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

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Performance of a CAD scheme applied to images obtained from mammographic film digitization and full-field digital mammography (FFDM).

This work has as purpose to compare the effects of a CAD scheme applied to digitized and \ud direct digital mamograms sets. A routine designed to be applied to mammogram in \ud DICOM standard was developed and a schema based on the Watershed Transform to \ud masses detection was applied to 252 ROIs from 130 digitized mammograms, resulting in \ud 92% of true positive and 10% of false positives. For clustered microcalcifications \ud detection, another procedure was applied to 165 ROIs from 120 mammograms, resulting in \ud 93% of true positive and 16% of false positive. By using the same procedures to 154 \ud digital mammograms obtained from FFDM, the rates have shown a little decrease in the \ud scheme performance: 89% of true positive and 16% of false positive for masses detection; \ud 90% of true positive and 27% of false positive for clusters detection. Although the tests \ud with digital mammograms have been carried with a smaller number of images and \ud different cases compared to the digitized ones, including several dense breasts images, the \ud results can be considered comparable, mainly forclustered microcalcifications detection \ud with a difference of only 3% between the sensibility rates for the both images sets. Another \ud important feature affecting these results is the contrast difference between the two images \ud set. This implies the need of extensive investigations not only with a larger number of \ud cases from FFDM but also on the parameters related to its image acquisition as well as to \ud its corresponding processing.Este trabalho tem como objetivo comparar os resultados de um esquema CAD aplicado em \ud conjunto de mamografias digitalizadas e em um conjunto de mamografias obtidas de um \ud mamógrafo digital. Para extrair as imagens do padrão DICOM, padrão utilizado pelos \ud mamógrafos digitais, uma rotina computacional foi desenvolvida. Para a detecção de \ud nódulos, um esquema baseado em Transforma Watershed foi aplicado a 252 regiões de \ud interesse (ROIs) de 130 mamografias digitalizadas, resultando em 92% de verdadeiro \ud positivo e 10%de falsos positivos. Para a detecção de microcalcificações agrupadas, outro \ud procedimento foi aplicado a165 ROIs extraídas de 120 mamografias digitalizadas, \ud resultando em 93% de verdadeiro positivo e 16% de falso positivo. Ao utilizar os mesmos \ud procedimentos para154 mamografias digitais obtidas a partir de um FFDM, as taxas \ud mostraram uma diminuição pequena no desempenho: 89% do verdadeiro positivo e 16% \ud de falso positivo para a detecção de nódulos, e 90% de verdadeiro positivo e 27% de falsos \ud positivo para a detecção de clusters de microcalcificações. Embora os testes com \ud mamografias digitais tenham sido realizados com um menor número de imagens e casos \ud diferentes em comparação com os digitalizados, incluindo várias imagens de mamas \ud densas, os resultados podem ser considerados comparáveis, principalmente para a detecção \ud de clusters de microcalcificações com uma diferença de apenas 3% entre as taxas de \ud sensibilidade para as imagens dos dois conjuntos. Outra característica importante que afeta \ud esses resultados é a diferença de contraste dos dois grupos de imagens analisados. Isto \ud implica na necessidade de extensas investigações não só com um maior número de casos \ud de mamografias digitais, mas também um estudo sobre os parâmetros relacionados a \ud aquisição da imagem, bem como para o seu processamentoCNPqFAPESPHospital of Clinics in Botucatu/S

Fabrication And Characterization Of Microcalcification Breast Phantom For Image Quality Analysis

This study aims to improve the early diagnosis of breast cancer through the application of image processing techniques based on the MATLAB algorithms to enhance the visibility of microcalcifications (MCs) in Full Field Digital Mammography (FFDM). Various polyvinyl alcohol (PVAL) composites phantoms were produced through freezing and thawing method to mimic the physical and radiological properties of different categories of breast tissue in line with the BIRADS classification. The density, elemental composition, effective atomic number (Zeff), electron density (ꝭeff), mass attenuation coefficients of the PVAL-based phantoms and MC features (CaCO3/graphite) were determined. The microstructure and CT number of the PVAL were also studied. The 50/50 water/ethanol-based 10 wt% PVAL (E50), the water-based 10 wt% PVAL (P10), 10 wt% PVAL mixed with 4% graphite powder (G4), and the heterogeneous phantom (H) had physical and radiological properties suitable to mimic BIRADS B, C, D, and a heterogeneous breast tissue respectively. Phantom E50, P10, G4, and H recorded densities of 0.952 ± 0.011 g/cm3, 1.056 ± 0.002 g/cm3, 1.081 ± 0.002 g/cm3, and 1.025 ± 0.006 g/cm3 respectively, their Zeff and ꝭeff ranged from 7.148 to 7.418 and 3.189 X 1023/cm3 to 3.209 X 1023/cm3 respectively

A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms.

Breast cancer is one of the most common death causes amongst women all over the world. Early detection of breast cancer plays a critical role in increasing the survival rate. Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer. Though there is a considerable success with mammography in biomedical imaging, detecting suspicious areas remains a challenge because, due to the manual examination and variations in shape, size, other mass morphological features, mammography accuracy changes with the density of the breast. Furthermore, going through the analysis of many mammograms per day can be a tedious task for radiologists and practitioners. One of the main objectives of biomedical imaging is to provide radiologists and practitioners with tools to help them identify all suspicious regions in a given image. Computer-aided mass detection in mammograms can serve as a second opinion tool to help radiologists avoid running into oversight errors. The scientific community has made much progress in this topic, and several approaches have been proposed along the way. Following a bottom-up narrative, this paper surveys different scientific methodologies and techniques to detect suspicious regions in mammograms spanning from methods based on low-level image features to the most recent novelties in AI-based approaches. Both theoretical and practical grounds are provided across the paper sections to highlight the pros and cons of different methodologies. The paper's main scope is to let readers embark on a journey through a fully comprehensive description of techniques, strategies and datasets on the topic