NDLI: Method of multi-region tumour segmentation in brain MRI images using grid-based segmentation and weighted bee swarm optimisation

Content Provider	IET Digital Library
Author	Mano, Abhisha Anand, Swaminathan
Abstract	Multi-region segmentation plays a major role in numerous medical diagnostics especially brain tumour identification and classification in Magnetic Resonance Imaging (MRI). Brain tumour segmentation is used in medical field for early diagnostics and detection of tumour. The main goal of this work is to improve the performance of detection by using grid based techniques with Weighted Bee Swarm Intelligence and K-means clustering. This technique is more effective due to hybrid combination of segmentation and optimisation as it seems to possess specific tasks of image information and detection to obtain a detailed and accurate image analysis. Grid based segmentation balance overall computation time and reduces complexity. Weighted Bee Swarm Optimisation is used to optimise segmentation parameters to get maximum performance. The various informative regions such as cerebrospinal fluid, grey matter, white matter are segmented by using proposed algorithm which will be most useful to study and characterise the tumour. The experimental outcomes show that the proposed strategy enhances performance measures in terms of sensitivity and specificity analysis. The performance of this technique is also improved by a factor of 1.5%.
Starting Page	2901
Ending Page	2910
Page Count	10
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 12, Oct (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1234
Journal	IET Image Processing
Publisher Date	2020-07-29
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Biology And Medical Computing Biomedical Magnetic Resonance Imaging Biomedical MRI Brain Brain MRI Image Segmentation Brain Tumour Classification Brain Tumour Identification Brain Tumour Segmentation Cerebrospinal Fluid Computer Vision And Image Processing Technique Computerised Digital Image Processing Technique Decision-making Time Grey Matter Grid-based Segmentation Grid-based Technique Image Analysis Image Information Image Segmentation Informative Region K-means Clustering Algorithm Magnetic Resonance Imaging Medical Diagnostics Medical Image Processing Medical Magnetic Resonance Imaging And Spectroscopy Multiregion Tumour Segmentation Optical, Image And Video Signal Processing Optimisation Optimisation Technique Patient Diagnostic Method And Instrumentation Pattern Clustering Segmentation Parameter Sensitivity Analysis Specificity Analysis Spectroscopy Swarm Intelligence Tumour Region Tumours Weighted Bee Swarm Intelligence Weighted Bee Swarm Optimisation White Matter
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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