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- W1529661811 abstract "This paper introduces an efficient technique for lung abnormalities segmentation in CT images based on the use of dual-tree complex wavelet transform (DT-CWT) and multilevel histogram thresholding. Recently, a scalar wavelet-based method has shown favorable results compared with previous approaches in honeycomb detection in pediatric CT images. Using our recently designed dual-tree complex filter bank and employing high resolution intensity similarities, we show that DT-CWT outperforms the results obtained with discrete wavelet transform (DWT) in general. Our early experiments show that multi-wavelets (MW) can also present a promising performance than DWT. The results indicate that DT-CWT performs slightly better than multi-wavelets, however, it can significantly outperform scalar wavelets. The former is probably due to better edge preserving property of multi-wavelets, while the latter is obtained because of good directionality and shift-invariance of dual-tree complex wavelet transform." @default.
- W1529661811 created "2016-06-24" @default.
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- W1529661811 date "2009-01-01" @default.
- W1529661811 modified "2023-09-26" @default.
- W1529661811 title "Efficient Segmentation of Lung Abnormalities in CT Images" @default.
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- W1529661811 doi "https://doi.org/10.1007/978-3-642-02611-9_74" @default.
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