Automated unsupervised learning‐based clustering approach for effective anomaly detection in brain magnetic resonance imaging (MRI)-Reference-Cited by-同舟云学术

Automated unsupervised learning‐based clustering approach for effective anomaly detection in brain magnetic resonance imaging (MRI)

Published:2020-11-04 Issue:14 Volume:14 Page:3516-3526
ISSN:1751-9659
Container-title:IET Image Processing
language:en
Short-container-title:IET image process

Author:

Govindaraj Vishnuvarthanan¹^ORCID,Thiyagarajan Arunprasath¹,Rajasekaran Pallikonda²,Zhang Yudong³^ORCID,Krishnasamy Rajesh⁴

Affiliation:

1. Department of Biomedical EngineeringKalasalingam Academy of Research and EducationKrishnankoil, Srivilliputur PostVirudhunagar DistrictTamilnaduIndia

2. Department of Electronics and Communication EngineeringKalasalingam Academy of Research and EducationKrishnankoil, Srivilliputur PostVirudhunagar DistrictTamilnaduIndia

3. F26, Informatics BuildingSchool of Informatics, University of Leicester, University RoadLeicesterUK

4. Department of Electrical and Electronics EngineeringKalasalingam Academy of Research and EducationKrishnankoil, Srivilliputur PostVirudhunagar DistrictTamilnaduIndia

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2020.0597

Reference36 articles.

1. An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images

2. Development of a combinational framework to concurrently perform tissue segmentation and tumor identification in T1 - W, T2 - W, FLAIR and MPR type magnetic resonance brain images

3. A complete automated algorithm for segmentation of tissues and identification of tumor region in T1, T2, and FLAIR brain images using optimization and clustering techniques

4. Uncertain fuzzy self-organization based clustering: interval type-2 fuzzy approach to adaptive resonance theory

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