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2016 | OriginalPaper | Buchkapitel

3D Brain Tumor Segmentation Based on Hybrid Clustering Techniques Using Multi-views of MRI

verfasst von : Eman A. Abdel Maksoud, Mohammed Elmogy

Erschienen in: Medical Imaging in Clinical Applications

Verlag: Springer International Publishing

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Abstract

3D medical images segmentation is a very difficult task. It may not be accurate and takes extreme time. In this chapter, we accurately detect the brain tumor from 3D MRI image with less time. The 3D image consists of multiple 2D slices. Segmenting each 2D slice by using the 2D techniques gives more accuracy rather than segmenting the whole 3D image. The integration between K-Means and Particle Swarm Optimization was proposed to segment the 2D MRI slices of the 3D MRI image. We solved the time problem of segmenting all 2D slices of the 3D image. The experiments emphasized the effectiveness of our proposed system in segmenting the 2D and 3D medical images. It achieved 100 % accuracy for the tested 3D dataset and 98.75 % average accuracy for all tested 2D and 3D datasets. The proposed integration reduced time by a mean of 10 min for the tested 2D and 3D datasets.

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Vorheriges Kapitel Magnetic Resonance Brain Imaging Segmentation Based on Cascaded Fractional-Order Darwinian Particle Swarm Optimization and Mean Shift Clustering

Nächstes Kapitel Comparison of CAD Systems for Three Class Breast Tissue Density Classification Using Mammographic Images

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Titel: 3D Brain Tumor Segmentation Based on Hybrid Clustering Techniques Using Multi-views of MRI
verfasst von: Eman A. Abdel Maksoud
Mohammed Elmogy
Verlag: Springer International Publishing
Buch: Medical Imaging in Clinical Applications
Print ISBN: 978-3-319-33791-3

Electronic ISBN: 978-3-319-33793-7

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-33793-7_4

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