Clustering of Brain Tumor Based on Analysis of MRI Images Using Robust Principal Component Analysis (ROBPCA) Algorithm.
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 M...
| Published in: | BioMed Research International pp. 1 - 12 |
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| Main Authors: | , , |
| Format: | diagnostic images equations & formulas research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
9/4/2021
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=152271357&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152271357 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 9/4/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 152271357 152271357 152271357 10.1155/2021/5516819 152271357 ppf: 1 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Clustering of Brain Tumor Based on Analysis of MRI Images Using Robust Principal Component Analysis (ROBPCA) Algorithm. aug: au: Hamzenejad, Ali Ghoushchi, Saeid Jafarzadeh Baradaran, Vahid affil: Department of Industrial Engineering, Islamic Azad University, Tehran North Branch, Tehran, Iran sug: subj: Brain Anatomy and Histology Brain Neoplasms Diagnosis Magnetic Resonance Imaging Methods Algorithms Evaluation Image Processing, Computer Assisted Predictive Value of Tests Evaluation Human Radiographic Image Enhancement Methods Glioma Diagnosis Huntington's Disease Diagnosis Meningioma Diagnosis Pick Disease of the Brain Diagnosis Alzheimer's Disease Diagnosis Sensitivity and Specificity ROC Curve ab: 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. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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