Faculty Advisor or Committee Member

Emmanuel O. Agu, Advisor

Faculty Advisor or Committee Member

Michael Gennert, Committee Member

Faculty Advisor or Committee Member

Riad Hammoud, Committee Member




Wound assessment using a smartphone image has recently emerged as a novel way to provide actionable feedback to patients and caregivers. Wound segmentation is an important step in image-based wound assessment, after which the wound area can be analyzed. Semantic segmentation algorithms for wounds assume favorable lighting conditions. However, smartphone wound imaging in natural environments can encounter adverse lighting that can cause several errors during semantic segmentation of wound images, which in turn affects the wound analysis. In this work, we study and characterize the effects of adverse lighting on the accuracy of semantic segmentation of wound images. Our findings inform a deep learning-based approach to mitigate the adverse effects. We make three main contributions in this work. First, we create the first large-scale Illumination Varying Dataset (IVDS) of 55440 images of a wound moulage captured under systematically varying illumination conditions and with different camera types and settings. Second, we characterize the effects of changing light intensity on U-Net’s wound semantic segmentation accuracy and show the luminance of images to be highly correlated with the wound segmentation performance. Especially, we show low-light conditions to deteriorate segmentation performance highly. Third, we improve the wound Dice scores of U-Net for low-light images to up to four times the baseline values using a deep learning mitigation method based on the Retinex theory. Our method works well in typical illumination levels observed in homes/clinics as well for a wide gamut of lighting like very dark conditions (20 Lux), medium-intensity lighting (750 - 1500 Lux), and even very bright lighting (6000 Lux).


Worcester Polytechnic Institute

Degree Name



Robotics Engineering

Project Type


Date Accepted





Lighting, Semantic Segmentation, Deep Learning, Wounds, Retinex Theory, Dataset

Available for download on Sunday, May 14, 2023