A-2003-2 Jouni Mykkänen,
Delineation of brain structures from functional positron emission tomography images.
July 2003.
Abstract.
Positron emission tomography (PET) imaging is a unique method for
studying biochemical processes involved in living species. It
provides quantitative information about the processes at a cellular
level, which is needed,
for example in
diagnosis of a disease and the development of new drugs. Quantitative
information can be determined from PET images by extracting volumes of
interest. In order to collect large databases of the functional data
derived from PET, new automatic methods for image analysis are
required.
The delineation of PET images is a challenging task due to noise and
individual
data contents in PET images.
It has not gained attention that it deserves.
This study proposes a new lossless image compression method and
novel approaches to delineate brain surfaces from PET brain images.
First, a low complexity lossless image compression method was
developed for noisy PET brain images. Next, a user-guided software
using intensity values of image was developed and utilized to
determine quantitative values from the PET brain images. Next, a
two-dimensional deformable model and the corresponding anatomical
references from MR images were applied to delineate cortical surfaces
from PET brain images. Deformable models are advanced delineation
methods entailing geometric shape and evolution rules, which connect
the model to data providing its adaptation to the salient features in
an image. This method was able to improve the registration alignment
and correct differences between the anatomical and functional
structures. However, proper segmentation of volumetric
PET images required a new three-dimensional deformable surface
model which was developed in close collaboration with this study.
It uses a global optimization to avoid the initialization problem
common with deformable models.
The new method was applied to extract surfaces
from images in PET brain studies with $^{18}$FDG and
$^{11}$C-Raclopride radiopharmaceuticals.
The delineation procedure was fully
automatic, repeatable and considerably faster than the entirely manual
delineation methods applied with PET images.
Consequently, the coarse cortical structures for the hemispheres
were determined in an iterative way and no anatomical references or user
interactions were required in the process.
This study contributes novel approaches for semi-automatic and
fully automatic surface delineation from PET brain images.
In addition,
an image compression procedure for PET brain images is proposed.
These provide new
possibilities for developing fully automatic applications for
neurological image analysis and databases.
Key words and phrases: brain surface extraction, volume of interest, deformable model,
three-dimensional image analysis, mid-sagittal plane, segmentation, compression.
Ph.D. Dissertation.
A-2003-2 has appeared electronically as
Acta Electronica Universitatis Tamperensis, vol. 271.
A-2003-3 Timo Poranen, Apptopinv - User's guide.
September 2003.
Abstract.
The maximum planar subgraph, maximum outerplanar subgraph, the
thickness and outerthickness of a graph are all NP-complete
optimization problems. Apptopinv is a program that contains
different heuristic algorithms for these four problems:
algorithms based on Hopcroft-Tarjan planarity testing algorithm,
the spanning-tree heuristic and various algorithms based on
the cactus-tree heuristic. Apptopinv contains also a
simulated annealing algorithm that can be used to
improve the solutions obtained from other heuristics.
Most of the heuristics have also a greedy version.
We have implemented graph generators for complete graphs, complete
k-partite graphs, complete hypercubes,
random graphs, random maximum planar and outerplanar
graphs and random regular graphs. Apptopinv supports three
different graph file formats.
Apptopinv is written in C++ programming language for
Linux-platform and GCC 2.95.3 compiler. To compile the
program, a commercial LEDA algorithm library (version 4.3
or newer) is needed.
pdf-file
A-2003-4 Heikki Hyyrö, Practical Methods for Approximate String Matching.
December 2003.
Abstract.
Given a pattern string and a text, the task of approximate string matching
is to find all locations in the text that are similar to the pattern.
This type of search may be done for example in applications of spelling
error correction or bioinformatics. Typically edit distance is used as
the measure of similarity (or distance) between two strings. In this
thesis we concentrate on unit-cost edit distance that defines the
distance beween two strings as the minimum number of edit operations
that are needed in transforming one of the strings into the other. More
specifically, we discuss the Levenshtein and the Damerau edit distances.
Approximate string matching algorithms can be divided into off-line and
on-line algorithms depending on whether they may or may not,
respectively, preprocess the text. In this thesis we propose practical
algorithms for both types of approximate string matching as well as for
computing edit distance.
Our main contributions are a new variant of the bit-parallel approximate
string matching algorithm of Myers, a method that makes it easy to modify
many existing Levenshtein edit distance algorithms into using the Damerau
edit distance, a bit-parallel algorithm for computing edit distance, a
more error tolerant version of the ABNDM algorithm, a two-phase filtering
scheme, a tuned indexed approximate string matching method for genome
searching, and an improved and extended version of the hybrid index of
Navarro and Baeza-Yates.
To evaluate their practicality, we compare most of the proposed methods
with previously existing algorithms. The test results support the claim of the
title of this thesis that our proposed algorithms work well in practice.
Ph.D. Dissertation.
A-2003-4 has appeared electronically in
Acta Electronica Universitatis Tamperensis, vol. 308.
A-2003-5 Poika Isokoski and Roope Raisamo,
Evaluation of a Multi-Device Extension of Quikwriting.
December 2003.
Abstract.
Several new text entry methods for mobile computers have been
proposed recently. Many of these have not been tested to reveal whether they
in fact perform as advertised. We report a longitudinal laboratory study
on the use of a system known as Quikwriting. We have extended Quikwriting
so that a stylus, a joystick, or a keyboard can be used as the input
device.
Twelve participants used the stylus and joystick modes of input in
twenty 15-minute sessions. In the end their performance with the keyboard
was tested to see if Quikwriting skill transfers to new devices easily.
Quikwriting was found to be functional with all of the tested devices,
but laborious to learn. The final text entry rates were 16.1 wpm with the
stylus, 13.2 wpm with the joystick, and 6.1 wpm with the keyboard.
A-2003-5 is available as paper copy only.
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