Analysis of supervised and semi-supervised GrowCut applied to segmentation of masses in mammography images
This work addresses breast cancer diagnosis by improving tumor segmentation in mammography images, but it is incremental as it adapts existing methods to a specific domain.
The authors tackled the problem of segmenting tumors in mammography images by evaluating GrowCut and proposing two semi-supervised versions, finding that GrowCut outperformed other techniques like Region Growing and Active Contours in metrics analyzed, with the semi-supervised versions achieving clinically satisfactory segmentation quality.
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.