131 research outputs found
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
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 Ã
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
extraÃdas por análise multifractal caracterÃsticas 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
COMPUTER AIDED SYSTEM FOR BREAST CANCER DIAGNOSIS USING CURVELET TRANSFORM
Breast cancer is a leading cause of death among women worldwide. Early detection is the key for improving breast cancer prognosis. Digital mammography remains one of the most suitable tools for early detection of breast cancer. Hence, there are strong needs for the development of computer aided diagnosis (CAD) systems which have the capability to help radiologists in decision making. The main goal is to increase the diagnostic accuracy rate. In this thesis we developed a computer aided system for the diagnosis and detection of breast cancer using curvelet transform. Curvelet is a multiscale transform which possess directionality and anisotropy, and it breaks some inherent limitations of wavelet in representing edges in images. We started this study by developing a diagnosis system. Five feature extraction methods were developed with curvelet and wavelet coefficients to differentiate between different breast cancer classes. The results with curvelet and wavelet were compared. The experimental results show a high performance of the proposed methods and classification accuracy rate achieved 97.30%.
The thesis then provides an automatic system for breast cancer detection. An automatic thresholding algorithm was used to separate the area composed of the breast and the pectoral muscle from the background of the image. Subsequently, a region growing algorithm was used to locate the pectoral muscle and suppress it from the breast. Then, the work concentrates on the segmentation of region of interest (ROI). Two methods are suggested to accomplish the segmentation stage: an adaptive thresholding method and a pattern matching method. Once the ROI has been identified, an automatic cropping is performed to extract it from the original mammogram. Subsequently, the suggested feature extraction methods were applied to the segmented ROIs. Finally, the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers were used to determine whether the region is abnormal or normal. At this level, the study focuses on two abnormality types (mammographic masses and architectural distortion). Experimental results show that the introduced methods have very high detection accuracies. The effectiveness of the proposed methods has been tested with Mammographic Image Analysis Society (MIAS) dataset. Throughout the thesis all proposed methods and algorithms have been applied with both curvelet and wavelet for comparison and statistical tests were also performed. The overall results show that curvelet transform performs better than wavelet and the difference is statistically significant
Detecting microcalcification clusters in digital mammograms: Study for inclusion into computer aided diagnostic prompting system
Among signs of breast cancer encountered in digital mammograms radiologists point to microcalcification clusters (MCCs). Their detection is a challenging problem from both medical and image processing point of views. This work presents two concurrent methods for MCC detection, and studies their possible inclusion to a computer aided diagnostic prompting system. One considers Wavelet Domain Hidden Markov Tree (WHMT) for modeling microcalcification edges. The model is used for differentiation between MC and non-MC edges based on the weighted maximum likelihood (WML) values. The classification of objects is carried out using spatial filters. The second method employs SUSAN edge detector in the spatial domain for mammogram segmentation. Classification of objects as calcifications is carried out using another set of spatial filters and Feedforward Neural Network (NN). A same distance filter is employed in both methods to find true clusters. The analysis of two methods is performed on 54 image regions from the mammograms selected randomly from DDSM database, including benign and cancerous cases as well as cases which can be classified as hard cases from both radiologists and the computer perspectives. WHMT/WML is able to detect 98.15% true positive (TP) MCCs under 1.85% of false positives (FP), whereas the SUSAN/NN method achieves 94.44% of TP at the cost of 1.85% for FP. The comparison of these two methods suggests WHMT/WML for the computer aided diagnostic prompting. It also certifies the low false positive rates for both methods, meaning less biopsy tests per patient
Microcalcification Detection Applying Artificial Neural Networks and Mathematical Morphology in Digital Mammograms
Breast cancer is one of the leading causes to women mortality in the world and early detection is an important means to reduce the mortality rate. The presence of microcalcifications clusters has been considered as a very important indicator of malignant types of breast cancer and its detection is important to prevent and treat the disease. This paper presents an alternative and effective approach in order to detect microcalcifications clusters in digitized mammograms based on the synergy of the image processing, pattern recognition and artificial intelligence. The mathematical morphology is an image processing technique used for the purpose of image enhancement. A k-means algorithm is used to cluster the data based on the features vectors and finally an artificial neural network-based classifier is applied and the classification performance is evaluated by a ROC curve. Experimental results indicate that the percentage of correct classification was 99.72%, obtaining 100% true positive (sensitivity) and 99.67% false positive (specificity), with the best classifier proposed. In case of the best classifier, we obtained a performance evaluation of classification of Az = 0.987
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