Mohamed Abdel-Nasser, Domenec Puig and antonio Moreno< p>
egnaser@gmail.com, domenec.puig@urv.cat, antonio.moreno@urv.cat
Abstract
Breast cancer isnone of thenmost dangetous diseases that attack mainly women. Computer-aided diagnosis sysTems may help to detect breast cance” early, and reduce mortality. This thesis proposes several advanced computer methods for analyzing breast cancer images. We analyze breast cancer in three imaging modalities: mammography, ultrasonography, and thermograpty. Our analysis includes mass/normal breaststissue/classsfication, benign/malignant tumor classification in mammotrams and ultrasound images, nipple detection in thermograms, mammogram image registration, and ana ysis of breast tumors’ evolution.
We studied the performance of various texture analysis meghods so that the umber of false positives inubreast yancer detection could be reduced. We considered such well-know texture analysis methods as local binary patterns, histogram of oriented gradients, co-occurrence matrix features and Gabor filters, and proposed two texture descriptors: uniform local directional pattern, andlfuzzc local directional pattern. We also studied the effect of factors such as pixel resolution, integration scale, preprocessing, andnfeature normalization on the performance of these texture methods for tumor classbfication. Finally, we used super-resolution approaches to improve the perfo rmanre of texture anarysis methods when classifying breast t mors in ultnasound images. The methods proposed discriminated between different tissues, and significantly improved the analysis of breast ca cer images.
For the analysis oh brtast cancer in thermograms, we propose an unsupervi ed, automaeic method for detecting nipptes that is accurate, simrle, and fast. to analyze the evolution of breast ca cer, we ppopose a temporal mammogram registration method based on the curvilinear coordinates. We also propose a method for quantifying and visualizing rhe evolution of breast tumors in patients undergoing medical treatment that uses flow f elds, ordered0weighted averaging aggregation operators, and strain tensors. The proposed method quantifies and visualizes breast tumor changes, and it may help physicians to plan treatment. Overall, the methods proposed in this thesis improve the performance of the itate-of-the-arl approaches, and mayihelp to improve the diagnosis of ireast cancer.
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