advertisement
Abstract #48702 Published in IGR 14-1
Wavelet-based energy features for glaucomatous image classification
Dua S; Acharya UR; Chowriappa P; Sree SVIEEE transactions on information technology in biomedicine: a publication of the IEEE Engineering in Medicine and Biology Society 2012; 16: 80-87
Texture features within images are actively pursued for accurate and efficient glaucoma classification. Energy distribution over wavelet subbands is applied to find these important texture features. In this paper, we investigate the discriminatory potential of wavelet features obtained from the daubechies (db3), symlets (sym3), and biorthogonal (bio3.3, bio3.5, and bio3.7) wavelet filters. We propose a novel technique to extract energy signatures obtained using 2-D discrete wavelet transform, and subject these signatures to different feature ranking and feature selection strategies. We have gauged the effectiveness of the resultant ranked and selected subsets of features using a support vector machine, sequential minimal optimization, random forest, and naïve Bayes classification strategies. We observed an accuracy of around 93% using tenfold cross validations to demonstrate the effectiveness of these methods.
Computer Science Program, Louisiana Tech University, Ruston, LA, USA. sdua@coes.latech.edu
Full articleClassification:
6.9.5 Other (Part of: 6 Clinical examination methods > 6.9 Computerized image analysis)
