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

•Novel SOM based FKM algorithm for tissue segmentation and tumor identification in magnetic resonance brain images (T1-w, T2-w, FLAIR and MPR sequences) is proposed through this work.•Exact demarcation between tumor and edema region is characterized.•Validation of the segmented results by an experie...

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Veröffentlicht in:	Applied soft computing Jg. 38; S. 190 - 212
Hauptverfasser:	Vishnuvarthanan, G., Rajasekaran, M. Pallikonda, Subbaraj, P., Vishnuvarthanan, Anitha
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.01.2016
Schlagworte:	Computational time Dice Overlap Index (DOI) FKM (fuzzy K-means algorithm) Jaccard Index Mean square error (MSE) MR brain tumor identification Peak signal to noise ratio (PSNR) Sensitivity SOM (self-organizing map) Specificity Tissue segmentation Computational time SOM (self-organizing map) Sensitivity Specificity FKM (fuzzy K-means algorithm) Jaccard Index MR brain tumor identification Tissue segmentation Mean square error (MSE) Peak signal to noise ratio (PSNR) Dice Overlap Index (DOI)
ISSN:	1568-4946, 1872-9681
Online-Zugang:	Volltext
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