Advancement in Research Techniques on Medical Imaging Processing for Breast Cancer Detection

With the advancement of medical image processing, the area of the healthcare sector has started receiving the benefits of the modern arena of diagnostic tools to identify the diseases effectively. Cancer is one of the dreaded diseases, where success factor of treatment offered by medical sector is still an unsolved problem. Hence, the success factor of the treatment lies in early stage of the disease or timely detection of the disease. This paper discusses about the advancement being made in the medical image processing towards an effective diagnosis of the breast cancer from the mammogram image in radiology. There has been enough research activity with various sorts of advances techniques being implemented in the past decade. The prime contribution of this manuscript is to showcase the advancement of the technology along with illustration of the effectiveness of the existing literatures with respect to research gap

Microcalcifications Detection Using Image And Signal Processing Techniques For Early Detection Of Breast Cancer

Breast cancer has transformed into a severe health problem around the world. Early diagnosis is an important factor to survive this disease. The earliest detection signs of potential breast cancer that is distinguishable by current screening techniques are the presence of microcalcifications (MCs). MCs are small crystals of calcium apatite and their normal size ranges from 0.1mm to 0.5mm single crystals to groups up to a few centimeters in diameter. They are the first indication of breast cancer in more than 40% of all breast cancer cases, making their diagnosis critical. This dissertation proposes several segmentation techniques for detecting and isolating point microcalcifications: Otsu’s Method, Balanced Histogram Thresholding, Iterative Method, Maximum Entropy, Moment Preserving, and Genetic Algorithm. These methods were applied to medical images to detect microcalcifications. In this dissertation, results from the application of these techniques are presented and their efficiency for early detection of breast cancer is explained. This dissertation also explains theories and algorithms related to these techniques that can be used for breast cancer detection

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

Quantification and segmentation of breast cancer diagnosis: efficient hardware accelerator approach

The mammography image eccentric area is the breast density percentage measurement. The technical challenge of quantification in radiology leads to misinterpretation in screening. Data feedback from society, institutional, and industry shows that quantification and segmentation frameworks have rapidly become the primary methodologies for structuring and interpreting mammogram digital images. Segmentation clustering algorithms have setbacks on overlapping clusters, proportion, and multidimensional scaling to map and leverage the data. In combination, mammogram quantification creates a long-standing focus area. The algorithm proposed must reduce complexity and target data points distributed in iterative, and boost cluster centroid merged into a single updating process to evade the large storage requirement. The mammogram database's initial test segment is critical for evaluating performance and determining the Area Under the Curve (AUC) to alias with medical policy. In addition, a new image clustering algorithm anticipates the need for largescale serial and parallel processing. There is no solution on the market, and it is necessary to implement communication protocols between devices. Exploiting and targeting utilization hardware tasks will further extend the prospect of improvement in the cluster. Benchmarking their resources and performance is required. Finally, the medical imperatives cluster was objectively validated using qualitative and quantitative inspection. The proposed method should overcome the technical challenges that radiologists face

Mammography

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

Aplicación de algoritmos aproximados al diagnóstico/clasificación de enfermedades

El diagnóstico de enfermedades puede formularse como un problema de clasificación, en consecuencia se trata de un problema NP-duro, como es el caso de las dos problemáticas que se pretenden resolver en este trabajo: clasificación, en benigno o maligno, de muestras de tumores de pacientes sospechados de sufrir de cáncer de mama; y clasificación, en negativo o positivo, de muestras de pacientes sospechados de padecer diabetes de tipo II. Por tal motivo, nuestra propuesta consiste en desarrollar algoritmos aproximados basados en perceptrones multicapa, en algoritmos genéticos y en algoritmos que hibridan estas opciones, para realizar diagnósticos confiables (clasificación) con respecto a estas enfermedades. Los experimentos numéricos permiten evaluar y comparar el rendimiento de las distintas propuestas utilizando conjuntos de datos reales. Los resultados muestran que nuestras propuestas logran resultados con errores de clasificación próximos a cero, además de, superar el desempeño de algoritmos propuestos en la literatura.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Aplicación de algoritmos aproximados al diagnóstico/clasificación de enfermedades

El diagnóstico de enfermedades puede formularse como un problema de clasificación, en consecuencia se trata de un problema NP-duro, como es el caso de las dos problemáticas que se pretenden resolver en este trabajo: clasificación, en benigno o maligno, de muestras de tumores de pacientes sospechados de sufrir de cáncer de mama; y clasificación, en negativo o positivo, de muestras de pacientes sospechados de padecer diabetes de tipo II. Por tal motivo, nuestra propuesta consiste en desarrollar algoritmos aproximados basados en perceptrones multicapa, en algoritmos genéticos y en algoritmos que hibridan estas opciones, para realizar diagnósticos confiables (clasificación) con respecto a estas enfermedades. Los experimentos numéricos permiten evaluar y comparar el rendimiento de las distintas propuestas utilizando conjuntos de datos reales. Los resultados muestran que nuestras propuestas logran resultados con errores de clasificación próximos a cero, además de, superar el desempeño de algoritmos propuestos en la literatura.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Aplicación de algoritmos aproximados al diagnóstico/clasificación de enfermedades

El diagnóstico de enfermedades puede formularse como un problema de clasificación, en consecuencia se trata de un problema NP-duro, como es el caso de las dos problemáticas que se pretenden resolver en este trabajo: clasificación, en benigno o maligno, de muestras de tumores de pacientes sospechados de sufrir de cáncer de mama; y clasificación, en negativo o positivo, de muestras de pacientes sospechados de padecer diabetes de tipo II. Por tal motivo, nuestra propuesta consiste en desarrollar algoritmos aproximados basados en perceptrones multicapa, en algoritmos genéticos y en algoritmos que hibridan estas opciones, para realizar diagnósticos confiables (clasificación) con respecto a estas enfermedades. Los experimentos numéricos permiten evaluar y comparar el rendimiento de las distintas propuestas utilizando conjuntos de datos reales. Los resultados muestran que nuestras propuestas logran resultados con errores de clasificación próximos a cero, además de, superar el desempeño de algoritmos propuestos en la literatura.Sociedad Argentina de Informática e Investigación Operativa (SADIO