The purpose of this work was to study specific texture properties of the brain's white matter (WM) based on conventional high-resolution T1-weighted magnetic resonance imaging (MRI) datasets. Quantitative parameters anisotropy and laminarity were derived from 3-D texture analysis. Differences in WM texture associated with gender were evaluated on an age-matched sample of 210 young healthy subjects (mean age 24.8, SD 3.97 years, 103 males and 107 females). Changes of WM texture with age were studied using 112 MRI-T1 datasets of healthy subjects aged 16 to 70 years (57 males and 55 females). Both texture measures indicated a "more regular" WM structure in females (p < 10(-6)). An age-related deterioration of WM structure manifests itself as a remarkable decline of both parameters (p < 10(-6)) that is more prominent in females (p < 10(-6)) than in males (p = 0.02). Texture analysis of anatomical MRI-T1 brain datasets provides quantitative information about macroscopic WM characteristics and helps discriminating between normal and pathological aging.

The human vision system can discriminate regions which differ up to the second-order statistics only. We present an algorithm designed to reveal "hidden" boundaries in gray level images, by computing gradients in higher order statistics of the data. We demonstrate it by applying it to the identification of possible "hidden" boundaries of glioblastomas as manifest themselves in three-dimensional (3-D) MRI scans, using a model driven approach. We also demonstrate the method using a nonmodel driven approach where we have no prior information about the location of possible boundaries. In this case, we use 3-D MRI data concerning schizophrenic patients and normal controls.

A method is proposed for three-dimensional (3-D) texture analysis of magnetic resonance imaging brain datasets. It is based on extended, multisort co-occurrence matrices that employ intensity, gradient and anisotropy image features in a uniform way. Basic properties of matrices as well as their sensitivity and dependence on spatial image scaling are evaluated. The ability of the suggested 3-D texture descriptors is demonstrated on nontrivial classification tasks for pathologic findings in brain datasets.

Two approaches to the characterization of three-dimensional (3-D) textures are presented: one based on gradient vectors and one on generalized co-occurrence matrices. They are investigated with the help of simulated data for their behavior in the presence of noise and for various values of the parameters they depend on. They are also applied to several medical volume images characterized by the presence of microtextures and their potential as diagnostic tools and tools for quantifying and monitoring the progress of various pathologies is discussed. No firm medical conclusions can be drawn as not enough clinical data are available. The gradient based method appears to be more appropriate for the characterization of microtextures. It also shows more consistent behavior as a descriptor of pathologies than the generalized co-occurrence matrix approach.

The objective of this work is to investigate the white blood cell (WBC) image recognition problem at all stages. A robust and effective method for automatic WBC differentiation, based on both statistical pattern recognition and neural net approaches, is presented. We demonstrate well-evaluated results ranging from image scene segmentation techniques to recognition details. Recognition accuracy on the test set of 662 images of five WBC types obtained by different imaging systems from 22 bloodstains is not less than 98%.