Grammatical Fireworks Algorithm Method for Breast Lesion Segmentation in DCE-MR Images
Dipak Kumar Patra1, Sukumar Mondal2, Prakash Mukherjee3
1D. K. Patra*, Research Centre of Natural and Applied Sciences, Department of Computer Science, Raja Narendralal Khan Women’s College, Midnapore (West Bengal), India.
2S. Mondal, Department of Mathematics, Raja Narendralal Khan Women’s College, Paschim Medinipur (West Bengal), India.
3P. Mukherjee, Department of Mathematics, Hijli College Kharagpur (West Bengal), India.
Manuscript received on May 22, 2021. | Revised Manuscript received on May 26, 2021. | Manuscript published on May 30, 2021. | PP: 170-182 | Volume-10 Issue-7, May 2021 | Retrieval Number: 100.1/ijitee.G90540510721| DOI: 10.35940/ijitee.G9054.0510721
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: For cancer detection and tissue characterization, DCE-MRI segmentation and lesion detection is a critical image analysis task. To segment breast MR images for lesion detection, a hard-clustering technique with Grammatical Fireworks algorithm (GFWA) is proposed in this paper. GFWA is a Swarm Programming (SP) system for automatically generating computer programs in any language. GFWA is used to create the cluster core for clustering the breast MR images in this article. The presence of noise and intensity inhomogeneities in MR images complicates the segmentation process. As a result, the MR images are denoised at the start, and strength inhomogeneities are corrected in the preprocessing stage. The proposed GFWA-based clustering technique is used to segment the preprocessed MR images. Finally, from the segmented images, the lesions are removed. The proposed approach is tested on 5 patients’ 25 DCE-MRI slices. The proposed method’s experimental findings are compared to those of the Grammatical Swarm (GS)-based clustering technique and the K-means algorithm. The proposed method outperforms other approaches in terms of both quantitative and qualitative results.
Keywords: Breast Cancer, DCE-MRI, Clustering, Warm Programming, Grammatical Fireworks Algorithm.