Modeling and Classifying Breast Tissue Density in Mammograms

We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature. We studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover we demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification. We show the results of tissue classification over the MIAS and DDSM datasets. We compare our method with approaches that classified these same datasets showing a better performance of our proposal

A Novel Breast Tissue Density Classification Methodology

It has been shown that the accuracy of mammographic abnormality detection methods is strongly dependent on the breast tissue characteristics, where a dense breast drastically reduces detection sensitivity. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. Here, we describe the development of an automatic breast tissue classification methodology, which can be summarized in a number of distinct steps: 1) the segmentation of the breast area into fatty versus dense mammographic tissue; 2) the extraction of morphological and texture features from the segmented breast areas; and 3) the use of a Bayesian combination of a number of classifiers. The evaluation, based on a large number of cases from two different mammographic data sets, shows a strong correlation ( and 0.67 for the two data sets) between automatic and expert-based Breast Imaging Reporting and Data System mammographic density assessment

Automatic classification of breast density

A recent trend in digital mammography is computer-aided diagnosis systems, which are computerised tools designed to assist radiologists. Most of these systems are used for the automatic detection of abnormalities. However, recent studies have shown that their sensitivity is significantly decreased as the density of the breast increases. This dependence is method specific. In this paper we propose a new approach to the classification of mammographic images according to their breast parenchymal density. Our classification uses information extracted from segmentation results and is based on the underlying breast tissue texture. Classification performance was based on a large set of digitised mammograms. Evaluation involves different classifiers and uses a leave-one-out methodology. Results demonstrate the feasibility of estimating breast density using image processing and analysis techniques

A new approach to the classification of mammographic masses and normal breast tissue

A new approach to mammographic mass detection is presented in this paper. Although different algorithms have been proposed for such a task, most of them are application dependent. In contrast, our approach makes use of a kindred topic in computer vision adapted to our particular problem. In this sense, we translate the eigenfaces approach for face detection/classification problems to a mass detection. Two different databases were used to show the robustness of the approach. The first one consisted on a set of 160 regions of interest (RoIs) extracted from the MIAS database, being 40 of them with confirmed masses and the rest normal tissue. The second set of RoIs was extracted from the DDSM database, and contained 196 RoIs containing masses and 392 with normal, but suspicious regions. Initial results demonstrate the feasibility of using such approach with performances comparable to other algorithms, with the advantage of being a more general, simple and cost-effective approach

Breast cancer incidence and overdiagnosis in Catalonia (Spain)

Early detection of breast cancer (BC) with mammography may cause overdiagnosis and overtreatment, detecting tumors which would remain undiagnosed during a lifetime. The aims of this study were: first, to model invasive BC incidence trends in Catalonia (Spain) taking into account reproductive and screening data; and second, to quantify the extent of BC overdiagnosis. We modeled the incidence of invasive BC using a Poisson regression model. Explanatory variables were: age at diagnosis and cohort characteristics (completed fertility rate, percentage of women that use mammography at age 50, and year of birth). This model also was used to estimate the background incidence in the absence of screening. We used a probabilistic model to estimate the expected BC incidence if women in the population used mammography as reported in health surveys. The difference between the observed and expected cumulative incidences provided an estimate of overdiagnosis.Incidence of invasive BC increased, especially in cohorts born from 1940 to 1955. The biggest increase was observed in these cohorts between the ages of 50 to 65 years, where the final BC incidence rates more than doubled the initial ones. Dissemination of mammography was significantly associated with BC incidence and overdiagnosis. Our estimates of overdiagnosis ranged from 0.4% to 46.6%, for women born around 1935 and 1950, respectively.Our results support the existence of overdiagnosis in Catalonia attributed to mammography usage, and the limited malignant potential of some tumors may play an important role. Women should be better informed about this risk. Research should be oriented towards personalized screening and risk assessment tools

Una mamografía le podría salvar la vida

This Spanish language sheet tells about mammograms.

Caracterització i diagnosi de masses en imatges de mamografia

Els objectius del projecte es divideixen en tres blocs: Primerament, realitzar una segmentació automàtica del contorn d'una imatge on hi ha una massa central. Tot seguit, a partir del contorn trobat, caracteritzar la massa. I finalment, utilitzant les característiques anteriors classificar la massa en benigne o maligne. En el projecte s'utilitza el Matlab com a eina de programació. Concretament les funcions enfocades al processat de imatges del toolbox de Image processing (propi de Matlab) i els classificadors de la PRTools de la Delft University of Technology

Microcalcification evaluation in computer assisted diagnosis for digital mammography

In order to develop applications for z;isual interpretation of medical images, the early detection and evaluation of microcalcifications in digital mammograms is verg important since their presence is often associated with a high incidence of breast cancers. Accurate classification into benign and malignant groups would help improve diagnostic sensitivity as well as reduce the number of unnecessa y biopsies. The challenge here is the selection of the useful features to distinguish benign from malignant micro calcifications. Our purpose in this work is to analyse a microcalcification evaluation method based on a set of shapebased features extracted from the digitised mammography. The segmentation of the microcalcifications is performed using a fixed-tolerance region growing method to extract boundaries of calcifications with manually selected seed pixels. Taking into account that shapes and sizes of clustered microcalcifications have been associated with a high risk of carcinoma based on digerent subjective measures, such as whether or not the calcifications are irregular, linear, vermiform, branched, rounded or ring like, our efforts were addressed to obtain a feature set related to the shape. The identification of the pammeters concerning the malignant character of the microcalcifications was performed on a set of 146 mammograms with their real diagnosis known in advance from biopsies. This allowed identifying the following shape-based parameters as the relevant ones: Number of clusters, Number of holes, Area, Feret elongation, Roughness, and Elongation. Further experiments on a set of 70 new mammogmms showed that the performance of the classification scheme is close to the mean performance of three expert radiologists, which allows to consider the proposed method for assisting the diagnosis and encourages to continue the investigation in the sense of adding new features not only related to the shape

Otimização de imagens de mamografia computadorizada

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

X-ray scattering and diffraction enhanced imaging studies of in vitro breast tissues

This thesis is a study of the x-ray scattering properties of tissues and tumours of the breast. Clinical radiography is based on the absorption of the x-rays when passing right through the human body and gives information about the densities of the tissues. Besides being absorbed, x-rays may change their direction within the tissues due to elastic scattering or even to refraction. The phenomenon of scattering is a nuisance to radiography in general, and to mammography in particular, because it reduces the quality of the images. However, scattered x-rays bear very useful information about the structure of the tissues at the supra-molecular level. Some pathologies, like breast cancer, produce alterations to the structures of the tissues, being especially evident in collagen-rich tissues. On the other hand, the change of direction due to refraction of the x-rays on the tissue boundaries can be mapped. The diffraction enhanced imaging (DEI) technique uses a perfect crystal to convert the angular deviations of the x-rays into intensity variations, which can be recorded as images. This technique is of especial interest in the cases were the densities of the tissues are very similar (like in mammography) and the absorption images do not offer enough contrast. This thesis explores the structural differences existing in healthy and pathological collagen in breast tissue samples by the small-angle x-ray scattering (SAXS) technique and compares these differences with the morphological information found in the DEI images and the histo-pathology of the same samples. Several breast tissue samples were studied by SAXS technique in the European Synchrotron Radiation Facility (ESRF) in Grenoble, France. Scattering patterns of the different tissues of the breast were acquired and compared with the histology of the samples. The scattering signals from adipose tissue (fat), connective tissue (collagen) and necrotic tissue were identified. Moreover, a clear distinction could be done between the scattering signals from healthy collagen and from collagen from an invasive tumour. Scattering from collagen is very characteristic. It includes several scattering peaks and scattering features that carry information about the size and the spacing of the collagen fibrils in the tissues. It was found that the collagen fibrils in invaded tumours were thinner and had a d-spacing length 0,7% longer that fibrils from healthy tumours. The scattering signals from the breast tissues were compared with the histology by building colour-coded maps across the samples. They were also imaged with the DEI technique. There was a total agreement between the scattering maps, the morphological features seen in the images and the information of the histo- pathological examination. The thesis demonstrates that the x-ray scattering signal can be used to characterize tissues and that it carries important information about the pathological state of the breast tissues, thus showing the potential of the SAXS technique as a possible diagnostic tool for breast cancer.

No evidence for association of ataxia-telangiectasia mutated gene T2119C and C3161G amino acid substitution variants with risk of breast cancer

Background There is evidence that certain mutations in the double-strand break repair pathway ataxia-telangiectasia mutated gene act in a dominant-negative manner to increase the risk of breast cancer. There are also some reports to suggest that the amino acid substitution variants T2119C Ser707Pro and C3161G Pro1054Arg may be associated with breast cancer risk. We investigate the breast cancer risk associated with these two nonconservative amino acid substitution variants using a large Australian population-based case–control study. Methods The polymorphisms were genotyped in more than 1300 cases and 600 controls using 5' exonuclease assays. Case–control analyses and genotype distributions were compared by logistic regression. Results The 2119C variant was rare, occurring at frequencies of 1.4 and 1.3% in cases and controls, respectively (P = 0.8). There was no difference in genotype distribution between cases and controls (P = 0.8), and the TC genotype was not associated with increased risk of breast cancer (adjusted odds ratio = 1.08, 95% confidence interval = 0.59–1.97, P = 0.8). Similarly, the 3161G variant was no more common in cases than in controls (2.9% versus 2.2%, P = 0.2), there was no difference in genotype distribution between cases and controls (P = 0.1), and the CG genotype was not associated with an increased risk of breast cancer (adjusted odds ratio = 1.30, 95% confidence interval = 0.85–1.98, P = 0.2). This lack of evidence for an association persisted within groups defined by the family history of breast cancer or by age. Conclusion The 2119C and 3161G amino acid substitution variants are not associated with moderate or high risks of breast cancer in Australian women.