This Is AuburnElectronic Theses and Dissertations

Automating Interpretation of Images and Visual Inspections in Modern Manufacturing and Medical Settings




Rasoolian, Behnam

Type of Degree

PhD Dissertation


Industrial and Systems Engineering


Automating visual inspection is rapidly finding its way into non-invasive industrial and medical processes. In this work, we address detection automation opportunities in the task of interpreting both medical and industrial images, we combine domain knowledge with state-of-the-art, domain-independent methods such as Convolutional Neural Networks (CNN) to address them. Specifically, we address two problems in additive manufacturing and one in medical imaging as described below. We provide a method to improve the resolution of and automate the extraction of local and global porosity features from XCT images of additively manufactured Ti-6Al-4V. We also address the automation issues in characterizing local roughness features, particularly the curvature of microscopic surface pits observed in microscopy images of AM Ti-6Al-4V. Finally, we developed a deep learning model to automate the task of detection and segmentation of different muscles in images obtained from MRI of the lower back.