Ambient Light Affects Image Interpretation

Article

When interpreting images on handheld devices, ambient light should be a consideration for accurate readings.

The quality of images interpreted by radiologists using mobile devices may be affected by ambient light, according to a study published in the Journal of Digital Imaging.

The availability of apps to allow for image interpretation has given radiologists the ability to access images wherever they are. However, even if a particular device has been accepted as a reliable tool, its effectiveness may change according to ambient lighting levels. Researchers from the University of Maryland undertook and observational study to determine if these effects were significant.

The researchers asked three subjects to detect and distinguish four characters embedded in a white-noise background using two current-generation smartphones. The setup included five illumination conditions simulating dark room (super- and medium-dark), office (average), and outdoor (medium- and super-bright) environments.

The researchers found that the darker the lighting, the better the quality of the image interpretation.

“We found and quantified that due to the high reflectivity of handheld devices, performance deteriorates as the user moves from dark areas into environments of greater ambient illumination,” the authors wrote. “The quantitative analysis suggests that differences in display reflection coefficients do not affect the low illumination performance of the device but rather the performance at higher levels of illumination.”

Recent Videos
What New Research Reveals About the Impact of AI and DBT Screening: An Interview with Manisha Bahl, MD
Can AI Assessment of Longitudinal MRI Scans Improve Prediction for Pediatric Glioma Recurrence?
A Closer Look at MRI-Guided Adaptive Radiotherapy for Monitoring and Treating Glioblastomas
Incorporating CT Colonography into Radiology Practice
What New Research Reveals About Computed Tomography and Radiation-Induced Cancer Risk
What New Interventional Radiology Research Reveals About Treatment for Breast Cancer Liver Metastases
New Mammography Studies Assess Image-Based AI Risk Models and Breast Arterial Calcification Detection
Can Deep Learning Provide a CT-Less Alternative for Attenuation Compensation with SPECT MPI?
Employing AI in Detecting Subdural Hematomas on Head CTs: An Interview with Jeremy Heit, MD, PhD
Pertinent Insights into the Imaging of Patients with Marfan Syndrome
Related Content
© 2025 MJH Life Sciences

All rights reserved.