Extraction of fluorescent dot traces from a scanning laser ophthalmoscope image sequence by spatio-temporal image analysis: Gabor filter and radon transform filtering

Abstract

The scanning laser ophthalmoscope (SLO) allows the tracking of fluorescent dot motion, thereby enabling the flow velocities in perimacular capillaries to be directly measured. These can serve as an important index of local retinal soundness or reflect the whole body circulation status in disorders such as diabetes. Although it is possible to perceive moving fluorescent dots with the human eye, they are so faint and unstable that it is difficult to detect them by conventional digital still-image processing methods. To solve this problem, we generated spatio-temporal images of the fluorescent dots in a capillary and applied Gabor filters tuned to the direction of the traces in order to detect them. Finally, by discriminating and integrating the output using two levels of threshold, we were able to extract their traces. Because the medium-size Gabor filter requires a considerable amount of time for two-dimensional convolution calculation, we prove that there is a certain equivalence between the Gabor filter, the radon transform, and the Hough transform. In the light of this, we propose a form of radon transform filtering that includes a radon transform Gabor filter as a very long Gabor filter. This allows a whole trace to be detected in a single step with a one-dimensional convolution, thereby shortening the processing time. In an experiment, 60% of the traces could be detected without error, which is sufficient to allow the mean flow velocity in a capillary to be measured.

Red Blood Cell Tracking Using Optical Flow Methods

“…Currently, several methods are used for automatic tracking of cells and blood flow analysis, such as the level set method [6, 7], the cross correlation method [8, 9], and filter methods directly used on spatiotemporal image [10, 11]. However, the level set method and cross correlation method are difficult to conduct in the case when the blood cells are unidentified from the microvessel contour; and the filter methods can work only when cells have fixed velocity.…”

Automatic tracking and measurement of the motion of blood cells in microvessels based on analysis of multiple spatiotemporal images

“…In previous works, many methods have been proposed to measure the orientation of traces on spatiotemporal images, such as using the Hough transform [20][21][22], Gabor filters [23][24][25], straight lines fitting [26] and singular value decomposition [27][28][29] to detect the global orientation of traces on a spatiotemporal image, or utilizing the Radon transform [30], the autocorrelation technique [31], to measure the local orientation of traces on a spatiotemporal image. Instead of the trace extraction from a spatiotemporal image, these methods are only able to measure cells' average or local average velocity, which is not enough for precise velocity evaluation and motion analysis of the cells, for the reason that in many cases the cells' motion is not always at constant velocity and their corresponding traces appear curvilinear on the spatiotemporal image.…”