Improved texture reproduction assessment of camera-phone-based medical devices with a dead leaves target

Abstract

Camera-phone-based medical devices (CPMDs) represent a major emerging platform for point-of-care diagnostic imaging of biological tissue. In order to evaluate degradation in texture reproduction due to visible light image processing performed by CPMDs, a method ($MT{F_{DL}}$MTFDL) involving the generation of a modulation transfer function ($MTF$MTF) based on measurements of a ‘dead leaves (DL)’ target has been proposed. In this study, we have identified discrepancies in the quantification of noise based on gray patches of the DL target as compared to the textual region. To address this issue, we have proposed an approach ($MT{F_{DL - den}}$MTFDL−den) for accurate $MT{F_{DL}}$MTFDL calculation through the use of effectively denoised DL images. Furthermore, we demonstrate that our $MT{F_{DL - den}}$MTFDL−den approach provides superior robustness to simulated noise. These findings will help establish effective standardized test methods for realistic benchtop assessment of CPMD image quality.