Towards an Unsupervised GrowCut Algorithm for Mammography Segmentation
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-44137-0_9
Reference13 articles.
1. Cordeiro, F.R., Santos, W.P., Silva-Filho, A.G.: An adaptive semi-supervised fuzzy GrowCut algorithm to segment masses of regions of interest of mammographic images. Appl. Soft Comput. 46, 613–628 (2016)
2. Dafni Rose, J., VijayaKumar, K., Singh, L., Sharma, S.K.: Computer-aided diagnosis for breast cancer detection and classification using optimal region growing segmentation with MobileNet model. Concurr. Eng. 30(2), 181–189 (2022)
3. Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)
4. Ge, F., Wang, S., Liu, T.: New benchmark for image segmentation evaluation. J. Electron. Imaging 16(3), 033011 (2007)
5. Ghosh, P., Antani, S.K., Long, L.R., Thoma, G.R.: Unsupervised grow-cut: cellular automata-based medical image segmentation. In: 2011 IEEE First International Conference on Healthcare Informatics, Imaging and Systems Biology, pp. 40–47. IEEE (2011)
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