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Computerized mouse pupil size measurement for pupillary light reflex analysis.

机译:电脑鼠标瞳孔大小测量,用于瞳孔光反射分析。

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摘要

Accurate measurement of pupil size is essential for pupillary light reflex (PLR) analysis in clinical diagnosis and vision research. Low pupil-iris contrast, corneal reflection, artifacts and noises in infrared eye imaging pose challenges for automated pupil detection and measurement. This paper describes a computerized method for pupil detection or identification. After segmentation by a region-growing algorithm, pupils are detected by an iterative randomized Hough transform (IRHT) with an elliptical model. The IRHT iteratively suppresses the effects of extraneous structures and noise, yielding reliable measurements. Experimental results with 72 images showed a mean absolute difference of 3.84% between computerized and manual measurements. The inter-run variation for the computerized method (1.24%) was much smaller than the inter-observer variation for the manual method (7.45%), suggesting a higher level of consistency of the former. The computerized method could facilitate PLR analysis and other non-invasive functional tests that require pupil size measurements.
机译:在临床诊断和视觉研究中,准确测量瞳孔大小对于瞳孔光反射(PLR)分析至关重要。红外眼成像中的低瞳孔虹膜对比度,角膜反射,伪影和噪声对自动瞳孔检测和测量提出了挑战。本文介绍了一种用于瞳孔检测或识别的计算机化方法。通过区域增长算法进行分割后,通过带有椭圆模型的迭代随机霍夫变换(IRHT)检测瞳孔。 IRHT反复抑制多余结构和噪声的影响,从而提供可靠的测量结果。 72张图像的实验结果表明,计算机和手动测量之间的平均绝对差为3.84%。计算机化方法的运行间差异(1.24%)远小于手工方法的观察者间差异(7.45%),表明前者的一致性更高。这种计算机化的方法可以促进PLR分析和其他需要测量瞳孔大小的非侵入性功能测试。

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