Publication
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.
Jiancheng Yang, Zhiye Wang, Jun Lu, Zhigang Li, Lin Qi, Ming Li, Bo Du, Yuxuan Sun, Ziyi Liu
Jiancheng Yang, Yi Wu, Ying Zhu, Boyu Zhang