Annual Spread Rate of Tomentosus Root Disease.

The spread rate of tomentosus root disease, caused by Inonotus tomentosus, was investigated by a new technique employing temporal differences in the initiation of the reduced annual radial increment between pairs of diseased trees. Pairs of infected trees (stumps) located on the periphery of disease centers were selected in each of six widely separated spruce (Picea spp.) stands in British Columbia. Distances between 12 pairs of stumps were measured, and disks were collected from each stump. Similarly, disks from four additional pairs were collected from trees in a younger stand. Uninfected control disks were collected for all sites. Tree-ring measurements were determined for all disk samples and the year in which the reduction of the annual increment attributable to I. tomentosus began was determined for infected trees. The difference between initiation years for pairs of infected trees divided into the distance between them produced an average annual spread rate of 20 cm/yr. This rate will be used in developing a model for the disease.

A digital image analysis system for comparing groups of small nematodes.

A digital imaging system was developed for measuring various physical characteristics of individual nematodes and for comparing groups of nematodes. The equipment consists of a microscope, a video camera, a video digitizer, interactive displays, and a computer. Various physical and mathematical methods were incorporated, algorithms devised, and computer software written for image acquisition, editing, and analysis. To test the system, four populations of an isolate of the pinewood nematode, Bursaphelenchus xylophilus, subjected to 100% relative humidity at 22 C for 0, 12, 24, or 48 hours were compared. The results showed that the system can be used to measure physical parameters of individual nematodes and to differentiate groups of nematodes.

Image texture features for cell nuclei based on absorbance surface curvature.

The absorbance values of a cell nucleus, as measured at each pixel location, can be considered as forming a surface in a three-dimensional space. The principal curvatures of differential geometry can then be evaluated for this surface. From these curvatures, two new variables are computed that, when averaged over all pixels in the nucleus, are viewed as image texture measures. These two measures were found to be effective for differentiating insect populations and compared well with other features used in the TICAS system. This paper presents the results of comparing the performance of these two features with the performance of other features for several populations of cells.

Surface curvature as a measure of image texture.

Methods from the theory of surfaces in differential geometry are applied to the gray tone intensity and absorbance surfaces defined by a digital image. In particular, the first fundamental form, the second fundamental form, the two principal curvatures, the Gaussian curvature, and mean curvature from classical differential geometry are presented. New curvatures are introduced which appear to be more appropriate for the purposes of image texture. For each curvature, a texture feature is generated. These features are applied to biological cell nuclei and it is found that they are useful for purposes of discrimination.

A computer-assisted cell identification system.

A computer and optical microscope image processing system for the acquisition, editing and analysis of quantitative, multivariate, cell nuclear data is described. The experimental equipment and methods are discussed, the mathematics used are presented, and the additions, modifications and deletions to the TICAS 11/45 set of computer programs for the analysis of the data are outlined.

A nonparametric approach to the discrimination of two cell populations.

An approach to the statistical testing of differences between two cell populations the elements of which are characterized by multivariate data is described. The approach is based on the Fisher discriminant and the Kruskal-Wallis test. The method, which is sufficient but not necessary, makes no assumptions about the normality of the data or about the equality of the covariance matrices for each population.