Influence of Segmentation over Magnetic Resonance Image Classification
Date Issued
2010-09
Author(s)
Abstract
Magnetic resonance imaging is an image based diagnostic technique
which is widely used in medical environment. Thus, the efficient automated
analysis of this kind of images is of great importance for both, scientific and
clinical environment. In this paper, analysis of evaluation results of the
classification of magnetic resonance images with different classifiers is
conducted. This analysis is provided in both cases, with and without application
of graph-based segmentation technique. The aim of the paper is to investigate
whether or not this kind of segmentation technique induces improvements in
the classification of MRIs. Seven descriptors are used for feature extraction in
our paper, and the classification is analyzed in all seven cases. The ultimate
goal of the paper is to signify in which combination of classification technique
and feature extraction algorithm, the examined segmentation technique is the
most appropriate for magnetic resonance images. For the overall investigation
in this paper, a specific hierarchical organized dataset of magnetic resonance
images is used.
which is widely used in medical environment. Thus, the efficient automated
analysis of this kind of images is of great importance for both, scientific and
clinical environment. In this paper, analysis of evaluation results of the
classification of magnetic resonance images with different classifiers is
conducted. This analysis is provided in both cases, with and without application
of graph-based segmentation technique. The aim of the paper is to investigate
whether or not this kind of segmentation technique induces improvements in
the classification of MRIs. Seven descriptors are used for feature extraction in
our paper, and the classification is analyzed in all seven cases. The ultimate
goal of the paper is to signify in which combination of classification technique
and feature extraction algorithm, the examined segmentation technique is the
most appropriate for magnetic resonance images. For the overall investigation
in this paper, a specific hierarchical organized dataset of magnetic resonance
images is used.
Subjects
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