  According to the World Health Organization, breast cancer is the most common
form of cancer in women. It is the second leading cause of death among women
round the world, becoming the most fatal form of cancer. Mammographic image
segmentation is a fundamental task to support image analysis and diagnosis,
taking into account shape analysis of mammary lesions and their borders.
However, mammogram segmentation is a very hard process, once it is highly
dependent on the types of mammary tissues. In this work we present a new
semi-supervised segmentation algorithm based on the modification of the GrowCut
algorithm to perform automatic mammographic image segmentation once a region of
interest is selected by a specialist. In our proposal, we used fuzzy Gaussian
membership functions to modify the evolution rule of the original GrowCut
algorithm, in order to estimate the uncertainty of a pixel being object or
background. The main impact of the proposed method is the significant reduction
of expert effort in the initialization of seed points of GrowCut to perform
accurate segmentation, once it removes the need of selection of background
seeds. We also constructed an automatic point selection process based on the
simulated annealing optimization method, avoiding the need of human
intervention. The proposed approach was qualitatively compared with other
state-of-the-art segmentation techniques, considering the shape of segmented
regions. In order to validate our proposal, we built an image classifier using
a classical multilayer perceptron. We used Zernike moments to extract segmented
image features. This analysis employed 685 mammograms from IRMA breast cancer
database, using fat and fibroid tissues. Results show that the proposed
technique could achieve a classification rate of 91.28\% for fat tissues,
evidencing the feasibility of our approach.
