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Proceedings Paper

Classification of brain tumors using MRI and MRS data
Author(s): Qiang Wang; Eirini Karamani Liacouras; Erickson Miranda; Uday S. Kanamalla; Vasileios Megalooikonomou
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Paper Abstract

We study the problem of classifying brain tumors as benign or malignant using information from magnetic resonance (MR) imaging and magnetic resonance spectroscopy (MRS) to assist in clinical diagnosis. The proposed approach consists of several steps including segmentation, feature extraction, feature selection, and classification model construction. Using an automated segmentation technique based on fuzzy connectedness we accurately outline the tumor mass boundaries in the MR images so that further analysis concentrates on these regions of interest (ROIs). We then apply a concentric circle technique on the ROIs to extract features that are utilized by the classification algorithms. To remove redundant features, we perform feature selection where only those features with discriminatory information (among classes) are used in the model building process. The involvement of MRS features further improves the classification accuracy of the model. Experimental results demonstrate the effectiveness of the proposed approach in classifying brain tumors in MR images.

Paper Details

Date Published: 29 March 2007
PDF: 8 pages
Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 65140S (29 March 2007); doi: 10.1117/12.713544
Show Author Affiliations
Qiang Wang, Temple Univ. (United States)
Eirini Karamani Liacouras, Temple Univ. (United States)
Erickson Miranda, Temple Univ. (United States)
Uday S. Kanamalla, Temple Univ. (United States)
Vasileios Megalooikonomou, Temple Univ. (United States)


Published in SPIE Proceedings Vol. 6514:
Medical Imaging 2007: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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