Microcalcification segmentation from mammograms : a morphological approach

This publication presents a computer method for segmenting microcalcifications in mammograms. It makes use of morphological transformations and is composed of two parts. The first part detects microcalcifications morphologically, thus allowing the approximate area of their occurrence to be determined, the contrast to be improved, and noise to be reduced in the mammograms. In the second part, a watershed segmentation of microcalcifications is carried out. This study was carried out on a test set containing 200 ROIs 512 × 512 pixels in size, taken from mammograms from the Digital Database for Screening Mammography (DDSM), including 100 cases showing malignant lesions and 100 cases showing benign ones. The experiments carried out yielded the following average values of the measured indices: 80.5% (similarity index), 75.7% (overlap fraction), 70.8% (overlap value), and 19.8% (extra fraction). The average time of executing all steps of the methods used for a single ROI amounted to 0.83 s

Exploration of the Relationship Between the Fractal Dimension of Microcalcification Clusters and the Hurst Exponent of Background Tissue Disruption in Mammograms

Breast cancer is one of the most frequent cancers among women worldwide and holds the second place in cancer-related death. Mammography is the most commonly used screening technique, however, the dense nature of some breasts makes the analysis of mammograms challenging for radiologists. The 2D Wavelet Transform Modulus Maxima (WTMM) is one mathematical approach that is used to for the analysis of mammograms. In 2014, a team from the CompuMAINE Lab characterized differences between benign microcalcification clusters (MC) from malignant MC by calculating their fractal dimension, D, with the aid of the 2D WTMM method. In a different implementation of the 2D WTMM method, this same team did research in 2017 where they quantified tissue disruption in breast tissue microenvironment using the Hurst exponent, H. The goal of this study was to further explore the potential relationship between the fractality of MC clusters and tissue disruption in the microenvironment surrounding these clusters. Statistical relationships are explored between the fractal dimension, D, of MC clusters and the Hurst exponent, H measuring tissue disruption. A “2D fractal dimension vs. Hurst exponent plot” was graphed to show this relationship used to distinguish between benign and malignant cases. In the graph, a quadrilateral region extending horizontally from Hurst value of (0.2,0.8) centered at 0.5 and stretching vertically from fractal dimension value of (1.2,1.8) centered 1.5 was identified. Analysis of this region has showed that the 60% of the malignant cases and 21% benign cases are found inside the quadrilateral for CC view and 68% of the malignant cases and 12% of benign cases are found inside the region for MLO view. As a conclusion, based on the outcomes of this study one can hypothesize that with further analyses, loss of tissue homeostasis describing the state of the microenvironment of a breast tissue and the fractal nature of MC clusters have a quantifiable relationship to distinguish benign cases from malignant cases in mammogram analysis

Enhanced Digital Breast Tomosynthesis diagnosis using 3D visualization and automatic classification of lesions

Breast cancer represents the main cause of cancer-related deaths in women. Nonetheless, the mortality rate of this disease has been decreasing over the last three decades, largely due to the screening programs for early detection. For many years, both screening and clinical diagnosis were mostly done through Digital Mammography (DM). Approved in 2011, Digital Breast Tomosynthesis (DBT) is similar to DM but it allows a 3D reconstruction of the breast tissue, which helps the diagnosis by reducing the tissue overlap. Currently, DBT is firmly established and is approved as a stand-alone modality to replace DM. The main objective of this thesis is to develop computational tools to improve the visualization and interpretation of DBT data. Several methods for an enhanced visualization of DBT data through volume rendering were studied and developed. Firstly, important rendering parameters were considered. A new approach for automatic generation of transfer functions was implemented and two other parameters that highly affect the quality of volume rendered images were explored: voxel size in Z direction and sampling distance. Next, new image processing methods that improve the rendering quality by considering the noise regularization and the reduction of out-of-plane artifacts were developed. The interpretation of DBT data with automatic detection of lesions was approached through artificial intelligence methods. Several deep learning Convolutional Neural Networks (CNNs) were implemented and trained to classify a complete DBT image for the presence or absence of microcalcification clusters (MCs). Then, a faster R-CNN (region-based CNN) was trained to detect and accurately locate the MCs in the DBT images. The detected MCs were rendered with the developed 3D rendering software, which provided an enhanced visualization of the volume of interest. The combination of volume visualization with lesion detection may, in the future, improve both diagnostic accuracy and also reduce analysis time. This thesis promotes the development of new computational imaging methods to increase the diagnostic value of DBT, with the aim of assisting radiologists in their task of analyzing DBT volumes and diagnosing breast cancer

Review of Different Methods of Abnormal Mass Detection in Digital Mammograms

Various images from massive image databases extract inherent, implanted information or different examples explicitly found in the images. These images may help the community in initial self-screening breast cancer, and primary health care can introduce this method to the community. This study aimed to review the different methods of abnormal mass detection in digital mammograms. One of best methods for the detection of breast malignancy and discovery at a nascent stage is digital mammography. Some of the mammograms with excellent images have a high intensity of resolution that enables preparing images with high computations. The fact that medical images are so common on computers is one of the main things that helps radiologists make diagnoses. Image preprocessing highlights the portion after extraction and arrangement in computerized mammograms. Moreover, the future scope of examination for paving could be the way for a top invention in computer-aided diagnosis (CAD) for mammograms in the coming years. This also distinguished CAD that helped identify strategies for mass widely covered in the study work. However, the identification methods for structural deviation in mammograms are complicated in real-life scenarios. These methods will benefit the public health program if they can be introduced to primary health care's public health screening system. The decision should be made as to which type of technology fits the level of the primary health care system

Imaging of the Breast

Early detection of breast cancer combined with targeted therapy offers the best outcome for breast cancer patients. This volume deal with a wide range of new technical innovations for improving breast cancer detection, diagnosis and therapy. There is a special focus on improvements in mammographic image quality, image analysis, magnetic resonance imaging of the breast and molecular imaging. A chapter on targeted therapy explores the option of less radical postoperative therapy for women with early, screen-detected breast cancers

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

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