  Breast cancer is already one of the most common form of cancer worldwide.
Mammography image analysis is still the most effective diagnostic method to
promote the early detection of breast cancer. Accurately segmenting tumors in
digital mammography images is important to improve diagnosis capabilities of
health specialists and avoid misdiagnosis. In this work, we evaluate the
feasibility of applying GrowCut to segment regions of tumor and we propose two
GrowCut semi-supervised versions. All the analysis was performed by evaluating
the application of segmentation techniques to a set of images obtained from the
Mini-MIAS mammography image database. GrowCut segmentation was compared to
Region Growing, Active Contours, Random Walks and Graph Cut techniques.
Experiments showed that GrowCut, when compared to the other techniques, was
able to acquire better results for the metrics analyzed. Moreover, the proposed
semi-supervised versions of GrowCut was proved to have a clinically
satisfactory quality of segmentation.
