PhD Thesis Final Defense to be held on July 13, 2020, at 12:00 (Teleconference via e:Presence)
Photo Credit: Sevastianos Chatzistergos
Thesis Title: Development of Mammographic Image Processing Methods, Based on Local Morphological Characteristics, for Computer Aided Diagnosis
Abstract: In this Thesis, a set of medical image processing methodsis presented, which can be utilized as building components of a computer aided diagnosis system (CAD) for mammography. A tensor like concept, which we call radial length, is introduced and shared among the proposed methods.
We first evaluate the use of tensor lengths on contrast enhancement. An enhancement process is used by the majority of CAD systems as a pre-processing step since it can significantly improve the performance of the whole system. Given the nature of mammographic images we attempt to perform contrast enhancement while preserving the diagnostically critical information. Next, we evaluate the use of radial lengths for the segmentation and removal of pectoral muscle from medio-lateral oblique mammographic images. The high diversity of images makes this segmentation task a very demanding one. The proposed procedure involves the use of radial lengths in order to define a number of candidate image points for the muscle-breast edge, followed by a line expansion procedure. We further evaluate the use of radial lengths for the identification of architectural distortions. Architectural distortions are the third most common form of mammographic lesions, behind masses and microcalcifications, but are the most difficult to identify. The proposed method tries to combine radial lengths with local binary patterns (LBP) and their modifications.
Finally, we propose an educational game for trainee radiologists in order to assist them in their efforts to effectively interpret mammograms. The main architectural characteristics of the game are defined after a thorough bibliographic analysis of modern learning theories and the way they can be applied in serious game development.
Supervisor: Konstantina Nikita, Professor
PhD student: Sevastianos Chatzistergos