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an automatic mammogram system: from screening to diagnosis

Breast cancer is the most common type of cancer in women worldwide, and the leading cause of death from cancer in women, especially those between 40 and 55 years of age. Screening mammography is performed in the asymptomatic population to detect early signs of breast cancer such as masses, calcifications, bilateral asymmetry and architectural distortion. Diagnostic mammography is performed on patients who have already demonstrated abnormal clinical findings. Both screening and diagnostic mammography are traditionally performed by radiologists who visually inspect mammograms. Manual inspection is a tiring and tedious task prone to human error. In this way, the search for Computer-Aided Detection and Diagnosis (CAD) techniques has been encouraged. The present thesis describes an effort to develop image and machine learning methods to help radiologists in the analysis of mammogram images. Contributions were made in the different phases, including: (1) pre-processing, (2) screening, (3) detection of suspicious regions, (4) characterization of suspicious regions, and (5) classification. All the techniques were thoroughly evaluated using a database of full field digital mammogram images that, along with the images, contains meta-data information like breast density, BI-RADS (Breast Imaging Reporting And Data System) assessment and very accurate segmentations of suspicious regions. This database is known as INbreast. Image manipulation can have a strong impact on the success of subsequent tasks. A typical pre-processing applied to mammogram images is the removal of the pectoral muscle region. Two methods for segmentation of the pectoral muscle are presented in this thesis, namely polar coordinates and the shortest path (SPPC) and shortest path with endpoints learnt by SVMs (SPLE). After pectoral muscle subtraction, the mammogram exam goes through the screening block. Since it has been observed that CAD performance depends on breast density, breasts are first classified as fatty or dense. Then, for each breast type, a specific classification block is designed to determine if the breast exam is suspicious. An extensive evaluation by testing a large set of features in combination with several classifiers was performed. The best density classification results were achieved with a k-Nearest Neighbours (kNN) classifier and using a feature vector consisting of statistic features extracted from both views. The best classifiers selected for the classification of images as suspicious or non-suspicious were also kNNs, but using Gabor features. Based on the outcome of the screening stage, non-suspicious patients return to the normal screening program advised by different countries while suspicious exams are sent to diagnosis. During this analysis it may be useful to direct the attention of the specialist to regions of the image that may be problematic. The two most common findings seen in mammogram images are calcifications and masses. Due to their different characteristics (size, intensity, shape, border contrast, etc.) different methods were used for each type of finding. An algorithm based on Bayesian surprise was developed for calcification detection, while an Iris filter followed by a closed contour segmentation method made in the original coordinate system was used to detect and segment masses.