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- Title
Clustering of Brain Tumor Based on Analysis of MRI Images Using Robust Principal Component Analysis (ROBPCA) Algorithm.
- Authors
Hamzenejad, Ali; Ghoushchi, Saeid Jafarzadeh; Baradaran, Vahid
- Abstract
Automated detection of brain tumor location is essential for both medical and analytical uses. In this paper, we clustered brain MRI images to detect tumor location. To obtain perfect results, we presented an unsupervised robust PCA algorithm to clustered images. The proposed method clusters brain MR image pixels to four leverages. The algorithm is implemented for five brain diseases such as glioma, Huntington, meningioma, Pick, and Alzheimer's. We used ten images of each disease to validate the optimal identification rate. According to the results obtained, 2% of the data in the bad leverage part of the image were determined, which acceptably discerned the tumor. Results show that this method has the potential to detect tumor location for brain disease with high sensitivity. Moreover, results show that the method for the Glioma images has approximately better results than others. However, according to the ROC curve for all selected diseases, the present method can find lesion location.
- Subjects
BRAIN anatomy; ALZHEIMER'S disease diagnosis; BRAIN tumor diagnosis; DIGITAL image processing; PREDICTIVE tests; MAGNETIC resonance imaging; GLIOMAS; PICK'S disease of the brain; DIAGNOSTIC imaging; MENINGIOMA; HUNTINGTON disease; RECEIVER operating characteristic curves; ALGORITHMS
- Publication
BioMed Research International, 2021, p1
- ISSN
2314-6133
- Publication type
Article
- DOI
10.1155/2021/5516819