Near-Affine-Invariant Texture Learning for Lung Tissue Analysis Using Isotropic Wavelet Frames

We propose near-affine-invariant texture descriptors derived from isotropic wavelet frames for the characterization of lung tissue patterns in high-resolution computed tomography (HRCT) imaging. Affine invariance is desirable to enable learning of nondeterministic textures without a priori localizations, orientations, or sizes. When combined with complementary gray-level histograms, the proposed method allows a global classification accuracy of 76.9% with balanced precision among five classes of lung tissue using a leave-one-patient-out cross validation, in accordance with clinical practice.


Published in:
IEEE Transactions On Information Technology In Biomedicine, 16, 665-675
Year:
2012
Publisher:
Institute of Electrical and Electronics Engineers
ISSN:
1089-7771
Keywords:
Laboratories:




 Record created 2012-07-27, last modified 2018-03-17


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