Quincunx wavelet

Abstract— This paper describes an approach for texture characterization based on nonseparable quincunx wavelet decomposition transforms and its application for the discrimination of visually similar ultrasound renal stone images. The proposed feature extraction method applies quincunx wavelet transform and calculation of second order (GLCM) and FFT parameter form LL and HH part of decomposed image. This Characterization is experimented on a set of one hundred and twelve (112) different stones, which also validated with FTIR analysis in standard laboratory. It shows that GLCM, FFT transform evaluation in combination with quincunx wavelet decomposition could be a reliable method for a texture characterization. 

Characterization of Renal Stones from Ultrasound Images Using Nonseparable Quincunx Wavelet Transform... 

Here results are presented for three different types namely Calcium Oxalate Monohydrate 80% Calcium Oxalate Dihydrate 20%(17 stones). Calcium Oxalate Monohydrate 90% Calcium Oxalate Dihydrate 10%(16 stones). Calcium Oxalate Monohydrate 60% Calcium Oxalate Dihydrate 30% Carbonate Apatite 10%(18 stones). For each texture class, the GLCM statistical, FFT and quincunx wavelet are estimated out of 112 texture samples with the leave-one-out method [23]. 

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