NDLI: Segmentation of brain magnetic resonance images using a novel fuzzy clustering based method

Content Provider	IET Digital Library
Author	Tripathi, Prasun Chandra Bag, Soumen
Abstract	Segmentation of tissues in brain magnetic resonance (MR) images has a crucial role in computer-aided diagnosis (CAD) of various brain diseases. However, due to the complex anatomical structure and the presence of intensity non-uniformity (INU) artefact, the segmentation of brain MR images is considered as a complicated task. In this study, the authors propose a novel locally influenced fuzzy C-means (LIFCM) clustering for segmentation of tissues in MR brain images. The proposed method incorporates local information in the clustering process to achieve accurate labelling of pixels. A novel local influence factor is proposed, which estimates the influence of a neighbouring pixel on the centre pixel. Furthermore, they have introduced the kernel-induced distance in LIFCM, which deals with complex brain MR data and produces effective segmentation. To evaluate the performance of the proposed method, they have used one simulated and one real MRI data set. Extensive experimental findings suggest that the authors' method not only produces effective segmentation but also retains crucial image details. The statistical significance test has been further conducted to support their experimental observations.
Starting Page	3705
Ending Page	3717
Page Count	13
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 15, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/15
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2020.0383
Journal	IET Image Processing
Publisher Date	2020-12-04
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 Diseases Brain Magnetic Resonance Image Brain MR Data Brain MR Image Centre Pixel Clustering Process Computer Vision And Image Processing Technique Computer-aided Diagnosis Diseases Fuzzy Clustering Fuzzy Set Theory Image Segmentation LIFCM Local Influence Factor Local Information Medical Image Processing Medical Magnetic Resonance Imaging And Spectroscopy MR Brain Image Optical, Image And Video Signal Processing Patient Diagnostic Method And Instrumentation Pattern Clustering Spectroscopy
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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