Application of a Modified Combinational Approach to Brain Tumor Detection in MR Images.
For many years, brain tumor detection has been one of the most essential and competitive issues for medical researchers. Many methods have been developed to detect normal and abnormal tissues in Magnetic Resonance (MR) images. In this work, we present a novel algorithm based on iterative Co-Clusteri...
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 6; pp. 1421 - 1433 |
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| Autores principales: | , |
| Formato: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160503234&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160503234 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2022 vid: 35 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 160503234 157167666 160503234 160503234 10.1007/s10278-022-00653-4 160503234 ppf: 1421 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Application of a Modified Combinational Approach to Brain Tumor Detection in MR Images. aug: au: Farnoosh, Rahman Noushkaran, Hamidreza affil: School of Mathematics, Iran University of Science and Technology, Narmak, 1684613114, Tehran, Tehran, Iran sug: subj: Brain Neoplasms Diagnosis Diagnosis, Brain Methods Magnetic Resonance Imaging Image Enhancement Image Processing, Computer Assisted Human Sensitivity and Specificity Cancer Patients Descriptive Statistics Comparative Studies ab: For many years, brain tumor detection has been one of the most essential and competitive issues for medical researchers. Many methods have been developed to detect normal and abnormal tissues in Magnetic Resonance (MR) images. In this work, we present a novel algorithm based on iterative Co-Clustering and K-Means (ICCK). After image pre-processing and enhancement, this algorithm recognizes the part of the image that contains the tumor and eliminates the unused parts using a modification of the Co-Clustering method. Finally, the K-Means clustering method is adopted to detect the tumor area. The Co-Clustering methods cannot be used directly for the detection of brain tumors because they manipulate the image matrix for the purpose of block clustering. Furthermore, they are incapable of detecting the tumor area correctly and accurately. Such issues are addressed by our proposed methodology. The latent block model (LBM) is applied as the Co-Clustering method in this work. We evaluate the performance of our method on the images that were collected from the BraTS2019 dataset. The sensitivity, specificity, accuracy, and dice similarity coefficient values for our method are 82.41%, 99.74%, 99.28%, and 84.87%, respectively, which shows that the proposed method outperforms the existing methods in the literature. Moreover, it performs much better on complex images. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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