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Brain Tumor Segmentation Based on Hybrid Clustering and Morphological Operations
Joint Authors
Zhang, Chong
Shen, Xuanjing
Cheng, Hang
Qian, Qingji
Source
International Journal of Biomedical Imaging
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-04-09
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Zhang, Chong& Shen, Xuanjing& Cheng, Hang& Qian, Qingji. 2019. Brain Tumor Segmentation Based on Hybrid Clustering and Morphological Operations. International Journal of Biomedical Imaging،Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1158315
Modern Language Association (MLA)
Zhang, Chong…[et al.]. Brain Tumor Segmentation Based on Hybrid Clustering and Morphological Operations. International Journal of Biomedical Imaging No. 2019 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-1158315
American Medical Association (AMA)
Zhang, Chong& Shen, Xuanjing& Cheng, Hang& Qian, Qingji. Brain Tumor Segmentation Based on Hybrid Clustering and Morphological Operations. International Journal of Biomedical Imaging. 2019. Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1158315
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1158315