PROJECT TITLE :

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

ABSTRACT :

We tend to 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 fascinating to enable learning of nondeterministic textures while not a priori localizations, orientations, or sizes. When combined with complementary gray-level histograms, the proposed methodology permits a international 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 follow.


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