Research Article
Classification of MRI Brain Images Using Cosine-Modulated Wavelets
@INPROCEEDINGS{10.1007/978-3-642-27308-7_29, author={Yogita Dubey and Milind Mushrif}, title={Classification of MRI Brain Images Using Cosine-Modulated Wavelets}, proceedings={Advances in Computer Science and Information Technology. Computer Science and Engineering. Second International Conference, CCSIT 2012, Bangalore, India, January 2-4, 2012. Proceedings, Part II}, proceedings_a={CCSIT PATR II}, year={2012}, month={11}, keywords={Brain MRI Texture classification cosine-modulated wavelets}, doi={10.1007/978-3-642-27308-7_29} }- Yogita Dubey
Milind Mushrif
Year: 2012
Classification of MRI Brain Images Using Cosine-Modulated Wavelets
CCSIT PATR II
Springer
DOI: 10.1007/978-3-642-27308-7_29
Abstract
This paper presents technique for the classification of the MRI images of human brain using cosine modulated wavelet transform. Better discrimination and low design implementation complexity of the cosine-modulated wavelets has been effectively utilized to give better features and more accurate classification results. The proposed technique consists of two stages, namely, feature extraction, and classification. In the first stage, the energy features from MRI images are obtained from sub-band images obtained after decomposition using cosine modulated wavelet transform. In the classification stage, Bays classifier is used to classify the image as normal or abnormal. Average Classification accuracy with a success rate of 100% has been obtained.