mammography. We investigate film digitizer systems using different technologies to determine their influence on the results of mammography image segmentation schemes. It also provides image scanning process regardless of the technology by the development of automatic software based on the digitizers’ characteristic curves. Comparative assessment of digitizer properties and features is performed as well as the software for managing the digitized image acquisition. The images were obtained from six different digitizers and evaluated by means of statistical analysis. Tests were conducted for comparing the responses from each equipment, regarding their respective curves, and they have presented significant variations relative to the original characteristic curve of high quality films used as reference—which largely influence the performance of processing schemes applied on sets of mammography images digitized by those systems. However, when our proposed scanning software was applied with intensity transformation procedure based on the characteristic curve “correction,” the images were comparable to the film optical density, which has improved the processing technique’s performance. The results have pointed out it is possible to achieve high sensitivity and performance of such schemes even with low-cost digitizer systems since their quality characteristics are well known and the procedure herein proposed is used within the mammogram scanning process.
Taking into account requirements for processing digital mammograms, systems dealing with the optimization of images
acquisition need to be adequately evaluated. The processes for generating these images are varied and they can be
grouped mainly in two categories: (1) films scanned by specialized digitizers; (2) images obtained from electronic
sensors associated to digital converters (CR and DR systems). The main two types of different scanners are those with
white light-based detection and CCD sensors and with a scanning laser beam. Thus the current investigation aims to
perform quality evaluation of film digitizers, mainly addressed to mammography. In this analysis the following
parameters were studied: digitizers characteristic curves - relating the pixel value assigned to a region and the
corresponding optical density of the film on the same region; noise - obtained by the Wiener spectrum; and
reproducibility - evaluating whether a device used to capture a digital image can be reliable in subsequent scans. Six
different digitizer equipments were investigated with purposes of determining tools to enhance the image quality based
on their characteristics. The results have indicated that although the most sophisticated scanners have the best
characteristics among those evaluated, knowledge about the scanner behavior can allow developing procedures to
provide the adequate quality image for processing schemes.
As all women over the age of 40 are recommended to perform mammographic exams every two years, the
demands on radiologists to evaluate mammographic images in short periods of time has increased considerably. As a tool
to improve quality and accelerate analysis CADe/Dx (computer-aided detection/diagnosis) schemes have been
investigated, but very few complete CADe/Dx schemes have been developed and most are restricted to detection and not
diagnosis. The existent ones usually are associated to specific mammographic equipment (usually DR), which makes
them very expensive. So this paper describes a prototype of a complete mammography CADx scheme developed by our
research group integrated to an imaging quality evaluation process. The basic structure consists of pre-processing
modules based on image acquisition and digitization procedures (FFDM, CR or film + scanner), a segmentation tool to
detect clustered microcalcifications and suspect masses and a classification scheme, which evaluates as the presence of
microcalcifications clusters as well as possible malignant masses based on their contour. The aim is to provide enough
information not only on the detected structures but also a pre-report with a BI-RADS classification. At this time the
system is still lacking an interface integrating all the modules. Despite this, it is functional as a prototype for clinical
practice testing, with results comparable to others reported in literature.
A prototype was built to provide means for clinical studies of alterations on the cornea UV natural protection from
current procedures, such as the refractive surgery and corneal crosslinking.
The prototype consists of an optical dual beam UVA/UVB system, for measuring the transmittance of the cornea at the
300nm - 400nm range.
The system performs 500 measurements/s (±0.25% precision for the transmittance). It has been correlated to
spectrophotometer (0.985) for donated human corneas.
Preliminary studies on human corneas demonstrate that as the stromal layer is reduced, there is significant loss of the
cornea natural UV protection.
Segmenting structures of interest represents one of the most important stages in the processes of interpreting, classifying
and diagnosing analyses for Computer Aided-Diagnosis schemes. In this work, a method to segment microcalcification
clusters in mammograms is proposed which is based on a differential filter, associated to the classic Sobel filter, in a presegmentation
step (Step 1). This process will identify the significant pixels, which means that they will have the same
value in both resultant images at each filtering processes. Also, two morphologic operations, a classic dilatation scheme
and the proposed filter in multidirectional format are applied to obtain better border definitions and filled in regions of
interest. In the next step (Step 2), an image map is obtained by translating a template in almost all possible image
positions generating a vector of densities, formed by counting significant pixels. This discrete function makes it possible
to find maximum points which will represent the possible microcalcification clusters. An algorithm to transform areas
into single points is proposed to enable counting how many possible microcalcifications there are inside these regions.
Tests with two databases composed of full mammograms and regions of interest with phantom images are presented with
its respective performances.
Database characteristics can affect significantly the performance of a mammography CAD scheme. Hence adequate
performance comparison among different CAD schemes is not suitable since a single scheme could present different
results depending on the set of chosen cases. Images in database should follow a set of quality criteria, since the imaging
process up to digital file. CAD schemes can not be developed without a database used to test their efficacy, but each
database with particular characteristics may influence on the processing scheme performance. A possible solution could
be using information on the imaging equipment characteristics. This work describes a preprocessing in order to
"compensate" the image degradation during the acquisition steps, assuring a better "uniformity" relative to the images
quality. Thus, poor quality images would be restored, providing therefore some independence on the images source to
CAD schemes and allowing to reach the better possible performance. Tests performed with mammography images sets
reported a 14% increase in sensitivity for microcalcifications detection. Although this result was followed by a little
increase in false positive rates, simple changes in techniques parameters can provide the same improvement but with a
reduction of the false positive detections.
We evaluated the performance of a novel procedure for segmenting mammograms and detecting clustered microcalcifications in two types of image sets obtained from digitization of mammograms using either a laser scanner, or a conventional “optical” scanner. Specific regions forming the digital mammograms were identified and selected, in which clustered microcalcifications appeared or not. A remarkable increase in image intensity was noticed in the images from the optical scanner compared with the original mammograms. A procedure based on a polynomial correction was developed to compensate the changes in the characteristic curves from the scanners, relative to the curves from the films. The processing scheme was applied to both sets, before and after the polynomial correction. The results indicated clearly the influence of the mammogram digitization on the performance of processing schemes intended to detect microcalcifications. The image processing techniques applied to mammograms digitized by both scanners, without the polynomial intensity correction, resulted in a better sensibility in detecting microcalcifications in the images from the laser scanner. However, when the polynomial correction was applied to the images from the optical scanner, no differences in performance were observed for both types of images.
This work presents a computational method for automatic determination of the anode angle of any radiographic
equipment using a non-invasive method. The anode angle is a significant parameter of radiographic equipment, as it
determines the focal spot size and the magnitude of the heel effect, which is directly related to the image quality. Even
though it is an important parameter, it is not always provided by the x-ray equipment manufacturer. Besides, it is very
difficult to be measured in practical quality assurance procedures. First, a pinhole matrix made of lead was built, which
contains 33 radial 50μm diameter holes. This pinhole matrix is used to obtain a radiographic image of focal spot
projections along the radiation field. The computer algorithm calculates the point spread function of each focal spot
projection as well as the distance between them. Thus, the anode angle can be determined automatically by using the
field characteristic, as the geometric unsharpness at any arbitrary field position can be derived from those at the central
beam position. Results showed good accuracy compared to nominal values, and also a methodology was developed to
validate the computational algorithm. Determination of anode angle of any radiographic equipment (including
mammographic ones) with great precision can easily be done by using the method proposed in this work.
The purpose of this work is the evaluation and analysis of Bayesian network models in order to classify clusters of microcalcifications to supply a second opinion to the specialists in the detection of breast diseases by mammography. Bayesian networks are statistics techniques, which provide explanation about the inferences and influences among features and classes of a determinated problem. Therefore, the technique investigation will aid in obtaining more detailed information to the diagnosis in a CAD scheme. From regions of interest (ROI), containing clusters of microcalcifications, detailed image analysis, pixel to pixel; in this step shape using geometric descriptors (Hu Invariant Moments, second and third order moments and radius gyration); irregularity measure; compactness; area and perimeter extracted descriptors. By using software of Bayesian network models construction, different Bayesian network classifier models could be generated, using the extracted features mentioned above in order to verify their behavior and probabilistic influences and used as the input to Bayesian network, some tests were performed in order to build the classifier. The results of generated nets models validation correspond to an average of 10 tests made with 6 different database sub-groups. The first results of validation have shown 83.17% of correct results.
Although digital mammography is currently being used all over the world, most of the mammographic units are still based on screen-film systems. In these systems, the choice of the best combination between the screen and the film is important to assure image quality. This work presents a computer simulation method to help choosing the proper screen-film system to each application, showing sensitometric parameters for each combination, like speed, latitude and contrast determined by sensitometric curves film and screen-film combination, measured experimentally, using commercial calibrated sensitometer and densitometer, allowing further comparison between them according to the application required. Panthom images are presented showing the results that will be obtained in clinical use. The influence of each screen is also determined. Phantom images were obtained using a known screen-film combination. These images were digitized in a laser scanner. The exposures information is used to predict the final image by using the screen-film sensitometric curve, chosen by the user. The computer simulation was used for evaluating several mammographic films combined with different screens, currently used for mammography. Simulated results were in good agreement with values obtained experimentally. The results obtained with the proposed algorithm confirm the possibility of using this method for evaluating the performance of any screen-film combination considering the sensitometric curve. It can be an important tool for quick evaluation of a screen-film system as it predicts the image characteristics with no unnecessary X-ray exposition.
This work presents a computer algorithm to evaluate film digitizers in terms of its spatial resolution by using image processing techniques. A testing pattern containing slits of different widths, not-equally spaced, was developed. When digitizing this pattern, the algorithm automatically analyses the digital image and evaluate spatial resolution capabilities of the digitizer. These analyses were made by calculating computationally digital image slit width and the distance between the slits. Sampling distances in both directions (parallel and perpendicular to scan direction) are determined by comparing calculated values with previous measurements made by using a calibrated microscope. Evaluation also includes the determination of the presampling modulation transfer function (MTF). The algorithm was used for the measurement of effective pixel size and presampling MTF of a Lumiscan 50 laser digitizer and an Umax Powerlook II optical scanner in both directions. Results showed that the laser digitizer presents significative difference between parallel and perpendicular MTF and the optical scanner presents better MTF considering high frequencies components. Results obtained with the proposed algorithm confirm the possibility of evaluating spatial resolution limitations of any film digitizer using an automatic and simple method.
This work presents a computational model for practical application of the transfer function method for radiographic units evaluation, in order to reduce some experimental constraints involved to its determination. With the proposed algorithm, the complete Optical Transfer Function (Modulation Transfer Function and Phase Transfer Function) can be easily determined as well as the effective focal spot sizes at any field location without using a microdensitometer. All measurements are done from a digitized slit image obtained experimentally at the field center position. The effective focal spot sizes can be calculated by using the Line Spread Function Root Mean Square (RMS) value or by the Modulation Transfer Function (MTF) first minimum. Besides, considering the variation of the effective focal spot size given by the field characteristic equations, all these parameters can also be determined at any location on the radiation field. The computer scheme was used for evaluating slit images obtained from nine different x-ray equipments. Results confirmed the possibility of using the transfer function method for quality evaluation of any radiological system in a simple and automatic way. This computer scheme replaces some of the expensive and specific devices necessary to the experimental MTF evaluation by quite more accessible and low cost equipments.
This work presents a classifier for mammographic masses using the wavelet transform as characteristics generator. It considers the BI-RADS classification, dividing mass according to their shapes: circulate, nodular and speculate. We developed procedures with two steps: the first involves a model applying one wavelet technique performing the contours analysis with simulated mass images. This procedure was used to choose the best wavelet that could generate the desired characteristics. The second procedure had the objective of applying the chosen wavelet to masses from segmented images. Both methods have as answers three classes of shape. A root-mean-square function is applied to obtain the energy measure for each level of wavelet decomposition. Thus the shape feature vectors are formed with the coefficients of the details and coefficients of approximation extracted by the energy of wavelet decomposition levels. Linear Discriminan Analysis (LDA) by using Fischer Discriminant was used to reduce the number of characteristics for the feature vector. The Mahalanobis distance was used by the classifier to verify aimed the pertinence of the images for each one the previously given classes. To test actual images, the leave-one-out method was used to the classifier training. The classifier has registered good results, compared to others reports in the corresponding literature.
Mammography is a critical medical imaging procedure concerning resolution due to the features of the details of clinical interest for the diagnosis, such as specifically the microcalcifications. Nonisotropic mammographic systems usually present discrepant dimensions of the effective focal spot between the parallel and the perpendicular axes relative to the tube axis, and this may cause a significant difference in microcalcifications sharpness, depending on the location where they are positioned. We could previously verify that there are regions of the radiation field in nonisotropic systems where sharper images can be obtained, named as Optimum Region. This work is about the determination of the Optimum Region for nonisotropic mammographic systems, by means of developing a procedure which determines the modulation transfer function (MTF) due to the focal spot by simulation. This procedure allows to obtain the MTFs corresponding to any orientation and location of the field, and, by a quantitative analysis of the MTFs curves, the Optimum Region can be determined. Comparisons with phantom images obtained in actual mammographic systems has shown that the Optimum Regions found by the MTF method were in agreement with the subjective visual analysis of the microcalcifications sharpness.
Dense breasts, that usually are characteristic of women less than 40 years old, difficult many times early detection of breast cancer. In this work we present the application of some image processing techniques intended to enhance the contrast in dense breast images, regarding the detection of clustered microcalcifications. The procedure was, firstly, determining in the literature the main techniques used for mammographic images contrast enhancement. The results indicate that, in general: (1) as expected, the overall performance of the CAD scheme for clusters detection decreased when applied exclusively to dense breast images, compared to the application to a set of images without this characteristic; (2) most of the techniques for contrast enhancement used successfully in generic mammography images databases are not able to enhance structures of athirst in databases formed only by dense breasts images, due to the very poor contrast between microcalcifications, for example, and other tissues. These features should stress, therefore, the need of developing a methodology specifically for this type of images in order to provide better conditions to the detection of breast suspicious structures in these group of women.
This work is about the development of a computer-based instrument intended to be applied to quality assurance of mammography systems. It was designed in order to obtain information about kVp, mA, exposure time, dosage, waveform and other important operational characteristics of the radiographic equipment by means of few singular exposures. In addition, the measurement of the focal spot sizes is provided, as in the field center, as in any other location. Indeed the system is designed to determine automatically the position of the sensor relative to the field center, independent on the place where it is positioned on the equipment table. Thus, the instrument is composed by a system which determines automatically the position of the test object relative to the field center, and it is based on silicon sensors coupled to an electronic amplifier system, and to a notebook computer. Calculations made by the software, developed specifically for this application, can correct automatically the shifts of positioning of the test objects relative to the field center. By using the field characteristics equations, it is able to determine also the target angulation on the anode, and intensity distributions characteristics due to the Heel effect. The notebook screen can display, thus, information about the operational characteristics of the radiological system, as well as a diagram of the effective focal spot characteristics along the radiation field. In tests performed with a mammography equipment, the instrument was able to determine the operational conditions with an average accuracy of +/- 1% and the average error of the focal spot sizes measurements was as low as 10 micrometer.
Following procedures used to simulate the image sharpness along the radiation field based on X-rays geometric exposure developed in previous work, here we describe another computer simulation procedure intended to evaluate the influence of any recording system as radiographic film or screen-film combinations, on the image sharpness in mammography. In this current work we take into account the parameters from the recording system besides the radiation projection from the focal spot in order to yield a simulated image on the computer screen relative to the expected image to be obtained in actual conditions with a singular recording system for a singular mammography equipment. The focal spot sizes in all field locations, as well as the respectives intensity distributions, the sensitometric curve for a radiographic film or for a screen-film combination, and also the screen intensifying factor, conversion efficiency, absorption factor and emission spectrum were used as input parameters for the simulation. Simulated images were compared to those obtained with actual mammographic equipment, by using a resolution phantom, and both types of images were in good agreement. The main advantage of this procedure will be the possibility of predicting the image sharpness characteristics for any mammography equipment with any type of recording system without exposure tests.
The magnitude of the image geometric unsharpness depends on the location in the field where the object is hit by the x- ray beam. This phenomenon is known as field characteristics and is caused by the target plane angulation. This yields different effective focal spot sizes and shapes when it is 'seen' from different directions and locations in the x-ray field . Due to the effect of the field characteristics, a more detailed evaluation of focal spot behavior in radiology systems is needed. Hence the focal spot should be evaluated in all field locations, which is very complex with experimental procedures, although feasible by computer simulation. This work describes an algorithm with the aim of determining the size and shape of effective focal spots in any location of the radiation field, on the basis of the focal spot size measurement in the filed center. The results obtained by the program have agreed with those obtained by pinholes matrix exposures in several radiology faculties. The program has proved efficient in computing the size of the focal spot projections for mammography systems, with a standard deviation around 0.03-0.04 mm.
This work proposes a new technique which allows the evaluation of the sharpness of radiologic images by a computer simulation. This simulation provides the image from objects placed at any field position so that the radiologist can determine previously the image sharpness to be obtained in actual exams. It also provides previous knowledge if the system will be able to image a particular detail. The x-ray source is simulated from its representation by the point spread function and it is plotted in a matrix in the image plane. The object is also calculated and plotted in an object matrix. The resultant image matrix is calculated from these two others. The validity of the simulation was verified by comparing the simulated images with the actual images obtained form single phantom exposures.
This paper proposes a method of evaluating x-ray tube focal spots and the corresponding image sharpness by computer simulation based on the transfer functions theory. This theory was chosen due to its quantitative as well as qualitative response for the radiographic systems performance, which provides less subjective evaluations and better predictions about the characteristics of the imaging process. The present method uses as input data the effective focal spot dimensions in the field center and the value of the target angulation. An ideal pinhole which scans the entire radiation field is simulated. It allows to obtain the point spread function (PSFs) for any region of interest. The modulation transfer functions (MTFs) are then determined from 2D Fourier transformation from the PSFs. This provides to evaluate the focal spot projection in all field locations and therefore to predict the sharpness of the associated image. Furthermore the computer simulation reduces greatly the number of practical procedures required for obtaining the data which provides the MTF evaluation of radiographic systems.
Although the transfer function method has been considered the most accurate and complete in evaluating radiological imaging systems performance, it is limited only to a few well equipped laboratories or radiological centers due to the sophisticated experimental apparatus used. Therefore, this paper proposes a new simpler way of performing this quality evaluation, using a computer simulation. This new method provides the MTF determination from a focal spot size and shape knowledge, which can be determined from a pinhole image. Simulation tests were made for an actual mammographic system and for focal spot sizes presented in a previous work by Doi, Fromes & Rossmann. The simulation results were in agreement with those obtain by the conventional method.
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