Breast cancer is currently the most common diagnosed cancer among women and a significant cause of death. Breast density is considered a significant risk factor and an important biomarker influencing the later risk of breast cancer. Therefore, ongoing epidemiological studies using MRI are evaluating quantitatively breast density in young women. One of the challenges is segmenting the breast in order to calculate total breast volume and exclude non-breast surrounding tissues. This thesis describes an automatic 3D breast volume segmentation based on 3D local edge detection using phase congruency and Poisson surface reconstruction to extract the total breast volume in 3D. The boundary localization framework is integrated on a subsequent atlas-based segmentation using a Laplacian framework. The 3D segmentation achieves breast-air and breast-chest wall boundary localization errors with a median of 1.36 mm and 2.68 mm respectively when tested on 409 MRI datasets.

Breast cancer is currently the most common diagnosed cancer among women and a significant cause of death. Breast density is considered a significant risk factor and an important biomarker influencing the later risk of breast cancer. Therefore, ongoing epidemiological studies using MRI are evaluating quantitatively breast density in young women. One of the challenges is segmenting the breast in order to calculate total breast volume and exclude non-breast surrounding tissues. This thesis describes an automatic 3D breast volume segmentation based on 3D local edge detection using phase congruency and Poisson surface reconstruction to extract the total breast volume in 3D. The boundary localization framework is integrated on a subsequent atlas-based segmentation using a Laplacian framework. The 3D segmentation achieves breast-air and breast-chest wall boundary localization errors with a median of 1.36 mm and 2.68 mm respectively when tested on 409 MRI datasets.

The objectives of this study are to determine the presence and extent of brand-level and of price-based segmentation for private label and manufacture brands. / Thesis (MBusiness(Research))--University of South Australia, 2008.

This thesis develops image segmentation methods for the application of automated cervical cancer screening. The traditional approach to automating this task has been to emulate the human method of screening, where every one of the hundreds of thousands of cells on each slide is analysed for abnormality. However, due to the complexity of cervical smear images and the low error tolerance imposed upon the segmentation stage, only limited success has previously been found. A different approach is to detect malignancy associated changes (MACs) in a relatively small sample of the total population of cells. Under this paradigm, the requirement to segment every cell is loosened, but delineation accuracy and error checking become essential. Following a review of generic and cervical smear segmentation, it is concluded that prior work on the traditional approach to automation is not suitable for a MACs solution. However, the previously proposed framework of a dual-magnification system is found to be relevant and is therefore adopted. Here, scene images are first captured at low resolution in order to rapidly locate the cells on a slide. Cells that are deemed to be suitable for further analysis are then imaged at high resolution for the more accurate segmentation of their nuclei. A water immersion algorithm is developed for low resolution scene segmentation. This method achieves a rapid and robust initial segmentation of the scene without the requirement of incorporating extensive a priori knowledge of the image objects. A global minimum searching contour is presented as a top-down method for segmenting the high resolution cell nucleus images where the image objects are well characterised by shape and appearance. This latter method is tested upon 20,000 images and found to achieve an accurate segmentation rate of 99.47%. An error checking method, that uses segmentation stability as an indicator of segmentation success, is developed that is capable of detecting 100% of the failures of the nucleus segmenter, at the expense of discarding only 9% of the data. Throughout this work, contemporary issues in the field of generic image segmentation are presented and some of these are addressed for the cervical smear application. Finally, an avenue of future work is proposed which may lead to the much wider proliferation of computer vision solutions to everyday problems.

Thesis (Ph.D.)--University of Maryland, College Park, 2008. / Adviser: Matthias Ruth. Includes bibliographical references.

Thesis (Ph.D.(Tourism Management))--University of Pretoria, 2009. / Abstract in English. Includes bibliographical references.

Thesis (Ph. D.)--Cornell University, 1994. / Vita. Includes bibliographical references (leaves [205]-211).

Thesis (Ph.D.)--Bowling Green State University, 2005. / Document formatted into pages; contains iv, 295 p. : ill. Includes bibliographical references.

Thesis (M.Sc.(Agric))(Agricultural Economics)--University of Pretoria, 2004. / Title from opening screen (viewed March 22, 2006). Includes summary. Includes bibliographical references.

Thesis (Ph. D.)--Georgia State University, 2007. / Title from title page. Kenneth Bernhardt, committee chair; Detmar Straub, Naveen Donthu, Pam Ellen, committee members. Electronic text (176 p. : ill. (some col.)) : digital, PDF file. Description based on contents viewed Oct. 8, 2007. Includes bibliographical references (p. 161-176).