Breast Cancer: Modelling and Detection

This paper reviews a number of the mathematical models used in cancer modelling and then chooses a specific cancer, breast carcinoma, to illustrate how the modelling can be used in aiding detection. We then discuss mathematical models that underpin mammographic image analysis, which complements models of tumour growth and facilitates diagnosis and treatment of cancer. Mammographic images are notoriously difficult to interpret, and we give an overview of the primary image enhancement technologies that have been introduced, before focusing on a more detailed description of some of our own recent work on the use of physics-based modelling in mammography. This theoretical approach to image analysis yields a wealth of information that could be incorporated into the mathematical models, and we conclude by describing how current mathematical models might be enhanced by use of this information, and how these models in turn will help to meet some of the major challenges in cancer detection

Analysis of temporal changes of mammographic features: Computerâ aided classification of malignant and benign breast masses

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135117/1/mp2242.pd

Automated registration of breast lesions in temporal pairs of mammograms for interval change analysisâ local affine transformation for improved localization

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134991/1/mp6134.pd

Bilateral analysis based false positive reduction for computerâ aided mass detection

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134848/1/mp6612.pd

Shape description and matching using integral invariants on eccentricity transformed images

Matching occluded and noisy shapes is a problem frequently encountered in medical image analysis and more generally in computer vision. To keep track of changes inside the breast, for example, it is important for a computer aided detection system to establish correspondences between regions of interest. Shape transformations, computed both with integral invariants (II) and with geodesic distance, yield signatures that are invariant to isometric deformations, such as bending and articulations. Integral invariants describe the boundaries of planar shapes. However, they provide no information about where a particular feature lies on the boundary with regard to the overall shape structure. Conversely, eccentricity transforms (Ecc) can match shapes by signatures of geodesic distance histograms based on information from inside the shape; but they ignore the boundary information. We describe a method that combines the boundary signature of a shape obtained from II and structural information from the Ecc to yield results that improve on them separately

Comparison of similarity measures for the task of template matching of masses on serial mammograms

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134879/1/mp1892.pd

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

Mammography Techniques and Review

Mammography remains at the backbone of medical tools to examine the human breast. The early detection of breast cancer typically uses adjunct tests to mammogram such as ultrasound, positron emission mammography, electrical impedance, Computer-aided detection systems and others. In the present digital era it is even more important to use the best new techniques and systems available to improve the correct diagnosis and to prevent mortality from breast cancer. The first part of this book deals with the electrical impedance mammographic scheme, ultrasound axillary imaging, position emission mammography and digital mammogram enhancement. A detailed consideration of CBR CAD System and the availability of mammographs in Brazil forms the second part of this book. With the up-to-date papers from world experts, this book will be invaluable to anyone who studies the field of mammography