Brain Tumor Segmentation Based on Hybrid Clustering and Morphological Operations
Inference of tumor and edema areas from brain magnetic resonance imaging (MRI) data remains challenging owing to the complex structure of brain tumors, blurred boundaries, and external factors such as noise. To alleviate noise sensitivity and improve the stability of segmentation, an effective hybri...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2019/7305832 |
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| _version_ | 1832548699618672640 |
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| author | Chong Zhang Xuanjing Shen Hang Cheng Qingji Qian |
| author_facet | Chong Zhang Xuanjing Shen Hang Cheng Qingji Qian |
| author_sort | Chong Zhang |
| collection | DOAJ |
| description | Inference of tumor and edema areas from brain magnetic resonance imaging (MRI) data remains challenging owing to the complex structure of brain tumors, blurred boundaries, and external factors such as noise. To alleviate noise sensitivity and improve the stability of segmentation, an effective hybrid clustering algorithm combined with morphological operations is proposed for segmenting brain tumors in this paper. The main contributions of the paper are as follows: firstly, adaptive Wiener filtering is utilized for denoising, and morphological operations are used for removing nonbrain tissue, effectively reducing the method’s sensitivity to noise. Secondly, K-means++ clustering is combined with the Gaussian kernel-based fuzzy C-means algorithm to segment images. This clustering not only improves the algorithm’s stability, but also reduces the sensitivity of clustering parameters. Finally, the extracted tumor images are postprocessed using morphological operations and median filtering to obtain accurate representations of brain tumors. In addition, the proposed algorithm was compared with other current segmentation algorithms. The results show that the proposed algorithm performs better in terms of accuracy, sensitivity, specificity, and recall. |
| format | Article |
| id | doaj-art-c283b3c089284035a6dacbc4d801e8fc |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-c283b3c089284035a6dacbc4d801e8fc2025-02-03T06:13:17ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962019-01-01201910.1155/2019/73058327305832Brain Tumor Segmentation Based on Hybrid Clustering and Morphological OperationsChong Zhang0Xuanjing Shen1Hang Cheng2Qingji Qian3College of Software, Jilin University, Changchun, ChinaCollege of Software, Jilin University, Changchun, ChinaDepartment of Pediatrics, The First Hospital, Jilin University, Changchun, ChinaCollege of Physics, Jilin University, Changchun, ChinaInference of tumor and edema areas from brain magnetic resonance imaging (MRI) data remains challenging owing to the complex structure of brain tumors, blurred boundaries, and external factors such as noise. To alleviate noise sensitivity and improve the stability of segmentation, an effective hybrid clustering algorithm combined with morphological operations is proposed for segmenting brain tumors in this paper. The main contributions of the paper are as follows: firstly, adaptive Wiener filtering is utilized for denoising, and morphological operations are used for removing nonbrain tissue, effectively reducing the method’s sensitivity to noise. Secondly, K-means++ clustering is combined with the Gaussian kernel-based fuzzy C-means algorithm to segment images. This clustering not only improves the algorithm’s stability, but also reduces the sensitivity of clustering parameters. Finally, the extracted tumor images are postprocessed using morphological operations and median filtering to obtain accurate representations of brain tumors. In addition, the proposed algorithm was compared with other current segmentation algorithms. The results show that the proposed algorithm performs better in terms of accuracy, sensitivity, specificity, and recall.http://dx.doi.org/10.1155/2019/7305832 |
| spellingShingle | Chong Zhang Xuanjing Shen Hang Cheng Qingji Qian Brain Tumor Segmentation Based on Hybrid Clustering and Morphological Operations International Journal of Biomedical Imaging |
| title | Brain Tumor Segmentation Based on Hybrid Clustering and Morphological Operations |
| title_full | Brain Tumor Segmentation Based on Hybrid Clustering and Morphological Operations |
| title_fullStr | Brain Tumor Segmentation Based on Hybrid Clustering and Morphological Operations |
| title_full_unstemmed | Brain Tumor Segmentation Based on Hybrid Clustering and Morphological Operations |
| title_short | Brain Tumor Segmentation Based on Hybrid Clustering and Morphological Operations |
| title_sort | brain tumor segmentation based on hybrid clustering and morphological operations |
| url | http://dx.doi.org/10.1155/2019/7305832 |
| work_keys_str_mv | AT chongzhang braintumorsegmentationbasedonhybridclusteringandmorphologicaloperations AT xuanjingshen braintumorsegmentationbasedonhybridclusteringandmorphologicaloperations AT hangcheng braintumorsegmentationbasedonhybridclusteringandmorphologicaloperations AT qingjiqian braintumorsegmentationbasedonhybridclusteringandmorphologicaloperations |