1,399 research outputs found

    Computer-aided detection and diagnosis of breast cancer in 2D and 3D medical imaging through multifractal analysis

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    This Thesis describes the research work performed in the scope of a doctoral research program and presents its conclusions and contributions. The research activities were carried on in the industry with Siemens S.A. Healthcare Sector, in integration with a research team. Siemens S.A. Healthcare Sector is one of the world biggest suppliers of products, services and complete solutions in the medical sector. The company offers a wide selection of diagnostic and therapeutic equipment and information systems. Siemens products for medical imaging and in vivo diagnostics include: ultrasound, computer tomography, mammography, digital breast tomosynthesis, magnetic resonance, equipment to angiography and coronary angiography, nuclear imaging, and many others. Siemens has a vast experience in Healthcare and at the beginning of this project it was strategically interested in solutions to improve the detection of Breast Cancer, to increase its competitiveness in the sector. The company owns several patents related with self-similarity analysis, which formed the background of this Thesis. Furthermore, Siemens intended to explore commercially the computer- aided automatic detection and diagnosis eld for portfolio integration. Therefore, with the high knowledge acquired by University of Beira Interior in this area together with this Thesis, will allow Siemens to apply the most recent scienti c progress in the detection of the breast cancer, and it is foreseeable that together we can develop a new technology with high potential. The project resulted in the submission of two invention disclosures for evaluation in Siemens A.G., two articles published in peer-reviewed journals indexed in ISI Science Citation Index, two other articles submitted in peer-reviewed journals, and several international conference papers. This work on computer-aided-diagnosis in breast led to innovative software and novel processes of research and development, for which the project received the Siemens Innovation Award in 2012. It was very rewarding to carry on such technological and innovative project in a socially sensitive area as Breast Cancer.No cancro da mama a deteção precoce e o diagnóstico correto são de extrema importância na prescrição terapêutica e caz e e ciente, que potencie o aumento da taxa de sobrevivência à doença. A teoria multifractal foi inicialmente introduzida no contexto da análise de sinal e a sua utilidade foi demonstrada na descrição de comportamentos siológicos de bio-sinais e até na deteção e predição de patologias. Nesta Tese, três métodos multifractais foram estendidos para imagens bi-dimensionais (2D) e comparados na deteção de microcalci cações em mamogramas. Um destes métodos foi também adaptado para a classi cação de massas da mama, em cortes transversais 2D obtidos por ressonância magnética (RM) de mama, em grupos de massas provavelmente benignas e com suspeição de malignidade. Um novo método de análise multifractal usando a lacunaridade tri-dimensional (3D) foi proposto para classi cação de massas da mama em imagens volumétricas 3D de RM de mama. A análise multifractal revelou diferenças na complexidade subjacente às localizações das microcalci cações em relação aos tecidos normais, permitindo uma boa exatidão da sua deteção em mamogramas. Adicionalmente, foram extraídas por análise multifractal características dos tecidos que permitiram identi car os casos tipicamente recomendados para biópsia em imagens 2D de RM de mama. A análise multifractal 3D foi e caz na classi cação de lesões mamárias benignas e malignas em imagens 3D de RM de mama. Este método foi mais exato para esta classi cação do que o método 2D ou o método padrão de análise de contraste cinético tumoral. Em conclusão, a análise multifractal fornece informação útil para deteção auxiliada por computador em mamogra a e diagnóstico auxiliado por computador em imagens 2D e 3D de RM de mama, tendo o potencial de complementar a interpretação dos radiologistas

    Mammography

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    In this volume, the topics are constructed from a variety of contents: the bases of mammography systems, optimization of screening mammography with reference to evidence-based research, new technologies of image acquisition and its surrounding systems, and case reports with reference to up-to-date multimodality images of breast cancer. Mammography has been lagged in the transition to digital imaging systems because of the necessity of high resolution for diagnosis. However, in the past ten years, technical improvement has resolved the difficulties and boosted new diagnostic systems. We hope that the reader will learn the essentials of mammography and will be forward-looking for the new technologies. We want to express our sincere gratitude and appreciation?to all the co-authors who have contributed their work to this volume

    Prediction of near-term risk of developing breast cancer using computerized features from bilateral mammograms

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    abstract: Asymmetry of bilateral mammographic tissue density and patterns is a potentially strong indicator of having or developing breast abnormalities or early cancers. The purpose of this study is to design and test the global asymmetry features from bilateral mammograms to predict the near-term risk of women developing detectable high risk breast lesions or cancer in the next sequential screening mammography examination. The image dataset includes mammograms acquired from 90 women who underwent routine screening examinations, all interpreted as negative and not recalled by the radiologists during the original screening procedures. A computerized breast cancer risk analysis scheme using four image processing modules, including image preprocessing, suspicious region segmentation, image feature extraction, and classification was designed to detect and compute image feature asymmetry between the left and right breasts imaged on the mammograms. The highest computed area under curve (AUC) is 0.754 ± 0.024 when applying the new computerized aided diagnosis (CAD) scheme to our testing dataset. The positive predictive value and the negative predictive value were 0.58 and 0.80, respectively.NOTICE: this is the author's version of a work that was accepted for publication in . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in , 38, 348-357. DOI: 10.1016/j.compmedimag.2014.03.00

    Microcalcification and Macrocalcification Detection in Mammograms Based on GLCM and ODCM Texture Features Using SVM Classifier

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    Breast cancer is a common cancer in women and the second leading cause of cancer deaths worldwide. Photographing the changes in internal breast structure due to formation of masses and microcalcification for detection of Breast Cancer is known as Mammogram, which are low dose x-ray images. These images play a very significant role in early detection of breast cancer. Usually in pattern recognition texture analysis is used for classification based on content of image or in image segmentation based on variation of intensities of gray scale levels or colours. Similarly texture analysis can also be used to identify masses and microcalcification in mammograms. However Grey Level Co-occurrence Matrices (GLCM) technique introduced by Haralick was initially used in study of remote sensing images. Radiologists f i n d i t d i f f i c u l t to identify the mass in a mammogram, since the masses are surrounded by pectoral muscle and blood vessels. In breast cancer screening, radiologists usually miss approximately 10% - 30% of tumors because of the ambiguous margins of tumors resulting from long-time diagnosis. Computer-aided detection system is developed to aid radiologists in detecting ma mammographic masses which indicate the presence of breast cancer. In this paper the input image is pre-processed initially that includes noise removal, pectoral muscle removal, thresholding, contrast enhancement and suspicious mass is detected and the features are extracted based on the mass detected. A feature extraction method based on grey level co- occurrence matrix and optical density features called GLCM -OD features is used to describe local texture characteristics and the discrete photometric distribution of each ROI. Finally, a support vector machine is used to classify abnormal regions by selecting the individual performance of each feature. The results prove that the proposed system achieves an excellent detection performance using SVM classifier

    Characterization of Mammogram Using Ensemble Classification Technique for Detection of Breast Cancer

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    Breast cancer is one of the most common known cancers in women today. Just like any other form of cancer an early detection of cancer provides better chances of cure. However, it is an arduous task for the radiologists to detect cancer accurately. Thus computer aided diagnosis of the mammographic images is the most popular medium to aid the radiologists in accurately classifying benign and malignant mammographic lesions. In this thesis an efficient approach is presented to classify the mammographic lesion for the detection of breast cancer. In this approach the extracted feature coefficients are balanced using Gaussian distribution. This distribution balances the class unbalanced dataset providing for better classification. This scheme uses Logit Boost classification technique. Logit Boost uses least squared regression cost function on the additive model of Adaboost. The standard MIAS database was used to obtain the mammographic lesions. With a classification accuracy rate of 99.1% and a performance index value of AUC = 0.98 in receiver operating characteristic (ROC) curve the results are pretty much optimal. These results are very promising when compared with existing methods

    A Pictorial Exploration of Mammary Paget Disease: Insights and Perspectives

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    Mammary Paget disease (MPD) is a rare condition primarily affecting adult women, characterized by unilateral skin changes in the nipple–areolar complex (NAC) and frequently associated with underlying breast carcinoma. Histologically, MPD is identified by large intraepidermal epithelial cells (Paget cells) with distinct characteristics. Immunohistochemical profiles aid in distinguishing MPD from other skin conditions. Clinical evaluation and imaging techniques, including magnetic resonance imaging (MRI), are recommended if MPD is suspected, although definitive diagnosis always requires histological examination. This review delves into the historical context, epidemiology, pathogenesis, clinical manifestations, and diagnosis of MPD, emphasizing the need for early detection. The classification of MPD based on pathogenesis is explored, shedding light on its varied presentations. Treatment options, including mastectomy and breast-conserving surgery, are discussed with clear guidelines for different scenarios. Adjuvant therapies are considered, particularly in cases with underlying breast cancer. Prognostic factors are outlined, underlining the importance of early intervention. Looking to the future, emerging techniques, like liquid biopsy, new immunohistochemical and molecular markers, and artificial intelligence-based image analysis, hold the potential to transform MPD diagnosis and treatment. These innovations offer hope for early detection and improved patient care, though validation through large-scale clinical trials is needed

    Estrogen Metabolism, Breast Density, and Breast Cancer

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    BACKGROUND: Estrogen metabolites, sex-steroid hormones, and breast density are associated with breast carcinogenesis. OBJECTIVE: Complete a systematic study of the contribution of two biological measures (breast density and hormone metabolism) to an endocrine-based model of breast cancer risk.METHODS: The study groups included breast cancer-free participants (N=282) in the Study of Osteoporotic Fractures (SOF), and participants in the Mammogram and Masses Study (MAMS), inclusive of 176 cases (55 pre-menopausal, 121 post-menopausal) and 380 controls (124 pre-menopausal, 256 postmenopausal). Sex-steroid hormones, percent breast density, serum concentrations of 2-hydroxyestrone (2-OH) and 16 alpha-hydroxyestrone (16alfa-OH), and breast cancer risk factors were evaluated to determine associations.RESULTS: In SOF,16alfa-OH was positively associated with body mass index (BMI) (r=0.162); however, this association was not significant in multivariate analyses that controlled for the serum sex-steroid hormone concentrations (total estradiol, total testosterone, SHBG). Women who reported a surgical menopause were significantly more likely to have higher levels of 16alfa-OH (OR=(tertile 3 vs tertile 1) 7.37, 95% Confidence Interval (CI) 2.20-24.70), but there was no type of menopause difference with respect to 2-OH tertile. In all MAMS control subjects (N=380), breast density correlated weakly with log-transformed serum concentrations of 16alfa-OH (Pearson correlation coefficient = 0.10, p-value < 0.1). Stratification according to menopausal status substantially reduced or eliminated associations between breast density and the estrogen metabolite concentrations. Logistic regression analyses showed a 3-4 fold increased risk of breast cancer among pre-menopausal women in the highest tertile of breast density compared with those in the lowest tertile of density, even with adjustment for the estrogen metabolites. A statistically non-significant 1.5-fold increased risk of breast cancer in high vs. low tertile of density was observed among post-menopausal women taking hormone therapy (HT) after adjusting for estrogen metabolites, BMI, and age. Breast density did not appear to substantially increase breast cancer risk among post-menopausal women not taking HT. CONCLUSION: In SOF, results did not show consistent associations between risk factors and estrogen metabolites except for a positive association between BMI and 16alfa-OH and surgical menopause and 16alfa-OH. With respect to MAMS, menopausal status may influence substrate estrogen hormone levels primarily, and, estrogen hormone levels may influence breast density secondarily, through pathways not involving the estrogen metabolites. The breast density-breast cancer association remains significant even with adjustment for the estrogen metabolites, at least in pre-menopausal women, suggesting that breast density may relate to breast cancer risk through pathways not involving estrogen metabolism.PUBLIC HEALTH SIGNIFICANCE: Understanding factors that affect breast density and their underlying mechanism is an important public health issue. Such an understanding will help us improve breast cancer screening and may help us identify women who are at an increased risk of breast cancer and for whom prevention strategies may be useful
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