Computer-aided analysis and interpretation of breast imaging data
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Early detection of breast cancer on screening mammograms is crucial to reduce mortality rates. Computer-aided detection (CADe) systems for mammography are of great importance since they have been shown to positively assist radiologists in detecting early cancer. However, one area where CADe systems for mammography need improvement is in the early detection and annotation of spiculated lesions, which may represent invasive malignancies, and hence, early detection is crucial. Spicule annotation is important since it can yield useful discriminative information about the suspect lesion location on the mammogram and can also provide rich visual evidence to the interpreting radiologist to make the right follow-up decision. However, spicule annotation is a non-trivial task since spicules are fine scale curvilinear structures that are often not clearly visible amidst the surrounding breast parenchyma. The first contribution of this dissertation is an active contour algorithm called snakules for the annotation of spicules on mammography. Observer studies with experienced radiologists to evaluate the performance of snakules demonstrate the potential of the algorithm as an annotation tool that could be used to augment existing spiculated mass CADe systems. Mammography suffers from a major limitation: the 3-D to 2-D projection process results in anatomical noise due to overlapping of out of plane tissue structures, which hinders both radiologists and CADe systems in finding early cancers. This has motivated the development of 3-D breast imaging in the form of breast tomosynthesis, stereoscopic (stereo) mammography, and breast computed tomography (CT) to augment mammography for early cancer detection. Our second contribution is a novel computational stereo model for estimating a dense disparity map from a pair of stereo mammograms. This problem is very important since this is the first step towards elucidating 3-D information that is essential for conducting 3-D digital analysis on the stereo mammogram images. Nearly all of the 3-D structural information of interest on a stereo mammogram exists as a complex network of multi-layered, heavily occluded curvilinear structures, which is unlike what is seen on optical images of the real world. Our proposed stereo model employs a new singularity index as a constraint in a global optimization framework to obtain better estimates of disparity along critical curvilinear structures. The new singularity index is an important contribution of this work. In-depth theoretical analyses and experiments on several real world images demonstrate the efficacy of the index for detecting multi-scale curvilinear structures. Experiments on synthetic images with known ground truth and on real stereo mammograms highlight the advantages of the proposed stereo model over the canonical stereo model. The final contribution of this dissertation is an observer study, which demonstrates the feasibility of viewing breast tomosynthesis projection images stereoscopically. Unlike stereo mammogram images, each tomosynthesis projection image is acquired at a much lower dose. Stereo viewing of tomosynthesis projection images has the potential to reveal the 3-D structure of the breast, unlike the current cine or slice-by slice viewing modes. The results from our study suggest that stereo viewing could be a viable reading mode for breast tomosynthesis data in the future.