Image Analysis and Machine Learning for Medical Images

• Author / Creator

  Liu, Lina

• Medical images play an essential role in detecting and diagnosing numerous diseases. With different medical imaging modalities, a large volume of images is generated every day across healthcare organizations worldwide, providing visualization of lesion appearance (e.g., dermoscopic images) anatomical information (e.g., MRI images), or cellular structures (e.g., laser scattering images). After obtaining the medical images, manual analysis and diagnosis is done by clinicians based on their prior knowledge and experiences. However, the diagnosis process can take a significant amount of time and the decision is subjective and biased toward different clinicians.

  The main goal of this dissertation is to develop machine learning methods for automatic medical image analysis and diagnosis. In this dissertation, two types of medical images, including dermoscopic images and laser scattering images, are used. The dermoscopic images are used to detect the malignant lesions from the benign lesions. Experimental and simulated laser scattering images are used for label-free cell identification, and cell property characterization, respectively.

  This thesis presents two methods based on machine learning for automatic skin lesion analysis. Skin lesion segmentation with auxiliary task is proposed for the accurate segmentation of the pigment regions, which does not require extra labeling information compared with the multi-task learning methods. An automatic skin lesion classification method based on mid-level feature learning is proposed for melanoma detection. State-of-the-art results have been obtained and performances are discussed by extensive verification. This thesis further presents a machine learning technique for the analysis of laser scattering images. Typically, scattering patterns of the staurosporine-treated and non-treated SH-SY5Y neuroblastoma cells are obtained and classified, aiming at providing a better understanding of Parkinson's disease. In addition, multi-wavelength multi-direction laser scattering patterns of single cells have also been simulated to discuss the roles of two factors, cell surface roughness, and mitochondria number, in contributing to the scattering patterns. A systematic and thorough study has been done by extensive experiments. Theoretical analysis about the influence of the multi-wavelength multi-direction scattering patterns has been included. Satisfactory performance has been achieved for both the experimental and simulated data.

• Subjects / Keywords

  • medical image analysis

• Graduation date

  Fall 2022

• Type of Item

  Thesis

• Degree

  Doctor of Philosophy

• DOI

  https://doi.org/10.7939/r3-npyp-7q53

• License

  This thesis is made available by the University of Alberta Library with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.