Brain Tumor Segmentation Based on Hybrid Clustering and Morphological Operations

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ID: 10476
2019
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Ranked #6 of 29 articles by views in international journal of biomedical imaging

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Abstract
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
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chong2019braininternational Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zhang, Chong;Shen, Xuanjing;Cheng, Hang;Qian, Qingji;Zhang, Chong;Shen, Xuanjing;Cheng, Hang;Qian, Qingji;
Journal international journal of biomedical imaging
Year 2019
DOI
10.1155/2019/7305832
URL
https://www.hindawi.com/journals/ijbi/2019/7305832/abs/
Keywords Keywords not found

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