Feature analysis methods for intelligent breast imaging parameter optimisation using CMOS active pixel sensors

This thesis explores the concept of real time imaging parameter optimisation in digital mammography using statistical information extracted from the breast during a scan. Transmission and Energy dispersive x-ray diffraction (EDXRD) imaging were the two very different imaging modalities investigated. An attempt to determine if either could be used in a real time imaging system enabling differentiation between healthy and suspicious tissue regions was made. This would consequently enable local regions (potentially cancerous regions) within the breast to be imaged using optimised imaging parameters. The performance of possible statistical feature functions that could be used as information extraction tools were investigated using low exposure breast tissue images. The images were divided into eight regions of interest, seven regions corresponding to suspicious tissue regions marked by a radiologist, where the final region was obtained from a location in the breast consisting solely of healthy tissue. Results obtained from this investigation showed that a minimum of 82% of the suspicious tissue regions were highlighted in all images, whilst the total exposure incident on the sample was reduced in all instances. Three out of the seven (42%) intelligent images resulted in an increased contrast to noise ratio (CNR) compared to the conventionally produced transmission images. Three intelligent images were of similar diagnostic quality to their conventional counter parts whilst one was considerably lower. EDXRD measurements were made on breast tissue samples containing potentially cancerous tissue regions. As the technique is known to be able to distinguish between breast tissue types, diffraction signals were used to produce images corresponding to three suspicious tissue regions consequently enabling pixel intensities within the images to be analysed. A minimum of approximately 70% of the suspicious tissue regions were highlighted in each image, with at least 50% of each image remaining unsuspicious, hence was imaged with a reduced incident exposure

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

Computer-Aided, Multi-Modal, and Compression Diffuse Optical Studies of Breast Tissue

Diffuse Optical Tomography and Spectroscopy permit measurement of important physiological parameters non-invasively through ~10 cm of tissue. I have applied these techniques in measurements of human breast and breast cancer. My thesis integrates three loosely connected themes in this context: multi-modal breast cancer imaging, automated data analysis of breast cancer images, and microvascular hemodynamics of breast under compression. As per the first theme, I describe construction, testing, and the initial clinical usage of two generations of imaging systems for simultaneous diffuse optical and magnetic resonance imaging. The second project develops a statistical analysis of optical breast data from many spatial locations in a population of cancers to derive a novel optical signature of malignancy; I then apply this data-derived signature for localization of cancer in additional subjects. Finally, I construct and deploy diffuse optical instrumentation to measure blood content and blood flow during breast compression; besides optics, this research has implications for any method employing breast compression, e.g., mammography

Complexity Reduction in Image-Based Breast Cancer Care

The diversity of malignancies of the breast requires personalized diagnostic and therapeutic decision making in a complex situation. This thesis contributes in three clinical areas: (1) For clinical diagnostic image evaluation, computer-aided detection and diagnosis of mass and non-mass lesions in breast MRI is developed. 4D texture features characterize mass lesions. For non-mass lesions, a combined detection/characterisation method utilizes the bilateral symmetry of the breast s contrast agent uptake. (2) To improve clinical workflows, a breast MRI reading paradigm is proposed, exemplified by a breast MRI reading workstation prototype. Instead of mouse and keyboard, it is operated using multi-touch gestures. The concept is extended to mammography screening, introducing efficient navigation aids. (3) Contributions to finite element modeling of breast tissue deformations tackle two clinical problems: surgery planning and the prediction of the breast deformation in a MRI biopsy device

Deep learning in medical imaging and radiation therapy

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/1/mp13264_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/2/mp13264.pd