GDL

Extraction of wavelet based features for classification of t2-weighted mri brain images

ISSN : 0976 - 710X
Journal from gdlhub / 2017-08-14 11:52:34
Oleh : Ms. Yogita K.Dubey1 and Milind M.Mushrif2, Signal & Image Processing : An International Journal
Dibuat : 2012-07-02, dengan 1 file

Keyword : Brain MRI, Feature extraction, Classification, Cosine-modulated Wavelets
Subjek : Extraction of wavelet based features for classification of t2-weighted mri brain images
Url : http://airccse.org/journal/sipij/papers/3112sipij10.pdf
Sumber pengambilan dokumen : Internet

Extraction of discriminate features is very important task in classification algorithms. This paper presents


technique for extraction cosine modulated feature for classification of the T2-weighted MRI images of


human brain. 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, Mahalanobis distance metric is used


to classify the image as normal or abnormal. Average Classification accuracy with a success rate of 100%


has been obtained.

Deskripsi Alternatif :




Extraction of discriminate features is very important task in classification algorithms. This paper presents


technique for extraction cosine modulated feature for classification of the T2-weighted MRI images of


human brain. 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, Mahalanobis distance metric is used


to classify the image as normal or abnormal. Average Classification accuracy with a success rate of 100%


has been obtained.

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