Next Generation Reporting and Diagnostic Tools for Healthcare and Biomedical Applications

Abstract

Virtually all fields of healthcare and biomedical research now rely on imaging as their primary data source. Though more and more data is being generated in the imaging centers, research shows that most of this data is discarded in routine practice. Further, certain routine practices in healthcare and biomedical research, such as radiological reporting and gene-to-physiology mapping, still represent relics of the pre-digital era that underutilize the available data and today?s computational technologies. The aim of this thesis is to use modern computer vision, image processing and computer graphic technologies to design reporting, analysis and diagnostic tools for healthcare and biomedical applications that not only better utilize existing, otherwise discarded, data but also uses modern techniques to enhance some of the archaic methodologies.

More specifically, using discarded radiological annotations, we aim to enhance traditional radiological reporting by proposing animated visual reports that highlight and position clinical findings in a three-dimensional volumetric context as opposed to the historic text-based white paper reports. In a second application on diagnosis of hepatic tumors, we employ already diagnosed cases of liver tumors to propose a fast content-based image retrieval system that assists experts in tumor diagnosis by retrieving similar confirmed cases from a database based on visual similarity of tumor images. As a third application we target the low efficiency age-old histological methodology of gene-to-physiology mapping and propose a defect detection framework that automatically identifies physiological defects in micro-CT images of transgenic mice.