Three-Dimensional Body Volume Measurement From Two-Dimensional Images: Towards A Smartphone Application

dc.contributor.advisorMutsvangwa, Tinashe
dc.contributor.advisorDouglas, Tania
dc.contributor.advisorLambert, Vicki
dc.contributor.authorMajola, Khwezi
dc.date.accessioned2021-02-05T08:44:38Z
dc.date.available2021-02-05T08:44:38Z
dc.date.issued2020
dc.date.updated2021-02-04T22:53:52Z
dc.description.abstractObesity poses a public health threat worldwide and is associated with a higher mortality, increased likelihood of diabetes, and an increased risk of cancer. When treating obesity, regular monitoring of metrics such as body mass index (BMI) and waist circumference has been found to result in improved health outcomes for patients. Three-dimensional (3D) scanners provide a useful tool to provide body measurements based on 3D images in obesity management. However, such scanners are often inaccessible due to cost. A smartphone image-based method able to produce 3D images may provide a more accessible measuring tool. As a step towards developing such a smartphone application, this project developed a method for 3D reconstruction of body images from two-dimensional (2D) images, using a full body 3D Gaussian process morphable model (GPMM). Separate GPMMs were trained to learn the shape of female and male human bodies. Gaussian process regression of the three-dimensional (3D) GPMM models onto two-dimensional (2D) images is performed. Corresponding landmarks on the 3D shapes and in the 2D images are employed in reconstruction. Measurements of body volume, waist circumference and height are then performed to extract information that is useful in obesity management. Different model configurations (shape model with arms; modified shape model with arms; shape model without arms; marginalised shape model without arms; shape model with different landmarks) were used to ascertain the most promising approach for the reconstruction. Each reconstructed body was tested for accuracy using the surface-tosurface distance per vertex, modified Hausdorff distance, and assessment of the measurements. Tests were performed using data from the same dataset used to build the model and generalised data from a different dataset. In all test cases, the best performing approach used shape models without arms when considering surface distances. However, the surface-to-surface distances errors were larger than those seen in literature. For body measurements, the best performing models varied with different models performing best for different measurements. For the measurements, the errors were larger than the allowable errors and larger than those found in literature. Landmark positions were evaluated separately and found to be imprecise. There are a few sources that contribute towards the reconstruction errors. Possible sources of error include an inability to interpret pose and landmark position errors. The major recommendations for future work are to use a model that incorporates both shape and pose and to use automatic landmarking methods. Regarding a pathway to a smartphone app, camera parameter information should be considered to improve processing of the images and smartphone orientation information should be considered to correct for distortions due to a tilted phone.
dc.identifier.apacitationMajola, K. (2020). <i>Three-Dimensional Body Volume Measurement From Two-Dimensional Images: Towards A Smartphone Application</i>. (). ,Faculty of Health Sciences ,Division of Biomedical Engineering. Retrieved from http://hdl.handle.net/11427/32797en_ZA
dc.identifier.chicagocitationMajola, Khwezi. <i>"Three-Dimensional Body Volume Measurement From Two-Dimensional Images: Towards A Smartphone Application."</i> ., ,Faculty of Health Sciences ,Division of Biomedical Engineering, 2020. http://hdl.handle.net/11427/32797en_ZA
dc.identifier.citationMajola, K. 2020. Three-Dimensional Body Volume Measurement From Two-Dimensional Images: Towards A Smartphone Application. . ,Faculty of Health Sciences ,Division of Biomedical Engineering. http://hdl.handle.net/11427/32797en_ZA
dc.identifier.ris TY - Master Thesis AU - Majola, Khwezi AB - Obesity poses a public health threat worldwide and is associated with a higher mortality, increased likelihood of diabetes, and an increased risk of cancer. When treating obesity, regular monitoring of metrics such as body mass index (BMI) and waist circumference has been found to result in improved health outcomes for patients. Three-dimensional (3D) scanners provide a useful tool to provide body measurements based on 3D images in obesity management. However, such scanners are often inaccessible due to cost. A smartphone image-based method able to produce 3D images may provide a more accessible measuring tool. As a step towards developing such a smartphone application, this project developed a method for 3D reconstruction of body images from two-dimensional (2D) images, using a full body 3D Gaussian process morphable model (GPMM). Separate GPMMs were trained to learn the shape of female and male human bodies. Gaussian process regression of the three-dimensional (3D) GPMM models onto two-dimensional (2D) images is performed. Corresponding landmarks on the 3D shapes and in the 2D images are employed in reconstruction. Measurements of body volume, waist circumference and height are then performed to extract information that is useful in obesity management. Different model configurations (shape model with arms; modified shape model with arms; shape model without arms; marginalised shape model without arms; shape model with different landmarks) were used to ascertain the most promising approach for the reconstruction. Each reconstructed body was tested for accuracy using the surface-tosurface distance per vertex, modified Hausdorff distance, and assessment of the measurements. Tests were performed using data from the same dataset used to build the model and generalised data from a different dataset. In all test cases, the best performing approach used shape models without arms when considering surface distances. However, the surface-to-surface distances errors were larger than those seen in literature. For body measurements, the best performing models varied with different models performing best for different measurements. For the measurements, the errors were larger than the allowable errors and larger than those found in literature. Landmark positions were evaluated separately and found to be imprecise. There are a few sources that contribute towards the reconstruction errors. Possible sources of error include an inability to interpret pose and landmark position errors. The major recommendations for future work are to use a model that incorporates both shape and pose and to use automatic landmarking methods. Regarding a pathway to a smartphone app, camera parameter information should be considered to improve processing of the images and smartphone orientation information should be considered to correct for distortions due to a tilted phone. DA - 2020_ DB - OpenUCT DP - University of Cape Town KW - Biomedical Engineering LK - https://open.uct.ac.za PY - 2020 T1 - Three-Dimensional Body Volume Measurement From Two-Dimensional Images: Towards A Smartphone Application TI - Three-Dimensional Body Volume Measurement From Two-Dimensional Images: Towards A Smartphone Application UR - http://hdl.handle.net/11427/32797 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/32797
dc.identifier.vancouvercitationMajola K. Three-Dimensional Body Volume Measurement From Two-Dimensional Images: Towards A Smartphone Application. []. ,Faculty of Health Sciences ,Division of Biomedical Engineering, 2020 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/32797en_ZA
dc.language.rfc3066eng
dc.publisher.departmentDivision of Biomedical Engineering
dc.publisher.facultyFaculty of Health Sciences
dc.subjectBiomedical Engineering
dc.titleThree-Dimensional Body Volume Measurement From Two-Dimensional Images: Towards A Smartphone Application
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationlevelMSc
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