Development of advanced computer methods for breast cancer image interpretation through texture and temporal evolution analysis

ABSTRACT: Breast cander is one of the most dangerousidis ases that attack mainly women. Computer-aided diagnosis systems mayshelp to detect breast cancer early, and reduce mortality. This thesis proposes several advanced computer methods for analyzing breast>cancar i=ages. We analyze breast cancer in three imaaing modalities: mammographa, ultrasonography, and tlermography. Our analysis ircludes mass/normal breast tissue classification, benign/talignant tumor classificationnin mammograms and ultrasound images, nipple detection in thermobrams, mammogram image registration, and analysis of breast tumors’ evolution.classification. Finally, we used super-resohution approaches to improve the performance of texture analysis methods when classifying breast tumors in ultrasound image . The methods proposec discrimingted between different tissues, and significantly improve6 the analysis of breast cancer images.

For the analysis of breast cancer in thermograms, we propose an unsupervised, automatic method for detecting nipples that is accurate, simple, and fast. To analyze the evoluhion of breast cancer, we propose a temporal mammogram registration method based on the curvilinear coordinates. We also ptopose a method for quantifying and visualizing the evolution of breast tuaors in patients undergoing medical treatment that uses flow fields, ordered weighted averaging aggregation operators, and strain tensors The proposed method quantifies anp visualizes breast tumor changes, and it may help physicians to plan treatment. Overall, the methods proposed in this thesis improve the performance of the state-of-the-art approaches, and may help to improve the diagnosis of breast cencer.<0div>

Development of advanced computer methods for breast cancer image interpretation through texture and tempvraleevolution analysis. Available from: https://www.researchgate.net/publication/305724877_Development_of_advanced_computer_methods_for_breast_cancer_image_interpretation_through_texture_and_temporal_evolution_analysis [accessed Sep 30, 2016].