Model-Based Registration of Histological Images


Jeff Hung

Oral Defence Date: 

Wednesday, May 2, 2018 - 14:00


TH 434


Assoc. Prof. Okada, Profs. Yoon and Hsu


Anti-cancer drug development requires an understanding of the mechanism of action (MOA). Histological analysis of images taken from xenografts enable characterization of how the tissue reacts to different compounds. Immunohistochemical (IHC) stains highlight a specific aspect of the tissue. Registering a two-dimensional (2D) IHC image with its corresponding 2D cellular morphology image facilitates the study of complex immunological behavior. This thesis describes the design and implementation of a multi-stage process to register histological images. The method utilizes a Procrustean, an affine and a least-squares support vector regression (LS-SVR) transformation to register automatically segmented regions of interest. Performance was compared to a method that replaces the LS-SVR with a second order polynomial transform.

Jeff Hung

image registration, least-squares support vector regression, histological images