Staff View
Computational appearance models for quantitative dermatology

Descriptive

TitleInfo
Title
Computational appearance models for quantitative dermatology
Name (type = personal)
NamePart (type = family)
Kaur
NamePart (type = given)
Parneet
DisplayForm
Parneet Kaur
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Dana
NamePart (type = given)
Kristin J.
DisplayForm
Kristin J. Dana
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Soljanin
NamePart (type = given)
Emina
DisplayForm
Emina Soljanin
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Zonouz
NamePart (type = given)
Saman
DisplayForm
Saman Zonouz
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Cula
NamePart (type = given)
Gabriela O.
DisplayForm
Gabriela O. Cula
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = personal)
NamePart (type = family)
Isnardi
NamePart (type = given)
Michael
DisplayForm
Michael Isnardi
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
School of Graduate Studies
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2017
DateOther (qualifier = exact); (type = degree)
2017-10
CopyrightDate (encoding = w3cdtf); (qualifier = exact)
2017
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
Skin appearance modeling using high-resolution imaging has led to advances in recognition, rendering and analysis. In dermatology, workforce shortage and long patient wait time has motivated the need for computational methods to assist the dermatologists. In recent automated image recognition tasks, deep learning with convolutional neural nets (CNN) has achieved remarkable results. However in many clinical settings, training data is often limited and insufficient for CNN training. Furthermore, skin images have subtle differences and are very different from the typical images used for computer vision tasks. This moti- vates the need of developing methods that can be used for limited and unique datasets. In this research, we propose computational models using deep learning approaches for novel problems in quantitative dermatology. First, we develop a photo-realistic facial style transfer method (FaceTex), which trans- fers facial texture from a new style image while preserving most of the original facial structure and identity. FaceTex has implications in commercial applications and dermatol- ogy, such as visualizing the effects of age, sun exposure, or skin treatments (e.g. anti-aging, acne). We suppress the changes around the meso-structures (eyes, eyebrow, nose, lips and lower facial contour) by introducing the Facial Prior Regularization that smoothly slows down the updating. Additionally, we tackle the challenge of preserving facial shape by minimizing a Facial Structure Loss, which we define as an identity loss from a pre-trained face recognition network that implicitly preserves the facial structure. Our results demonstrate superior texture transfer than state-of-the-art methods because of the ability to maintain the identity of the original face image. Second, we develop a computational skin texture model to characterize image-based patterns from ultraviolet and blue fluorescence multimodal images and link them to distri- bution of microbes on the skin surface, i.e. the skin microbiome. The intersection of ap- pearance and microbiome clusters reveals a pattern of microbiome that is predictable with high accuracy based on skin appearance. We present a new approach, appearance-driven multiview co-clustering (AMCO), which incorporates both multiview and co-clustering in order to discover which microbiome parameters are linked to appearance. Finally, to measure the thickness of skin layers, we develop a hybrid deep learning method to classify reflectance confocal microscopy images. We also use CNNs to classify the images and demonstrate that smaller training datasets are insufficient for CNN training and feature extraction is essential in such cases. We compare our method with a suite of texture recognition methods for RCM images and show that hybrid deep learning outperforms the state-of-the-art with a test accuracy of 81.73%. Using a patch-based approach and pre- trained CNNs for feature extraction, we achieve a peak classification accuracy of 89.87%.
Subject (authority = RUETD)
Topic
Electrical and Computer Engineering
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_8419
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xii, 105 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Subject (authority = ETD-LCSH)
Topic
Dermatology--Data processing
Note (type = statement of responsibility)
by Parneet Kaur
RelatedItem (type = host)
TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/T3X06B6K
Genre (authority = ExL-Esploro)
ETD doctoral
Back to the top

Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Kaur
GivenName
Parneet
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2017-09-27 09:45:34
AssociatedEntity
Name
Parneet Kaur
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
RightsEvent
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2017-10-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2018-05-02
Type
Embargo
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after May 2nd, 2018.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
Back to the top

Technical

RULTechMD (ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem (VERSION = 5.1)
windows xp
CreatingApplication
Version
1.5
ApplicationName
pdfTeX-1.40.17
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2017-09-27T00:18:39
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2017-09-27T00:18:39
Back to the top
Version 8.4.8
Rutgers University Libraries - Copyright ©2022