Imaging, image processing and pattern analysis of skin capillary ensembles

Abstract

BACKGROUND/AIMS: The capillary bed is recognized as the site where metabolic and nutrient processes occur for living tissues at all levels. The evaluation of this vital process is a major concern in microcirculation. Unlike traditional approaches that concentrated on the extreme local properties of this process, a more global analysis toward capillary ensembles is employed here, since capillaries work as a cooperative entirety. As a first step toward ensemble analysis, the static and planar geometric parameters are investigated. Parameters such as the capillary adjacency and size information are very important in predicting and analysing certain malfunctions in the microvascular bed. METHODS/RESULTS: In order to achieve an objective and accurate analysis of these vital parameters, a computerized imaging system is proposed. Not only the number of capillaries and the capillary cross-sectional areas are important in describing the microvascular bed but the planar distribution pattern of the capillaries also carries valid information. This information, unique to the ensemble analysis, can be used to reveal, visualise and quantify the clustering of capillaries; and this information, according to the Krogh model, is fundamental in estimating the tissue oxygen supply. Two spatial models, the closest neighbor and triangulation methods, have been applied to the captured images of capillary ensembles. The closest neighbor technique generates a minimal distance map or displays a distribution, which depicts the local clustering of capillaries. The triangulation technique, on the other hand, generates a mutual distance map, which is a global description of the capillary positions. Triangulation methods have been evaluated but all except the Greedy triangulation method have been rejected due to lack of robustness and model weakness. Therefore, the capillaries are triangulated by the Greedy triangulation method, and the capillary distribution uniformity is defined as one minus the coefficient of variance of the edge lengths of the mutual distance map. CONCLUSIONS: A series of advanced image processing methods have been developed that efficiently extract the capillary position, size and distribution information from the images. These results facilitate the automatic counting of capillaries and the capillary size-related pathological analysis.

Flow Perfusion Improves Seeding of Tissue Engineering Scaffolds with Different Architectures

“…The average number of cells for a cross section ( x) along with the standard deviation (s) were determined. Percent uniformity for a cross section was calculated by the following equation: 56,65 …”

“…To obtain a quantitative measurement of cell uniformity, we utilized the method proposed by Zhao et al and Wendt et al 56,65 Six cross sections were obtained for each scaffold. Each cross section was divided into five areas in the direction of the flow starting from the top.…”

The Prostaglandin E1 Analog Misoprostol Reduces Symptoms and Microvascular Arteriovenous Shunting in Erythromelalgia—A Double-Blind, Crossover, Placebo-Compared Study

“…Combining modern microscopic techniques and information technology, a package able to facilitate the extraction of capillary parameters from microscopic skin images has been developed, analyzing capillary density (Zhong et al, 2000). The software can also calculate the mutual distance, which after creation of a triangulation map between visible capillaries gives the average length of the limbs of the triangles.…”

An evaluation of image enhancement techniques for capillary imaging

“…2) Gaussian filter [14]: a smoothing filter, defined by a Gaussian kernel, which shows lower blurring effects compared to simpler averaging filters. 3) α-trimmed filter [15]: a non-linear window-based filter. It can be considered as a hybrid filter derived from mean and median filters.…”

“…In particular, we benchmark ten image enhancement techniques, namely the median filter [14], Gaussian filter [14], α-trimmed filter [15], band-pass ε filter [16], anisotropic diffusion filtering [17], bilateral filter [18], bilateral enhancer [19], wavelet filtering with adaptive threshold [20], non local means [21], and adaptive damped wave equation [22] as a pre-cursor to edge detection aimed at identifying capillaries. We evaluate edge maps obtained from images processed using these techniques and compare them to a manually defined ground truth using Pratt's figure of merit [23].…”