Abstract
Breast cancer causes the highest number of deaths among all types of cancers in women. Therefore, it is necessary to properly diagnose the breast cancer for treatment of the patients. At present, dynamic contrast-enhanced (DCE)-MRI is widely used biomedical imaging technique to diagnose the breast cancer. In this paper, a breast DCE-MRI segmentation method using modified hard-clustering with Fireworks Algorithm (FWA) is developed for lesion detection. The segmentation of DCE-MRI suffers from noise and intensity inhomogeneities present in the images. On the outset, MR images are denoised using anisotropic diffusion filter and intensity inhomogeneities are corrected using max filter-based method in the preprocessing step. After that, images are segmented using hard-clustering technique with FWA algorithm. Finally, the lesions are extracted from the segmented images in the postprocessing step. The results of the proposed segmentation method are compared with segmentation methods based on Particle Swarm Optimizer (PSO), and K-means algorithms. The experimental results demonstrate that the proposed method outperforms other methods in the segmentation of breast DCE-MRI.
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Si, T., Mukhopadhyay, A. (2021). Breast DCE-MRI Segmentation for Lesion Detection Using Clustering with Fireworks Algorithm. In: Gao, XZ., Kumar, R., Srivastava, S., Soni, B.P. (eds) Applications of Artificial Intelligence in Engineering. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-4604-8_2
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