3D brain tumor segmentation scheme using K-mean clustering and connected component labeling algorithms

In the recent years human brain segmentation in three-dimensional magnetic resonance imaging (MRI) has gained a lot of importance in the field of biomedical image processing since it is the main stage for the automatic brain disease diagnosis. In this paper, we propose an image segmentation scheme t...

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Veröffentlicht in:2010 10th International Conference on Intelligent Systems Design and Applications S. 320 - 324
Hauptverfasser: Moftah, Hossam M, Hassanien, Aboul Ella, Shoman, Mohamoud
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.11.2010
Schlagworte:
Classification algorithms
Clustering algorithms
Image segmentation
Labeling
Magnetic resonance imaging
Three dimensional displays
Tumors
ISBN:1424481341, 9781424481347
ISSN:2164-7143
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Zusammenfassung:In the recent years human brain segmentation in three-dimensional magnetic resonance imaging (MRI) has gained a lot of importance in the field of biomedical image processing since it is the main stage for the automatic brain disease diagnosis. In this paper, we propose an image segmentation scheme to segment 3D brain tumor from MRI images through the clustering process. The clustering is achieved using K-mean algorithm in conjunction with the connected component labeling algorithm to link the similar clustered objects in all 2D slices and then obtain 3D segmented tissue using the patch object rendering process.
ISBN:1424481341
9781424481347
ISSN:2164-7143
DOI:10.1109/ISDA.2010.5687244