U.S.-Norway Comparison of Interval Breast Cancers

Background: Publications from the International Breast Screening Network (IBSN) have shown that varying definitions create hurdles for comparison of screening performance. Interval breast cancer rates are particularly affected. Objective: to test whether variations in definition of interval cancer rates (ICR) affect comparisons of international ICR, specific to a comparison of ICR in Norway and North Carolina (NC). Methods: An interval cancer (IC) was defined as a cancer diagnosed following a negative screening mammogram in a defined follow-up period. ICR was calculated for women ages 50-69, at subsequent screening in Norway and NC, during the time period 1996 - 2002. ICR was defined using three different denominators (negative screens, negative final assessments and all screens) and three different numerators (DCIS, invasive cancer and all cancers). ICR was then calculated with two methods: 1) number of ICs divided by the number of screens, and ICs divided by the number of women-years at risk for IC. Results: There were no differences in ICR depending on the definition used. In the 1-12 month follow up period ICR (based on number of screens) were: 0.53, 0.54, and 0.54 for Norway; and 1.20, 1.25 and 1.17 for NC, for negative screens, negative final assessment and all screens, respectively: The same trend was seen for 13-24 and 1-24 months follow-up. Using women-years for the analysis did not change the trend. ICR was higher in NC compared to Norway under all definitions and in all follow-up time periods, regardless of calculation method. Conclusion: The ICR within or between Norway and NC did not differ by definition used. ICR were higher in NC than Norway. There are many potential explanations for the difference.

Mammographic density in asymptomatic menopausal women: correlation with clinical and sonographic findings

OBJECTIVE: To evaluate mammographic breast density in asymptomatic menopausal women in correlation with clinical and sonographic findings. MATERIALS AND METHODS: Mammograms and clinical and sonographic findings of 238 asymptomatic patients were retrospectively reviewed in the period from February/2022 to June/2006. The following variables were analyzed: mammographic density patterns, sonographic findings, patients' age, parity, body mass index and use of hormone replacement therapy. RESULTS: Age, parity and body mass index showed a negative correlation with breast density pattern, while use of hormone replacement therapy showed a positive correlation. Supplementary breast ultrasonography was performed in 103 (43.2%) patients. Alterations which could not be visualized at mammography were found in 34 (33%) of them, most frequently in women with breast density patterns 3 and 4. CONCLUSION: The authors concluded that breast density patterns were influenced by age, parity, body mass index and time of hormone replacement therapy. Despite not having found any malignant abnormality in the studied cases, the authors have observed a predominance of benign sonographic abnormalities in women with high breast density patterns and without mammographic abnormalities, proving the relevance of supplementary ultrasonography to identify breast lesions in such patients.

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

Les inégalités sociales dans le dépistage du cancer du sein, quand le système s'en mêle...

Réalisé en cotutelle avec Dr. Béatrice Godard, Professeure titulaire à l'Université de Montréal.

Fractal based techniques for classification of mammograms and identification of microcalcifications

After skin cancer, breast cancer accounts for the second greatest number of cancer diagnoses in women. Currently the etiologies of breast cancer are unknown, and there is no generally accepted therapy for preventing it. Therefore, the best way to improve the prognosis for breast cancer is early detection and treatment. Computer aided detection systems (CAD) for detecting masses or micro-calcifications in mammograms have already been used and proven to be a potentially powerful tool , so the radiologists are attracted by the effectiveness of clinical application of CAD systems. Fractal geometry is well suited for describing the complex physiological structures that defy the traditional Euclidean geometry, which is based on smooth shapes. The major contribution of this research include the development of • A new fractal feature to accurately classify mammograms into normal and normal (i)With masses (benign or malignant) (ii) with microcalcifications (benign or malignant) • A novel fast fractal modeling method to identify the presence of microcalcifications by fractal modeling of mammograms and then subtracting the modeled image from the original mammogram. The performances of these methods were evaluated using different standard statistical analysis methods. The results obtained indicate that the developed methods are highly beneficial for assisting radiologists in making diagnostic decisions. The mammograms for the study were obtained from the two online databases namely, MIAS (Mammographic Image Analysis Society) and DDSM (Digital Database for Screening Mammography.

classification and content based retrieval of digital mammograms and placental sonograms

Image processing has been a challenging and multidisciplinary research area since decades with continuing improvements in its various branches especially Medical Imaging. The healthcare industry was very much benefited with the advances in Image Processing techniques for the efficient management of large volumes of clinical data. The popularity and growth of Image Processing field attracts researchers from many disciplines including Computer Science and Medical Science due to its applicability to the real world. In the meantime, Computer Science is becoming an important driving force for the further development of Medical Sciences. The objective of this study is to make use of the basic concepts in Medical Image Processing and develop methods and tools for clinicians’ assistance. This work is motivated from clinical applications of digital mammograms and placental sonograms, and uses real medical images for proposing a method intended to assist radiologists in the diagnostic process. The study consists of two domains of Pattern recognition, Classification and Content Based Retrieval. Mammogram images of breast cancer patients and placental images are used for this study. Cancer is a disaster to human race. The accuracy in characterizing images using simplified user friendly Computer Aided Diagnosis techniques helps radiologists in detecting cancers at an early stage. Breast cancer which accounts for the major cause of cancer death in women can be fully cured if detected at an early stage. Studies relating to placental characteristics and abnormalities are important in foetal monitoring. The diagnostic variability in sonographic examination of placenta can be overlooked by detailed placental texture analysis by focusing on placental grading. The work aims on early breast cancer detection and placental maturity analysis. This dissertation is a stepping stone in combing various application domains of healthcare and technology.

Enhancement of Calcifications in Mammograms using Volterra Series based Quadratic Filter

The paper summarizes the design and implementation of a quadratic edge detection filter, based on Volterra series, for enhancing calcifications in mammograms. The proposed filter can account for much of the polynomial nonlinearities inherent in the input mammogram image and can replace the conventional edge detectors like Laplacian, gaussian etc. The filter gives rise to improved visualization and early detection of microcalcifications, which if left undetected, can lead to breast cancer. The performance of the filter is analyzed and found superior to conventional spatial edge detectors

Classification of Mammograms and DW T based Detection of Microcalcification

Cancer treatment is most effective when it is detected early and the progress in treatment will be closely related to the ability to reduce the proportion of misses in the cancer detection task. The effectiveness of algorithms for detecting cancers can be greatly increased if these algorithms work synergistically with those for characterizing normal mammograms. This research work combines computerized image analysis techniques and neural networks to separate out some fraction of the normal mammograms with extremely high reliability, based on normal tissue identification and removal. The presence of clustered microcalcifications is one of the most important and sometimes the only sign of cancer on a mammogram. 60% to 70% of non-palpable breast carcinoma demonstrates microcalcifications on mammograms [44], [45], [46].WT based techniques are applied on the remaining mammograms, those are obviously abnormal, to detect possible microcalcifications. The goal of this work is to improve the detection performance and throughput of screening-mammography, thus providing a ‘second opinion ‘ to the radiologists. The state-of- the- art DWT computation algorithms are not suitable for practical applications with memory and delay constraints, as it is not a block transfonn. Hence in this work, the development of a Block DWT (BDWT) computational structure having low processing memory requirement has also been taken up.

A New Fast Fractal Modeling Approach for the Detection of Microcalcifications in Mammograms

In this paper, a novel fast method for modeling mammograms by deterministic fractal coding approach to detect the presence of microcalcifications, which are early signs of breast cancer, is presented. The modeled mammogram obtained using fractal encoding method is visually similar to the original image containing microcalcifications, and therefore, when it is taken out from the original mammogram, the presence of microcalcifications can be enhanced. The limitation of fractal image modeling is the tremendous time required for encoding. In the present work, instead of searching for a matching domain in the entire domain pool of the image, three methods based on mean and variance, dynamic range of the image blocks, and mass center features are used. This reduced the encoding time by a factor of 3, 89, and 13, respectively, in the three methods with respect to the conventional fractal image coding method with quad tree partitioning. The mammograms obtained from The Mammographic Image Analysis Society database (ground truth available) gave a total detection score of 87.6%, 87.6%, 90.5%, and 87.6%, for the conventional and the proposed three methods, respectively.

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

Archivo docente en imágenes del seno: mamografía digital. Fundación santa fe de Bogotá.

Se conformó el primer archivo docente digital de patología específica del seno en la modalidad de mamografía a nivel nacional, el cuál permitirá el entrenamiento de radiólogos y residentes de radiología según el sistema de lectura BI-RADS, buscando la unificación de criterios y mejoría de las competencias en la interpretación de las imágenes con la finalidad de aumentar la detección temprana del carcinoma de seno

Sistema auxiliar na análise de mamogramas digitais

This work proposes the development of a Computer System for Analysis of Mammograms SCAM, that aids the doctor specialist in the identification and analysis of existent lesions in digital mammograms. The computer system for digital mammograms processing will make use of a group of techniques of Digital Image Processing (DIP), with the purpose of aiding the medical professional to extract the information contained in the mammogram. This system possesses an interface of easy use for the user, allowing, starting from the supplied mammogram, a group of processing operations, such as, the enrich of the images through filtering techniques, the segmentation of areas of the mammogram, the calculation the area of the lesions, thresholding the lesion, and other important tools for the medical professional's diagnosis. The Wavelet Transform will used and integrated into the computer system, with the objective of allowing a multiresolution analysis, thus supplying a method for identifying and analyzing microcalcifications

Fatores associados a não realização da mamografia e do exame clínico das mamas: um estudo de base populacional em Campinas, São Paulo, Brasil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Comparação das estimativas de prevalência de indicadores de saúde no Município de Campinas, São Paulo, Brasil, nos anos de 2001/2002 (ISA-SP) e 2008/2009 (ISA-Camp)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Classification of masses in mammographic image using wavelet domain features and polynomial classifier

Breast cancer is the most common cancer among women. In CAD systems, several studies have investigated the use of wavelet transform as a multiresolution analysis tool for texture analysis and could be interpreted as inputs to a classifier. In classification, polynomial classifier has been used due to the advantages of providing only one model for optimal separation of classes and to consider this as the solution of the problem. In this paper, a system is proposed for texture analysis and classification of lesions in mammographic images. Multiresolution analysis features were extracted from the region of interest of a given image. These features were computed based on three different wavelet functions, Daubechies 8, Symlet 8 and bi-orthogonal 3.7. For classification, we used the polynomial classification algorithm to define the mammogram images as normal or abnormal. We also made a comparison with other artificial intelligence algorithms (Decision Tree, SVM, K-NN). A Receiver Operating Characteristics (ROC) curve is used to evaluate the performance of the proposed system. Our system is evaluated using 360 digitized mammograms from DDSM database and the result shows that the algorithm has an area under the ROC curve Az of 0.98 ± 0.03. The performance of the polynomial classifier has proved to be better in comparison to other classification algorithms. © 2013 Elsevier Ltd. All rights reserved.