Image Processing-based Automatic Pupillometry on Infrared Videos

Abstract

Pupillometry is a non-invasive technique that can be used to objectively characterize pathophysiological changes involving the pupillary reflex. It is essentially the measurement of the pupil diameter over time. Here, specially designed computer algorithms provide fast, reliable and reproducible solutions for the analysis. These methods use a priori information about the shape and color of the pupil. Our study focuses on measuring the diameter and dynamics of the pupils of rats with schizophrenia using videos recorded with a modified digital camera under infrared (IR) illumination. We developed a novel, robust method that measures the size of a pupil even under poor circumstances (noise, blur, reflections and occlusions). We compare our results with measurements obtained using manual annotation.

Automating, Analyzing and Improving Pupillometry with Machine Learning Algorithms

“…Furthermore, albino rats lack pigments in their body including their eyes, which reduces the contrast between the iris and the pupil. A specifically designed pupil detection and measurement algorithm was used to handle these quality drawbacks [10]. The input of the algorithm was the video recording; and the output was a curve of the determined relative pupil diameters in each frame -the pupillogram.…”

“…The images recorded with the new measurement setup have a completely different nature, as it can be seen in Figure 3.b. Therefore, our previously developed method [10] cannot be used (different intensity levels, different resolution, etc.). To support the development of a new pupil segmentation algorithm and to validate the new setup, 56 experimental videos were recorded each containing more than 5000 frames.…”

Behavioral and autonomic characterization of a chronic schizophrenia rat model (WISKET)

“…The custom image and signal processing methods were developed in MATLAB (Version 2015b, Mathworks) to extract automatically the required parameters before and after the light stimuli on each recorded frame (Table 2) [218]. The pupil diameter was expressed as a ratio of the diameter of iris in % ( It has to be mentioned that in the video-recorded images, the exact definition of the pupil in the iris may be challenging due to scattering movements, significant blur, low contrast difference between the pupil and iris, noise and reflections [218]. Besides the robust handling of these, the measurement process needs to be fast, accurate and reproducible.…”

“…The solution was a novel, energy attenuation model-based ray propagation method, which used mathematical, geometrical and physical relations to explore and analyze the structure of the iris and pupil regions and to estimate the latter's diameter. The evaluation of the proposed method on 20 manually processed video recordings showed that the overall diameter measurement error was less than ± 2 % [218]. For the correlation analysis linear regression and calculation of Pearson correlation coefficients were assessed.…”