Automated unsupervised learning‐based clustering approach for effective anomaly detection in brain magnetic resonance imaging (MRI). Issue 14 (4th November 2020)

• Record Type: Journal Article
• Authors: Govindaraj, Vishnuvarthanan; Thiyagarajan, Arunprasath; Rajasekaran, Pallikonda; Zhang, Yudong; Krishnasamy, Rajesh
• Is Part Of: IET image processing. Volume 14:Issue 14(2020)
• Page Start: 3516
• Page End: 3526
• Publication Date: 2020-11-04
• Subjects: biomedical MRI -- biological tissues -- pattern clustering -- image segmentation -- brain -- fuzzy logic -- diseases -- medical image processing -- self‐organising feature maps -- fuzzy set theory -- tumours -- unsupervised learning; aberrant regions -- patient brain -- noninvasive analysis -- peak signal‐to‐noise ratio -- automated unsupervised learning‐based -- anomaly detection -- brain magnetic resonance imaging -- magnetic resonance brain image segmentation -- medical image analysis -- MR brain image -- tissue structures -- effective demarcation -- abnormal regions -- MR images -- treatment processes -- self‐organising map -- interval type‐2 fuzzy logic clustering -- noise figure 0.234778 dB -- noise figure 54.847 dB; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2020.0597
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : This research study is intended to deliver effective magnetic resonance (MR) brain image segmentation, which is an ambiguous process in the domain of medical image analysis. In general, MR brain image comprises various tissue structures; and an accurate representation of the above‐mentioned regions is essential to have a perfect identification of different grades of tumours, and obtaining effective demarcation of different areas in which the oedema portion is widespread. The accurate representation and identification of the abnormal regions in the MR images can be a vital tool for the radiologists and oncologists to proceed further with the treatment processes. This study aims in developing a novel automated approach that combines self‐organising map and interval type‐2 fuzzy logic clustering, providing ample knowledge to the clinicians in identifying the aberrant regions present in the patient brain. A non‐invasive analysis blended with quicker segmentation results are proffered by the proposed methodology and its functioning abilities have been assessed using comparison metrics such as mean‐squared error (MSE), peak signal‐to‐noise ratio (PSNR), processing time duration, and few other standard metrics. The proposed methodology has offered commendable MSE and PSNR values, which are 0.234778 and 54.847 dB, and it can be undeniably utilised for analysing the patient diseases.