With the development of Computer-aided Diagnosis (CAD) and image scanning techniques,
Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis …

This chapter reviews and discusses various aspects of texture analysis. The concentration is
on the various methods of extracting textural features from images. The geometric, random …

Texture is a fundamental characteristic of many types of images, and texture representation
is one of the essential and challenging problems in computer vision and pattern recognition …

Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics-a
very active area of research in which few up-to-date reference works are available. This is …

Feature Extraction for Image Processing and Computer Vision is an essential guide to the
implementation of image processing and computer vision techniques, with tutorial …

We study the recognition of surfaces made from different materials such as concrete, rug,
marble, or leather on the basis of their textural appearance. Such natural textures arise from …

Heterogeneity is a key feature of malignancy associated with adverse tumour biology.
Quantifying heterogeneity could provide a useful non-invasive imaging biomarker …

Methods for digital-image texture analysis are reviewed based on available literature and
research work either carried out or supervised by the authors. The review has been …

This paper proposes a novel approach to extract image features for texture classification.
The proposed features are robust to image rotation, less sensitive to histogram equalization …

Extracting effective features is always a challenging problem for texture classification
because of the uncertainty of scales and the clutter of textural patterns. For texture …