Improvement of MRI brain classification using principal component analysis - Abdullah, N.; Lee Wee Chuen; Ngah, U. K.; Ahmad, K. A. (2011 IEEE International Conference on Control System, Computing and Engineering, 2011)
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.