Interactive segmentation of abdominal organs from 3D CT and MRI images

52 Pages·2010·1.62 MB·English

by  Olof Hansson

LiU-ITN-TEK- A --10/025--SE

Interactive segmentation of

abdominal organs from 3D CT and

MRI images

Olof Hansson

2010-06-04

Department of Science and Technology Institutionen för teknik och naturvetenskap
Linköping University Linköpings Universitet
SE-601 74 Norrköping, Sweden 601 74 Norrköping
LiU-ITN-TEK-A--10/025--SE
Interactive segmentation of
abdominal organs from 3D CT and
MRI images
Examensarbete utfört i medieteknik
vid Tekniska Högskolan vid
Linköpings universitet
Olof Hansson
Handledare Chunliang Wang
Examinator Örjan Smedby
Norrköping 2010-06-04
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© Olof Hansson
Abstract
Within the medical field, image segmentation is an important tool which can be
usedbyradiologistsandsurgeonswhowantquantitativemeasurementsofalesion
ororgan. Tobeclinicallyuseful,thetoolhastobefastandeasytouse. Thiswork
comprisesimplementationoftheimageforestingtransformforsegmentationusing
the Dijkstra algorithm and compares computation time between the implemented
algorithmandapreviousimplementedalgorithm,Bellman-Ford. Thesealgorithms
solve the shortest path with minimum cost problem. For a given cost function,
similar results both in computation time and visual results were obtained with
the two algorithms. Changing the cost functions, on the other hand, yielded very
different segmentation results. The volume of liver and kidney was compared
with manually delineated organs regarding seed planting and execution time. A
graphical user interface has also been implemented.
1

Acknowledgments
First of all I would like to thank my excellent supervisor, Chunliang Wang, Ph.D
student at CMIV for his encouragement, patience and support to me during this
thesis. He has been a major influence with his sense of humour, positiveness, joy
and knowledge.
Thanks also to my examiner Professor Örjan Smedby, who gave me this opportu-
nity and has come with straight suggestions whenever I felt vacillating. Thanks
to all CMIV colleagues for a warm and open enviroment making my thesis time
enjoyable.
I would also like to thank my family and friends for supporting me and believe in
me through all these years.
There is a particular friend that I want to thank. Avraz Hirori, originially from
Kurdistan, but now living in France. With a rare intellect he has influenced me
mostofeverybodyIhavemetduringthelastsixyearsofmylifeinNorrköpingand
Linköping. Thestoriesabouthislifewerebreathtakingandexciting,remembering
me how easy life is in Sweden compared to other countries in the world. He gave
me new persepectives of life by which one could see and distinguish which parts
in lifethat are mostimportant. He also learned me about choosing harddecisions
in life and how to take care of them.
I miss our discussions about life and culture.
I miss our nights at school when studying math until late meanwhile having a
great time and for the first time really understanding math.
I miss his joy, fantasy and energy.
I miss my friend.
Finally, I thank my lovely Marie for supporting me through all this work. She
has been an enormous energy source with her positiveness, joy and love. I will
never forget your face expression when carefully riding your new bike.
For the first time in my life I really understand the change mathematics and
physics can do. I think about the people that have contributed to this part of
science before me. This work is built upon that knowledge.
3

Contents
1 Introduction 1
1.1 Problem description . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Aim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Reader prerequisites . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Background 5
2.1 Medical images and Acquisition techniques . . . . . . . . . . . . . 5
2.1.1 CT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.1.2 MRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.3 Minimum-cost path search . . . . . . . . . . . . . . . . . . . . . . . 10
2.3.1 Simple case - Dijkstra . . . . . . . . . . . . . . . . . . . . . 11
2.3.2 Simple case - Bellman-Ford . . . . . . . . . . . . . . . . . . 13
2.3.3 2D case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3.4 Connectivity map . . . . . . . . . . . . . . . . . . . . . . . 14
2.3.5 Label map. . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3.6 Moving on to 3D . . . . . . . . . . . . . . . . . . . . . . . . 15
2.4 Cost of the path . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.5 Cost of edges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3 Implementation 19
3.1 Minimum-cost path search algorithms . . . . . . . . . . . . . . . . 19
3.2 I/O of the Image Foresting Transform Module. . . . . . . . . . . . 20
3.2.1 Version 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.2.2 Version 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.3 An application for abdominal organ segmentation . . . . . . . . . . 23
4 Results 25
4.1 Computation performance . . . . . . . . . . . . . . . . . . . . . . . 25
4.1.1 Segmentation results - Dijkstra vs Bellman-Ford . . . . . . 26
4.1.2 Cost functions evaluation - Kidney . . . . . . . . . . . . . . 27
4.1.3 Cost functions evaluation - Liver . . . . . . . . . . . . . . . 28
5
6 Contents
5 Discussion 31
5.1 Computation results . . . . . . . . . . . . . . . . . . . . . . . . . . 31
5.2 Visual results of Dijkstra and Bellman-Ford . . . . . . . . . . . . . 32
5.3 Cost functions and volume calculation . . . . . . . . . . . . . . . . 32
5.4 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
5.4.1 Improving Dijkstra . . . . . . . . . . . . . . . . . . . . . . . 33
5.4.2 Improving user interface . . . . . . . . . . . . . . . . . . . . 33
5.4.3 Cost functions . . . . . . . . . . . . . . . . . . . . . . . . . 35
5.4.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
Bibliography 37
A User interface manual 39
A.1 The interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
A.1.1 A little scenario. . . . . . . . . . . . . . . . . . . . . . . . . 40
Contents 7
List of Tables
2.1 Table of Dijkstra algorithm . . . . . . . . . . . . . . . . . . . . . . 12
2.2 Table of Bellman-Ford algorithm . . . . . . . . . . . . . . . . . . . 13
4.1 Seeding and computation time . . . . . . . . . . . . . . . . . . . . 26
4.2 ResultswhensubtractingDijkstrafromBellman-Fordsegmenteddata 26
4.3 Results when calculating volume of kidney with different cost func-
tions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.4 Volume of kidneys manual segmented . . . . . . . . . . . . . . . . 28
4.5 Results when calculating volume of kidney with different cost func-
tions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4.6 Volume of liver manual segmented . . . . . . . . . . . . . . . . . . 29
List of Figures
2.1 A graph system - a simple case . . . . . . . . . . . . . . . . . . . . 11
2.2 The label map - in theory . . . . . . . . . . . . . . . . . . . . . . . 15
3.1 The Connectivity Map . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.2 The Label Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.3 The 2D interface part . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.4 The 3D interface part . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.1 Comparison of a segmented slice using different algorithm . . . . . 26
4.2 Comparison of segmented slice using different cost functions . . . . 27
4.3 Comparison of segmented slice of liver using different cost functions 29
A.1 The 2D interface part . . . . . . . . . . . . . . . . . . . . . . . . . 39
A.2 The 3D interface part . . . . . . . . . . . . . . . . . . . . . . . . . 41