Title from PDF of title page. / Document formatted into pages; contains 63 pages. / Thesis (M.S.C.S.)--University of South Florida, 2003. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: The work presented here is an important component of an on going project of developing an automated mass classification system for breast cancer screening and diagnosis for Digital Mammogram applications. Specifically, in this work the task of automatically separating mass tissue from normal breast tissue given a region of interest in a digitized mammogram is investigated. This is the crucial stage in developing a robust automated classification system because the classification depends on the accurate assessment of the tumor-normal tissue border as well as information gathered from the tumor area. In this work the Expectation Maximization (EM) method is developed and applied to high resolution digitized screen-film mammograms with the aim of segmenting normal tissue from mass tissue. / ABSTRACT: Both the raw data and summary data generated by Laws' texture analysis are investigated. Since the ultimate goal is robust classification, the merits of the tissue segmentation are assessed by its impact on the overall classification performance. Based on the 300 image dataset consisting of 97 malignant and 203 benign cases, a 63% sensitivity and 89% specificity was achieved. Although, the segmentation requires further investigation, the development and related computer coding of the EM algorithm was successful. The method was developed to take in account the input feature correlation. This development allows other researchers at this facility to investigate various input features without having the intricate understanding of the EM approach. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.

The work presented here is an important component of an on going project of developing an automated mass classification system for breast cancer screening and diagnosis for Digital Mammogram applications. Specifically, in this work the task of automatically separating mass tissue from normal breast tissue given a region of interest in a digitized mammogram is investigated. This is the crucial stage in developing a robust automated classification system because the classification depends on the accurate assessment of the tumor-normal tissue border as well as information gathered from the tumor area. In this work the Expectation Maximization (EM) method is developed and applied to high resolution digitized screen-film mammograms with the aim of segmenting normal tissue from mass tissue. Both the raw data and summary data generated by Laws' texture analysis are investigated. Since the ultimate goal is robust classification, the merits of the tissue segmentation are assessed by its impact on the overall classification performance. Based on the 300 image dataset consisting of 97 malignant and 203 benign cases, a 63% sensitivity and 89% specificity was achieved. Although, the segmentation requires further investigation, the development and related computer coding of the EM algorithm was successful. The method was developed to take in account the input feature correlation. This development allows other researchers at this facility to investigate various input features without having the intricate understanding of the EM approach.

Aquesta tesi està emmarcada dins la detecció precoç de masses, un dels símptomes més clars del càncer de mama, en imatges mamogràfiques. Primerament, s'ha fet un anàlisi extensiu dels diferents mètodes de la literatura, concloent que aquests mètodes són dependents de diferent paràmetres: el tamany i la forma de la massa i la densitat de la mama. Així, l'objectiu de la tesi és analitzar, dissenyar i implementar un mètode de detecció robust i independent d'aquests tres paràmetres. Per a tal fi, s'ha construït un patró deformable de la massa a partir de l'anàlisi de masses reals i, a continuació, aquest model és buscat en les imatges seguint un esquema probabilístic, obtenint una sèrie de regions sospitoses. Fent servir l'anàlisi 2DPCA, s'ha construït un algorisme capaç de discernir aquestes regions són realment una massa o no. La densitat de la mama és un paràmetre que s'introdueix de forma natural dins l'algorisme. / This thesis deals with the detection of masses in mammographic images. As a first step, Regions of Interests (ROIs) are detected in the image using templates containing a probabilistic contour shape obtained from training over an annotated set of masses. Firstly, PCA is performed over the training set, and subsequently the template is formed as an average of the gradient of eigenmasses weighted by the top eigenvalues. The template can be deformed according to each eigenmass coefficient. The matching is formulated in a Bayesian framework, where the prior penalizes the deformation, and the likelihood requires template boundaries to agree with image edges. In the second stage, the detected ROIs are classified into being false positives or true positives using 2DPCA, where the new training set now contains ROIs with masses and ROIs with normal tissue. Mass density is incorporated into the whole process by initially classifying the two training sets according to breast density. Methods for breast density estimation are also analyzed and proposed. The results are obtained using different databases and both FROC and ROC analysis demonstrate a better performance of the approach relative to competing methods.