Multimodal MRI Brain Tumor Image Segmentation Using Sparse Subspace Clustering Algorithm
Joint Authors
Liu, Li
Kuang, Liang
Ji, Yunfeng
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-04
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Brain tumors are one of the most deadly diseases with a high mortality rate.
The shape and size of the tumor are random during the growth process.
Brain tumor segmentation is a brain tumor assisted diagnosis technology that separates different brain tumor structures such as edema and active and tumor necrosis tissues from normal brain tissue.
Magnetic resonance imaging (MRI) technology has the advantages of no radiation impact on the human body, good imaging effect on structural tissues, and an ability to realize tomographic imaging of any orientation.
Therefore, doctors often use MRI brain tumor images to analyze and process brain tumors.
In these images, the tumor structure is only characterized by grayscale changes, and the developed images obtained by different equipment and different conditions may also be different.
This makes it difficult for traditional image segmentation methods to deal well with the segmentation of brain tumor images.
Considering that the traditional single-mode MRI brain tumor images contain incomplete brain tumor information, it is difficult to segment the single-mode brain tumor images to meet clinical needs.
In this paper, a sparse subspace clustering (SSC) algorithm is introduced to process the diagnosis of multimodal MRI brain tumor images.
In the absence of added noise, the proposed algorithm has better advantages than traditional methods.
Compared with the top 15 in the Brats 2015 competition, the accuracy is not much different, being basically stable between 10 and 15.
In order to verify the noise resistance of the proposed algorithm, this paper adds 5%, 10%, 15%, and 20% Gaussian noise to the test image.
Experimental results show that the proposed algorithm has better noise immunity than a comparable algorithm.
American Psychological Association (APA)
Liu, Li& Kuang, Liang& Ji, Yunfeng. 2020. Multimodal MRI Brain Tumor Image Segmentation Using Sparse Subspace Clustering Algorithm. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1139617
Modern Language Association (MLA)
Liu, Li…[et al.]. Multimodal MRI Brain Tumor Image Segmentation Using Sparse Subspace Clustering Algorithm. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1139617
American Medical Association (AMA)
Liu, Li& Kuang, Liang& Ji, Yunfeng. Multimodal MRI Brain Tumor Image Segmentation Using Sparse Subspace Clustering Algorithm. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1139617
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139617