Skin capillary network recognition and analysis by means of neural algorithms

Background: The intra-dermal capillary network can be easily assessed by a computerized videomicroscope system. Nevertheless, finding capillary loops automatically in an image is a difficult yet important first step in order to achieve microcirculation analysis. Methods: A detection system was tested by combining videocapillaroscopy and principal component analysis (PCA). Our goal was to build a generic detector of capillary associated with a retinally connected neural network filter. The filter examines small windows of an image, and decides with this detector whether each window contains a capillary or not. Results: Comparisons with manual detections showed that the system has a detection rate of 82% on test set A containing 100 good-quality images of the scalp. A detection rate of 65% was obtained on test set B containing 50 images with noisy background and large artifacts. The performance was increased by a color detector with a detection rate of 71% on the last test. These results correspond to a false detection rate lower than or equal to 10%. Conclusion: This neural filter system is capable of real-time processing; it recognizes capillaries anywhere in an image, and operates successfully under wide range of lighting and noisy conditions.