MaSkinLe

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Skin Lesion Segmentation on 3D Surfaces

The use of computers in the analysis of skin lesions has always been an interesting and developing application of computer vision in dermatology. Identifying lesion borders, or segmentation, is one of the most crucial and active areas in computerized analysis of skin lesions. While most segmentation and analysis are done in 2 dimensions, utilizing the 3D space can provide more information to make this task more accurate. There are a number of new features and geometric information that can be gathered from a 3D representation over a 2D image. One such example is to automatically determine a definitive surface area of the skin lesion. In this thesis, we present a proof of concept towards the segmentation of a marked lesion area and determining an estimate for the surface area. We process a 3D model of a body part generated by the KinectFusion? algorithm and textured with texture mapping. An approach towards filtering and modifying the generated point cloud of the general lesion area is presented. Afterwards, we can utilize the polygon faces between the points of the lesion to calculate an estimated surface area. With the processing power of modern CPUs and GPUs, generation of the model and going through the segmentation pipeline can be done in real time. The information calculated by the pipeline can be used beneficially in the analysis and treatment of skin cancers.

ProjectForm
Title: Skin Lesion Segmentation on 3D Surfaces
Abstract: The use of computers in the analysis of skin lesions has always been an interesting and developing application of computer vision in dermatology. Identifying lesion borders, or segmentation, is one of the most crucial and active areas in computerized analysis of skin lesions. While most segmentation and analysis are done in 2 dimensions, utilizing the 3D space can provide more information to make this task more accurate. There are a number of new features and geometric information that can be gathered from a 3D representation over a 2D image. One such example is to automatically determine a definitive surface area of the skin lesion. In this thesis, we present a proof of concept towards the segmentation of a marked lesion area and determining an estimate for the surface area. We process a 3D model of a body part generated by the KinectFusion? algorithm and textured with texture mapping. An approach towards filtering and modifying the generated point cloud of the general lesion area is presented. Afterwards, we can utilize the polygon faces between the points of the lesion to calculate an estimated surface area. With the processing power of modern CPUs and GPUs, generation of the model and going through the segmentation pipeline can be done in real time. The information calculated by the pipeline can be used beneficially in the analysis and treatment of skin cancers.
Student: Charles Lee
Director: Federico Tombari
Supervisor: Helisa Dhamo
Type: Master Thesis
Area: Segmentation, Medical Imaging, Computer Vision
Status: finished
Start: 01.07.2018
Finish: 01.01.2019
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