Spatio-frequency texture analysis using wavelet increment entropy: Methodology and application to MRI in multiple sclerosis
Résumé
Texture analysis is crucial for understanding images by extracting features that define spatial patterns. Recently, bi-dimensional extensions of entropy measures have gained attention due to their simplicity and strong theoretical foundations. However, existing methods primarily operate in the spatial domain and thus overlook frequency-domain and multiscale information. To address this, we introduce bidimensional wavelet increment entropy (wavelet IncrEn). A one-level discrete wavelet transform (DWT) with the Haar wavelet decomposes each image into approximation (low-frequency) and, for some neuroimaging data, detail (high-frequency) subbands; IncrEn is then applied both to capture global structural patterns and fine, detailed texture variations. We evaluated wavelet IncrEn on synthetic and real datasets, demonstrating its effectiveness in distinguishing between different noise types (white Gaussian, salt-and-pepper, and speckle noise). Comparisons between periodic and synthesized images revealed lower wavelet IncrEn values for periodic textures. Tests on real texture datasets highlight the method's ability to differentiate various patterns. In particular, wavelet IncrEn achieved 86.69% accuracy in distinguishing MRI images of healthy versus multiple sclerosis–affected brains. Overall, wavelet IncrEn offers a robust, frequency-aware descriptor that outperforms existing 2D entropy methods.