A semisupervised fuzzy GrowCut algorithm for segmenting masses of regions of interest of mammography images
Filipe R. Cordeiro, Wellington Pinheiro dos Santos, Abel G. Silva-Filho · Institution of Engineering and Technology eBooks · 2018
According to the World Health Organization, breast cancer is the most common cancer in women worldwide, becoming one of the most fatal types of cancer. Mammography image analysis is still the most effective imaging technology for breast cancer diagnosis, which is based on texture and shape analysis of mammary lesions. The GrowCut algorithm is a general-purpose segmentation method based on cellular automata, able to perform relatively accurate segmentation through the adequate selection of internal and external seed points. This chapter shows an adaptive semisupervised version of the GrowCut algorithm, based on the modification of the automaton evolution rule by adding a Gaussian fuzzy membership function in order to model nondefined borders. In this proposal, manual selection of seed points of the suspicious lesion is changed by a semiautomatic stage, where just the internal points are selected by using a differential evolution algorithm. We evaluated the proposal using 59 lesion images obtained from MiniMIAS database. The results were compared with the semisupervised state-of-the-art approaches bidimensional empirical mode decomposition, breast mass contour segmentation, wavelet analysis, topographic approach, and marker-controlled watershed (MCW). The results show that fuzzy GrowCut achieves better results for circumscribed, spiculated lesions, and ill-defined lesions, considering the similarity between segmentation results and ground-truth images.