Let there be light... Characterizing the Effects of Adverse Lighting on Semantic Segmentation of Wound Images and Mitigation using a Deep Retinex Model

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 as...

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Main Author: Iyer, Akshay B.
Other Authors: Emmanuel O. Agu, Advisor
Format: Others
Published: Digital WPI 2020
Subjects:
Online Access:https://digitalcommons.wpi.edu/etd-theses/1368
https://digitalcommons.wpi.edu/cgi/viewcontent.cgi?article=2389&context=etd-theses
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spelling ndltd-wpi.edu-oai-digitalcommons.wpi.edu-etd-theses-23892020-07-29T05:26:59Z Let there be light... Characterizing the Effects of Adverse Lighting on Semantic Segmentation of Wound Images and Mitigation using a Deep Retinex Model Iyer, Akshay B. 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). 2020-05-14T07:00:00Z text application/pdf https://digitalcommons.wpi.edu/etd-theses/1368 https://digitalcommons.wpi.edu/cgi/viewcontent.cgi?article=2389&context=etd-theses Masters Theses (All Theses, All Years) Digital WPI Emmanuel O. Agu, Advisor Michael Gennert, Committee Member Riad Hammoud, Committee Member Lighting Semantic Segmentation Deep Learning Wounds Retinex Theory Dataset
collection NDLTD
format Others
sources NDLTD
topic Lighting
Semantic Segmentation
Deep Learning
Wounds
Retinex Theory
Dataset
spellingShingle Lighting
Semantic Segmentation
Deep Learning
Wounds
Retinex Theory
Dataset
Iyer, Akshay B.
Let there be light... Characterizing the Effects of Adverse Lighting on Semantic Segmentation of Wound Images and Mitigation using a Deep Retinex Model
description 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).
author2 Emmanuel O. Agu, Advisor
author_facet Emmanuel O. Agu, Advisor
Iyer, Akshay B.
author Iyer, Akshay B.
author_sort Iyer, Akshay B.
title Let there be light... Characterizing the Effects of Adverse Lighting on Semantic Segmentation of Wound Images and Mitigation using a Deep Retinex Model
title_short Let there be light... Characterizing the Effects of Adverse Lighting on Semantic Segmentation of Wound Images and Mitigation using a Deep Retinex Model
title_full Let there be light... Characterizing the Effects of Adverse Lighting on Semantic Segmentation of Wound Images and Mitigation using a Deep Retinex Model
title_fullStr Let there be light... Characterizing the Effects of Adverse Lighting on Semantic Segmentation of Wound Images and Mitigation using a Deep Retinex Model
title_full_unstemmed Let there be light... Characterizing the Effects of Adverse Lighting on Semantic Segmentation of Wound Images and Mitigation using a Deep Retinex Model
title_sort let there be light... characterizing the effects of adverse lighting on semantic segmentation of wound images and mitigation using a deep retinex model
publisher Digital WPI
publishDate 2020
url https://digitalcommons.wpi.edu/etd-theses/1368
https://digitalcommons.wpi.edu/cgi/viewcontent.cgi?article=2389&context=etd-theses
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