Automatic classification of histology images, the objective of our research, is aimed at supporting the pathologists in their diagnosis. In this paper, we present a comparative study between 3D spectral/spatial analysis (SSA) and 2D spatial analysis (SA) for the classification of colon biopsy samples from their hyperspectral images. Classification results suggest that textural analysis of 2D ba...

A novel face recognition algorithm using single training face image is proposed. The algorithm is based on textural features extracted using the 2D log Gabor wavelet. These features are encoded into a binary pattern to form a face template which is used for matching. Experimental results show that on the colour FERET database the accuracy of the proposed algorithm is higher than the local featu...

Segmentation remains an important problem in image processing. For homogeneous (piecewise smooth) images, a number of important models have been developed and refined over the past several decades. However, these models often fail when applied to the substantially larger class of natural images that simultaneously contain regions of both texture and homogeneity. This work introduces a bi-level ...

Image classification plays an important role in many applicable fields in our life, such as image analysis, remote sensing, and pattern recognition [9]. It can be defined as the process of sorting all the pixels in an image into a finite number of individual classes[5]. There are many types of techniques which can be used to classify and recognize different types of objects in images. For conve...

A surface is often modeled as a triangulated mesh of 3D points and textures associated with faces of the mesh. The 3D points could be either sampled from range data or derived from a set of images using a stereo or Structurefrom-Motion algorithm. When the points do not lie at critical points of maximum curvature or discontinuities of the real surface, faces of the mesh do not lie close to the m...

Texture analysis is a major task in many areas of computer vision and pattern recognition, including biological imaging. Indeed, visual textures can be exploited to distinguish specific tissues or cells in a biological sample, to highlight chemical reactions between molecules, as well as to detect subcellular patterns that can be evidence of certain pathologies. This makes automated texture ana...

In this paper we consider the problem of unsupervised boundary localization in textured images, reporting a texture separation algorithm which extracts textural density gradients by a non-linear multiple scale-space analysis of the image. Texture boundaries are extracted by segmenting the images resulting from a multiscale fuzzy gradient operation applied to detail images. The segmentation stag...

This study presents the results of object-based classifications assessing the potential of bi-temporal Pléiades images for mapping broadleaf and coniferous tree species potentially used by the ring-necked parakeet Psittacula krameri for nesting in the urban area of Marseille, France. The first classification was performed based solely on a summer Pléiades image (acquired on 28 July 2015) and th...

BACKGROUND The nucleoside analog 3'-deoxy-3'-18F-fluorothymidine (FLT) has been investigated for evaluating tumor proliferating activity in brain tumors. We evaluated FLT uptake heterogeneity using textural features from the histogram analysis in patients with newly diagnosed gliomas and examined correlation of the results with proliferative activity and patient prognosis, in comparison with th...

UNLABELLED Intratumoral uptake heterogeneity in (18)F-FDG PET has been associated with patient treatment outcomes in several cancer types. Textural feature analysis is a promising method for its quantification. An open issue associated with textural features for the quantification of intratumoral heterogeneity concerns its added contribution and dependence on the metabolically active tumor volu...