| |
Introduction
2
the cortex into visual areas. A tool to assist with this process was developed and is described
here. It was called mrFindBorders and helps to identifying retinotopic visual areas by using a
model (atlas) that can be parameterized and deformed to fit an individual functional image.
Since it is easier to visualize and manipulate two-dimensional images than three-dimensional
data sets, mrFindBorders uses functional flat maps to perform the task of identifying reti-
notopic visual areas.
1.2 Purpose
This documentation might be useful for those who wish to evaluate the work done during the
course of this project, for users of mrFindBorders or the other programs described here, and
for programmers who intend to upgrade or extend the system.
This "Diplomarbeit" (Master’s thesis) was done to partially fulfill the requirements for the
academic degree “Diplom-Ingenieur” (Master of Science in Electrical Engineering and Infor-
mation Technology) in a degree program at the Department for Electrical Engineering and In-
formation Technology at Universität Karlsruhe (TH), Germany. The work was carried out at
Stanford University, Department of Psychology, Vision and Imaging Science and Technology
Activities (VISTA).
1.3 Scope
This document intends to explain how retinotopic visual areas can be semi-automatically seg-
regated. The whole sequence of tasks is described, starting with the data from the MRI scan-
ner and finishing with the visualization of a 3D brain with color-coded visual areas. In addi-
tion, background information, e.g. human physiology and anatomy is given. Many chapters
start with a brief overview, which introduces the topic and gives an overview of what the
reader can learn in the current chapter. After this introduction, the second chapter explains the
relevant anatomy and physiology, for example the cerebral cortex and the visual system. In the
|  |
|
| |
|
|
