Improvement of MRI brain classification using principal component analysis

The field of medical imaging gains its importance with increase in the need of automated and efficient diagnosis in a short period of time. One of the primary diagnostic and treatment evaluation tools for brain interpretation has been magnetic resonance imaging (MRI). It has been a widely-used metho...

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Veröffentlicht in:2011 IEEE International Conference on Control System, Computing and Engineering S. 557 - 561
Hauptverfasser: Abdullah, N., Lee Wee Chuen, Ngah, U. K., Ahmad, K. A.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.11.2011
Schlagworte:
Brain
Feature extraction
feature vector
Magnetic resonance imaging
Magnetic Resonance Imaging (MRI)
Medical Imaging
Principal component analysis
Principal Component Analysis (PCA)
Support Vector Machine (SVM)
Support vector machine classification
Wavelet transforms
ISBN:9781457716409, 1457716402
Online-Zugang:Volltext
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Zusammenfassung:The field of medical imaging gains its importance with increase in the need of automated and efficient diagnosis in a short period of time. One of the primary diagnostic and treatment evaluation tools for brain interpretation has been magnetic resonance imaging (MRI). It has been a widely-used method of high quality medical imaging, especially in brain imaging where MRI's soft tissue contrast and non invasiveness are clear advantages. Classification is an important part in retrieval system. The classifications of brain MRI data as normal and abnormal are important to prune the normal patient and to consider only those who have the possibility of having abnormalities or tumor. This step was done by using support vector machine (SVM). The aim of this paper is to compare percentage of accuracy in classification data with and without the implementation of principal component analysis (PCA). As a result, we found that by using PCA method, the number of feature vector has been reduced from 17689 to 200 and increase the percentage of accuracy.
ISBN:9781457716409
1457716402
DOI:10.1109/ICCSCE.2011.6190588