MRI Texture-Based Classification of Dystrophic Muscles. A Search for the Most Discriminative Tissue Descriptors

• Step 2: Texture curves from known samples are used to train a machine learning classifier (a Support Vector Machine) to discriminate between pseudo-progression and

Texture synthesizers from the literature process with a rect- angular patch. Its relevance is essential to ensure an efficient synthesis in our context. In the literature, patch

In order to test the efficacy of the proposed features for rotation invariant texture analysis, we carry out the experiment on 20 structured texture images selected from the Brodatz

Scheunders, “Wavelet-based colour texture retrieval using the Kullback-Leibler divergence between bivari- ate generalized Gaussian models,” in Image Processing, IEEE

We have evaluated the proposed descriptor over a wide range of textures using, Outex, CUReT and KTH TIPS2 databases and studied different problems related to texture recognition such

So, features extracted from each scale are concatenated in a single feature histogram with n*8 feature dimension where n is number of scales... These databases

A multivariate Gamma model using Gaussian copula is proposed and its performances are evaluated for texture classification. The experiments results show that the

Based on the generalized Gamma distribution (GΓD) for the modeling of wavelet coefficients, we have derived a new algorithm to compute the centroid from a collection of GΓD