Texture Classification Using Cosine-modulated Wavelets

Mushrif, Milind M.; Dubey, Yogita

doi:10.7763/ijcee.2012.v4.493

Cited by 1 publication

(1 citation statement)

References 13 publications

(11 reference statements)

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“…Cosine-modulated wavelet based texture features are used by M. Kokare et al [6] for content-based image retrieval with good retrieval efficiency and accuracy. M. M. Mushrif et al [7] used cosine modulated wavelet transform based features for texture classification and reported improved classification rates compared to the traditional Gabor wavelet based approach, rotated wavelet filters based approach, DT-CWT approach and the DLBP approach.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Wavelet Based Features for Classification of T2-Weighted MRI Brain Images

Dubey¹

2012

SIPIJ

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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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Section: Introductionmentioning

confidence: 99%