Modelling grey-level intensities for smart phones to view medical images

PhD thesis

Soni, M. 2023. Modelling grey-level intensities for smart phones to view medical images. PhD thesis Middlesex University Computer Science
TypePhD thesis
TitleModelling grey-level intensities for smart phones to view medical images
AuthorsSoni, M.

This research concerns with the modelling of grey level intensities for mobile phones in order to view medical images in greyscale using different mobile phones.

While medical image format of DICOM employs a Greyscale Standard Display Function (GSDF) that describes the relationship between luminance level and display intensity values, medical images that have high contrast depicted in a computer monitor may not present as clearly in a smart phone. This is because a smart phone has a limitation of resolution with a small screen and cannot be calibrated to a specified grey level setting. This research is to investigate the difference between a computer monitor and a mobile phone with regard to depicting medical images.

The International Commission on Illumination (CIE) has recommended a colour appearance model CIECAM16 that can predict a colour appearance under many viewing conditions, e.g. an LCD display, as accurate as an average observer from human colour vision point of view. In this research, this colour appearance model is applied and enhanced in an attempt to predict grey-level intensity for mobile phones so that an image will appear near the same as it appears on an LCD monitor. Towards this end, more than ten psychophysical experiments are conducted by 14 observers with normal colour vision to study human perception on both an LCD monitor and mobile phones. It has found that for iPhone6S, middle range grey samples appear much brighter than on the LCD display that is calibrated to D65. The enhancement hence takes by modifying ๐‘ value of CIECAM16, representing ambient colour compensation. It appears that when ๐‘ = 0.59, the model of CIECAM16 correlates the best with observersโ€™ estimations. Then experiments on visual estimation are carried out to match the original x-ray chest images with COVID19 displayed on the LCD with those displayed on a phone before and after enhancement. The results show that the enhanced images by CIECAM16 with ๐‘ = 0.59 appear to be much closer to the original in terms of COVID19 specific features, implying the importance of this work. Future work includes containing colour images (e.g. oesophagus videos, retinal images, etc.), more mobile phones in additional of iPhones and more test samples (currently with 20 grey samples). It is concluded that iPhones can be applied to view medical images without compromising key features.

Sustainable Development Goals9 Industry, innovation and infrastructure
Middlesex University ThemeHealth & Wellbeing
Department nameComputer Science
Science and Technology
Institution nameMiddlesex University
PublisherMiddlesex University Research Repository
Publication dates
Online21 Mar 2024
Publication process dates
Accepted29 Sep 2023
Deposited21 Mar 2024
Output statusPublished
Accepted author manuscript
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