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.
Keywords: High-resolution computed tomography (HRCT) ; interstitial lung diseases (ILDs) ; isotropic wavelet frames ; lung tissue analysis ; texture analysis ; High-Resolution Ct ; Computed-Tomography Findings ; Quantitative-Analysis ; Medical Images ; Classification ; Diseases ; Segmentation ; Retrieval ; System ; Hrct
Record created on 2012-07-27, modified on 2016-08-09