Near-Affine-Invariant Texture Learning for Lung Tissue Analysis Using Isotropic Wavelet Frames PROJECT TITLE :Near-Affine-Invariant Texture Learning for Lung Tissue Analysis Using Isotropic Wavelet FramesABSTRACT :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. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Anonymous Indexing of Health Conditions for a Similarity Measure EMG and EPP-Integrated Human–Machine Interface Between the Paralyzed and Rehabilitation Exoskeleton
