Pupil Response Profile Generation via Temporal Filtering
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Solution Overview
Problem
Current pupilometers face challenges in accurately measuring pupillary responses due to delays in iris sphincter muscle reaction and the presence of false round shapes in images, leading to incorrect detection or misdiagnosis of medical conditions.
Innovation Solution
A computer-implemented method and system that obtain scan data frames before, during, and after light exposure, use image processing techniques like HAAR cascade eye detection, MSER, and filtering to standardize and refine pupil measurements, generating a pupil response profile with parameters such as constriction and dilation amplitudes and velocities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image analysis is performed on still images or video frames to detect pupil dimensions, then pupil measurement can be obtained, but false round shapes (reflections, artifacts) are incorrectly identified as pupils leading to measurement errors
Solution Approach 1:
The patent segments the image analysis process into multiple distinct stages: candidate identification, verification, and measurement. By dividing the detection process into these phases, the system can apply different criteria at each stage to eliminate false positives while maintaining sensitivity to true pupils.
Solution Approach 2:
The patent performs preliminary actions by first identifying candidate round shapes before confirming them as pupils. This preliminary identification stage allows the system to filter out false positives (reflections, artifacts) before final measurement, improving both accuracy and reliability.
2Adaptability or versatility
If multiple round shapes are detected in the image, then comprehensive shape analysis is performed, but it becomes difficult to distinguish the actual pupil from other round shapes (iris, reflections)
Solution Approach 1:
The patent applies local quality by examining specific local characteristics of each detected round shape, such as position relative to the eye, size constraints, and geometric properties. By analyzing local features rather than treating all round shapes uniformly, the system can distinguish pupils from other circular structures in the eye region.
Solution Approach 2:
The patent changes parameters by applying multiple filtering criteria (size, position, shape characteristics) to differentiate pupils from other round shapes. By adjusting and applying different parameter thresholds at various stages, the system can adaptively identify the pupil among multiple detected shapes.
3Reliability
If pupillary response is measured to diagnose medical conditions, then diagnostic information is obtained, but measurement delays in iris sphincter muscle reaction may lead to incorrect or missed diagnoses
Solution Approach 1:
The patent employs continuous video frame capture and analysis instead of single still images. This continuous monitoring allows the system to track pupil response over time, capturing the full dynamics of pupil constriction and recovery, thereby improving diagnostic accuracy despite physiological response delays.
Solution Approach 2:
The system uses feedback by continuously monitoring pupil response across multiple video frames and adjusting measurements based on the temporal pattern of change. This feedback mechanism allows the system to account for the delayed iris sphincter muscle reaction by analyzing the progression of pupil response rather than relying on a single instantaneous measurement.
Data Source
AI summary
A system and method are provided for obtaining a pupil response profile for a subject. The method include: obtaining scan data as frames of a pupil response over time prior to, during and after exposure to a flash of a light source; locating a candidate pupil to be measured from the scan data; image processing the scan data to obtain a set of pupil candidate measurements to generate a graph of pupil measurements against time; filtering the graph to produce a final set of pupil measurements forming a pupil response profile. The method may also include: measuring profile parameters from the pupil response profile; and using the profile parameters to determine aspects of the pupil response.