Abstract
Breast cancer is one of the most common causes of deaths among the women in the world. Digital mammography is the most effective technique for early detection of masses or abnormalities which is related to breast cancer. In this paper we present an effective approach on mammography images using texture features and region growing algorithm for breast cancer segmentation in mammograms that can be implemented in Computer Aided Diagnosis (CADx) system. The proposed method used automatic seed selection by extracting the statistical features. The extraction of the textural features (contrast, energy and homogeneity) of region of interest (ROI) is done by using gray level co-occurrence matrices (GLCM) which is constructed at four different directions (0°, 45°, 90°,135°) for each block from region of interest. The results of this method prove that the Gray Level Co-occurrence Matrices at each direction with a window size of 8 × 8 give significant texture information to distinguish between masses and non-masses and then automatic seed point selection.
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Saleck, M.M., El Moutaouakkil, A. (2018). Automatic Seeded Region Growing Based on Texture Features for Mass Segmentation in Digital Mammography. In: Noreddine, G., Kacprzyk, J. (eds) International Conference on Information Technology and Communication Systems. ITCS 2017. Advances in Intelligent Systems and Computing, vol 640. Springer, Cham. https://doi.org/10.1007/978-3-319-64719-7_28
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DOI: https://doi.org/10.1007/978-3-319-64719-7_28
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