2016 3rd international conference on biomedical and
TRANSCRIPT
Proceedings of
2016 3rd
International Conference on Biomedical and
Bioinformatics Engineering
ICBBE 2016
Taipei, Taiwan
November 12-14, 2016
ISBN: 978-1-4503-4824-9
The Association for Computing Machinery
2 Penn Plaza, Suite 701
New York New York 10121-0701
ACM ISBN: 978-1-4503-4824-9
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III
Table of Contents
Proceedings of 2016 3rd International Conference on Biomedical and Bioinformatics Engineering
Preface……………………..………………………………………………………….…. ……..……………… V
Conference Committees…………………………………………………………………………………...…..VI
Session 1-Bioinformatics and Computational Biology
A Novel Framework for Repeated Measurements in Diffusion Tensor Imaging
Mohammad Alipoor, Irene Y.H. Gu, Andrew Mehnert, Göran Starck, and Stephan E. Maier
1
Tactile Sensor for Cardiovascular Catheters
Chao-Hsien Huang, Cheng-Hung Shih, and Nai-Jun An
7
Algorithm for Segmentation and Reduction of Fractured Bones in Computer-Aided Preoperative Surgery
Irwansyah, Jiing-Yih Lai, Terence Essomba, and Pei-Yuan Lee
12
Study on Ratcheting Behavior of Trabecular Bone with and without Marrow Under Cyclic Compression
Chao-lei Wei, Li-lan Gao, and Chun-qiu Zhang
19
Analysis of Methicillin-resistant Staphylococcus Aureus Using Apriori, DBSCAN, and K-means Algorithms
Min Young Lee, Taeseon Yoon
23
Influence of Contrast Enhancement Methods in Brain Tumor Detection
Ahsan Khawaja, Maher un Nisa
29
Mechanical Behavior of Articular Cartilage Soaked in Physiological Saline under Cyclic Compressive Loading
Dong-dong Liu, Li-lan Gao, and Xiao-yi Qin
35
Hand Motion Capture for Medical Usage
Kiyoshi Hoshino, Sota Sugimura, Motomasa Tomida, Naoki Igo, Isao Kawano, and Masahiko
Sumitani
40
Quantitative Analysis of Spectroscopy Data for Skin Oximetry
Audrey Huong, Xavier Ngu
46
Measurement of Rotational Eye Movement with Blue Light Irradiation
Kiyoshi Hoshino, Nayuta Ono, Motomasa Tomida, and Naoki Igo
50
Numerical Simulation of the Adenoidectomy Preoperative and Postoperative Upper Airway in Children with OSAHS
Chi Yu, Gang Wang, and Jing Zhang
55
IV
Development of a Swordsmanship Machine Enabling the Inner and Outer Muscles to be Safely Trained while Having Fun
Kiyoshi Hoshino, Chuanhan Cheng
59
Research on the Factors of Colonoscopy Screening Compliance in High-risk Colorectal Cancer Groups
MA Hong-mei, LU Jian-hong
63
FP-AK-QIEA-R for Multi-Objective Optimization Josimar Edinson Chire Saire
67
Session 2- Pharma Medicine and Biological Sciences
Potentiometric Determination of a Regulated Veterinary Drug via MIP-Modified Electrode
Yasmin D.G. Edañol, Marleane Rovi R. Ferrer, Rizzie Kimberly M. Raguindin, and Susan D. Arco
71
The Primary Study of the Relationship between Environmental Factors and Dawn Song in Varied Tits
Tingting Zhao, Jingfeng Lin, Xiande Zhang, Dongmei Wan, and Jiangxia Yin 75
The Impact of Externally Supplied Protein on Root and Phytohormone in Endangered Species Cercidiphyllum Japonicum Cutting Seedling
Shaohui Huang
81
Session 3- Environmental and Chemical Engineering
Preparation and Characterization of Kenaf Derived Heterogeneous Catalyst for Esterification Reaction
Koguleshun Subramaniam, Fei-Ling Pua, Kumaran Palanisamy, and Saifuddin M.Nomanbhay
86
Combination of Photocatalytic and Biological Process for Treatment of Wastewater
Nur Syazrin Amalina Abdullah, Sufian So’aib, Fazlena Hamzah
90
Preliminary Study of Electrolytic Cell a Treatment for Wasted Brine from Resin Regeneration
Patcharin Racho, Sasiwimon Namgool, Warutai Dejtanon
94
Session 4- Coastal and Urban Engineering
Submerged Breakwater Hydrodynamic Modeling for Wave Dissipation and Coral Restorer Structure
Safari Mat Desa, Othman A. Karim, and Azuhan Mohamed
98
Research on Sea Reclamation and Urban Sustainable Development
Shen Yilei
102
Author Index 106
V
Preface
This volume contains papers presented at the 2016 3rd
International Conference on Biomedical and
Bioinformatics Engineering, which was held during November 12-14, 2016 in Taipei, Taiwan.
ICBBE provides a scientific platform for both local and international scientists, engineers and
technologists who work in all aspects of biomedical and bioinformatics engineering. In addition to the
contributed papers, internationally known experts from several countries are also invited to deliver
keynote and plenary speeches at ICBBE 2016.
The volume includes 22 selected papers which were submitted to the conference from universities,
research institutes and industries. Each contributed paper has been peer-reviewed by reviewers who
were collected organizing and technical committee members as well as other experts in the field from
different countries. The proceedings tend to present to the readers the newest researches results and
findings in the field of biomedical and bioinformatics engineering.
Much of the credit of the success of the conference is due to topic coordinators who have devoted their
expertise and experience in promoting and in general co-ordination of the activities for the organization
and operation of the conference. The coordinators of various session topics have devoted a considerable
time and energy in soliciting papers from relevant researchers for presentation at the conference.
The chairpersons of the different sessions played important role in conducting the proceedings of the
session in a timely and efficient manner and the on behalf of the conference committee, we express
sincere appreciation for their involvement. The reviewers of the manuscripts, those by tradition would
remain anonymous, have also been very helpful in efficiently reviewing the manuscripts, providing
valuable comments well within the time allotted to them. We express our sincere and grateful thanks to
all reviewers.
ICBBE 2016 Organizing Committee
November 12-14, 2016
VI
Conference Committees
Conference Chairs Prof. David Zhang, Hong Kong Polytechnic University, Hong Kong
Assoc. Prof. Kuo-Yuan Hwa, National Taipei University of Technology, Taiwan
Conference Program Chairs Prof. Xuefeng Tong, Tongji University, China
Prof. Irene Yu-Hua Gu, Dept. of Signals and Systems, Chalmers Univ. of Technology, Sweden
Prof. Congo Tak Shing CHING, Graduate Institute of Biomedical Engineering, National Chung
Hsing University, Taiwan
Prof. Manoj R. Tarambale, Marathwada Mitra Mandal’s College of Engineering, Pune, India
Technical Committees Prof. Ming-Wei Lin, Institute of Biomedical Informatics, National Yang Ming University,
Taiwan
Prof. Chuang-Chien Chiu, Feng Chia University, Taiwan, R.O.C.
Prof. Mohamed Osama Abdelaal Rabie Elshazly, Cairo University, Egypt
Prof. Edwin Wang, National Research Council Canada, McGill University, Canada
Prof. Sujata Dash, Bijupatnaik University of Technology, Orissa, India
Prof. Helmut Zarbl, Rutgers, The State University of New Jersey, USA
Prof. Mohammed Bougataya, UQO Quebec Canada
Prof. Rita Singh Majumdar, Dept of Biotechnology, Sharda University, Greater Noida, India
Prof. Jun F. (James) Liang, Stevens Institute of Technology, New Jersey, USA
Prof. Muhammad Nawaz Iqbal, Pakistan Engineering Council, Pakistan
Prof. Ajitkumar Gorakhanath Patil, S.B.M.Polytechnic, Mumbai, India
Prof. Bimal Kumar Sarkar, Department of Physics, Galgotias University, India
Prof. Zhen Xie, Tsinghua National Lab for Information Science and Technology, Tsinghua
University, China
Prof. Alexander Polyakov, Sevastopol National Technical University, Russia
Prof. Pedro Joaquin Gutierrez-Yurrita, Instituto Politecnico Nacional, Mexico
Prof. Nagendra Kumar Kaushik, Plasma Bioscience Research Center, Kwangwoon University,
Seoul, South Korea
Assoc. Prof. Lee, Yuan-Chii Gladys, Taipei Medical University, Taiwan
Assoc. Prof. P.SHANMUGHAVEL, Department of Bioinformatics, Bharathiar University
Coimbatore, India
Dr. Wen-Ling Chan, Department of Bioinformatics and Medical Engineering, Asia University,
Taiwan
Dr. Muhammad Arshad Malik, Department of Bioinformatics & Biotechnology, International
Islamic University, Pakistan
Author Index
A
Ahsan Khawaja 29
Andrew Mehnert 1
Audrey Huong 46
Azuhan Mohamed 98
C
Chao-Hsien Huang 7
Chao-lei Wei 19
Cheng-Hung Shih 7
Chi Yu 55
Chuanhan Cheng 59
Chun-qiu Zhang 19
D
Dong-dong Liu 35
Dongmei Wan 75
F
Fazlena Hamzah 90
Fei-Ling Pua 86
G
Gang Wang 55
Göran Starck 1
I
Irene Y.H. Gu 1
Irwansyah 12
Isao Kawano 40
J
Jiangxia Yin 75
Jiing-Yih Lai 12
Jing Zhang 55
Jingfeng Lin 75
Josimar Edinson Chire Saire 67
K
Kiyoshi Hoshino 40, 50, 59
Koguleshun Subramaniam 86
Kumaran Palanisamy 86
L
Li-lan Gao 19, 35
LU Jian-hong 63
M
MA Hong-mei 63
Maher un Nisa 29
Marleane Rovi R. Ferrer 71
Masahiko Sumitani 40
Min Young Lee 23
Mohammad Alipoor 1
Motomasa Tomida 40, 50
N
Nai-Jun An 7
Naoki Igo 40, 50
Nayuta Ono 50
Nur Syazrin Amalina Abdullah 90
O
Othman A. Karim 98
P
Patcharin Racho 94
Pei-Yuan Lee 12
R
Rizzie Kimberly M. Raguindin 71
S
Safari Mat Desa 98
Saifuddin M.Nomanbhay 86
Sasiwimon Namgool 94
Shaohui Huang 81
Shen Yilei 102
Sota Sugimura 40
Stephan E. Maier 1
Sufian So' aib 90
Susan D. Arco 71
T
Taeseon Yoon 23
106
Terence Essomba 12
Tingting Zhao 75
W
Warutai Dejtanon 94
X
Xavier Ngu 46
Xiande Zhang 75
Xiao-yi Qin 35
Y
Yasmin D.G. Edañol 71
107
Algorithm for Segmentation and Reduction of Fractured Bones in Computer-Aided Preoperative Surgery
Irwansyah, Jiing-Yih Lai, Terence Essomba Mechanical Engineering Department
National Central University Taoyuan City 320, Taiwan
Pei-Yuan Lee Orthopedic Department
Show Chwan Memorial Hospital Changhua 500, Taiwan
ABSTRACT
CT images have extensively been used for the diagnosis of serious
bone injuries, such as comminuted fracture, and for the
preoperative planning of orthopedic surgery. In order to enhance
the use of CT images in preoperative planning, it is necessary to
develop a 3D modeling and simulation tools to acquire more
information regarding the real surgery. Bone segmentation and
reduction are two important simulation tools in computer-aided
preoperative surgery. By improving both algorithms can affect to
the feasibility of 3D preoperative planning for fractured bones and
provide more valuable information prior to decision making. We
provide a technique to model, segment and recover fractured bone
from CT images. A multi-region segmentation algorithm is
proposed to reduce processing time and get efficiency. A manual
and a semi-automatic bone reduction algorithm are proposed to
deal with different kinds of fractured bone cases. Two semi-
automatic bone reduction algorithms, multi-point and mirror
positioning, are proposed to improve the efficiency of manual
reduction. The manual reduction is still necessary to deal with
cases which cannot be caught by the semi-automatic algorithms.
Several realistic examples by using real patients’ CT images are
provided to illustrate the feasibility of the proposed method.
CCS Concepts • Applied computing→Health care information systems
Keywords
Bone reduction; bone segmentation; computer aided preoperative
surgery; 3D bone reconstruction.
1. INTRODUCTION Reconstruction of 3D model from computed - tomography (CT)
scan of patient has been routinely used to assist a clinician
deciding an appropriate operating procedure. In order to construct
3D model, the user should extract 2D image contour from each
slices of CT images that save digital images and communication
in DICOM format and apply an algorithm to convert those
contours to a 3D bone model. Two kinds of process that play
important key to generate 3D bone model are segmentation and
reduction. Segmentation is a process to recognize and separate the
bones. In segmentation process some drawbacks such as how to
segment multiple fragments of bone simultaneously, keep the gray
value of different regions of bone that may not be constant,
identify the transition intensity value near the joint area that
generally appears to be fuzzy, and some areas within the bone that
may have similar intensity to the surrounding soft tissue are still
the issues to be solved [1, 2]. In case of fracture, bones are more
difficult to identify because bone fragments may have arbitrary
shape and can belong to any bone in the nearby area [2]. The
observation of fractured lines and fragments using 2D images
such information can be analyzed with a 3D tool.
Bone segmentation approaches can be classified into three types:
interactive, semi-automatic and fully automatic [3]. The
interactive approach is typically applied on 2D images. The
approach works by detecting every contour slice of the bone of
interest. It is time consuming and may leads to errors. Semi-
automatic approach could provide and maintain accuracy as well
as completeness of the bone model. The fully-automatic is the best
method but most difficult. 3D region growing is a common
method applied for bone segmentation. The model constructed by
this method is good enough for visualization but lacks of accuracy
and completeness.
Our previous studies have been contributing to segmentation
algorithm for constructing 3D images and have come out with
most of bone structure to be segmented effectively by the
proposed bone segmentation algorithm. Reference [4] proposed a
region growing algorithm for segmentation of the volume data.
The two consecutive procedures considered are applied
simulatneously, image processing and triangulation. An
interactive region growing method is applied to guaranty that the
targeted bone structure growing is succesful. Reference [3]
presented an optimized method for multiple-bones segmentation
that includes automatic seed region definition, iterative region
growing as well as re-combination and re-segmentation
procedures. This approach beneficial for solving over flow
problem whereas multiple regions points combined together
which makes different bones too difficult to separate accurately.
Several researchers also have been working on an accurate,
complete, efficient and segmentation algorithm for constructing
3D images. Reference [2] proposed a less user interaction method
for segmentation and labeling bone fragments from CT images.
The region growing based method is employed to easily detect
and solve overgrowing cases. Reference [5] presented a new cross
validation based segmentation algorithm which automatically
extracts multiple-level bone structures using a combination of
anatomical knowledge and computational techniques. The
Permission to make digital or hard copies of all or part of this work for
personal or classroom use is granted without fee provided that copies
are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights
for components of this work owned by others than ACM must be
honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior
specific permission and/or a fee. Request permissions from
[email protected]. ICBBE '16, November 12-14, 2016, Taipei, Taiwan
© 2016 ACM. ISBN 978-1-4503-4824-9/16/11…$15.00
DOI: http://dx.doi.org/10.1145/3022702.3022703
12
algorithm successfully detects the bones and is effective as well as
robust in term of the quantitative measurements. Reference [6]
presented an interactive tool for separating bone fragments in CT
volumes. The proposed method combining direct volume
rendering with interactive 3D texture painting interface that
allows the user to identify and mark bone structures instantly. The
user enables place seed point directly either on the rendered bone
surfaces or individual CT slices. The random walks segmentation
algorithm applied to separate marked bones. Reference [7]
proposed an active contour segmentation process to identify the
exact location of the fracture region on the images. The
identification process of bone regions refers to differences the
gray level pixels on the images.
Reduction is a surgical procedure to restore a dislocated or
fractured part to its former place. To maintain stability of the
fracture, specific implant used as fixation device such as plates,
screws, nails or other implants which may be external or internal.
It is important to verify the accuracy of reduction by clinical tests
and X-ray, especially in the case of joint dislocations. The
reduction procedures still have variety of problems depending on
the type of bone and fracture. Aligning the two bone fragment in
order to recover their original posititon is one of the simple
fracture reduction case. If the fracture generates more than two
fragments, then manual alignment becomes a difficult task. For
that purpose some approaches consider to match fracture zones
and algorithms to calculate this zone are generated.
Reference [8] presented a review about the current approaches
employed to match and register bone fragments in complex
fractures. For matching procedure, they classified the reducing
complex bone fractures methods into three kinds; interactive tools,
match fracture areas, and register templates. After the matching
procedure, most of the proposed studies perform a final alignment
to callibrate the result. The alignment is performed by registering
fracture zones such as surface, lines or points to each other.
Reference [1] presented an integrated surgical simulation process
including 3D modelling, visualization, segmentation, bone
reduction, fixation and data output. In order to provide efficient
segmentation process, a bone semi-automatic segmentation is
employed by combining three procedures, multi-region growth,
region re-segmentation and region re-combination. The proposed
bone segmentation algorithm has shown bone structure segmented
successfully [3]. In bone reduction process, manual bone
reduction takes long operating time. Each of the fragment bone
transform piece by piece and the final result visually evaluated.
Reference [9] presented an extended study to resolve some
drawback in previous system and provide an effective 3D
preoperative planning surgery. Semi-automatic bone reduction to
overcome the inefficiency of manual bone reduction for multiple
fragments is introduced. In semi automatic bone reduction, multi-
point positioning and mirror positioning algorithms are proposed.
For the final alignment a registration algorithm is employed to fit
each broken fragment with the reference bone. Reference [10]
presented an extended study to investigate the feasibility of an
integrated preoperative planning system. The operating times
required for each step of preoperative planning and simulation is
measured and several patients’ images data are illustrated such as
femur, tibia, foot, proximal, scapula, distal hand and elbow.
In this paper, we provide a technique to model and segment bone
tissue from CT images, with different bone segments being
modeled individually. A multi-region segmentation algorithm is
proposed to determine seed points automatically, and propagate
all regions simultaneously. In addition, a bone reduction process
allows recovering the original status of broken bones. We provide
a manual and a semi-automatic bone reduction algorithm to deal
with different kinds of situation. The semi-automatic bone
reduction algorithm can be implemented when the CT images of
the un-broken side is available. Otherwise, the manual bone
reduction algorithm can be implemented. Bone segmentation and
reduction are two important simulation tools in computer-aided
preoperative planning for simulating the bone reduction process in
real surgery. Improving the algorithm of bone segmentation and
reduction may affect to the feasibility of 3D preoperative planning
for fractured bones and provide more valuable information prior
to decision making. In the following we organized the paper by
describing the entire stages of computer aided preoperative
surgery modules, the fractured bone segmentation and reduction
system, segmentation and reduction algorithm, realistic example
clinical cases to illustrate the feasibility of the proposed algorithm.
2. COMPUTER AIDED PREOPERATIVE
SURGERY The integrated preoperative planning system provides an
environment for 3D visualization and manipulation. The system is
named PhysiGuide. It is able to display and manipulate both 3D
medical images and computer aided design (CAD) models
simultaneously [10]. The system consists in several modules
including 2D and 3D display, bone segmentation, bone resection,
bone reduction, and fixation. Figure 1 shows the flow diagram of
the integrated preoperative planning system main modules.
Figure 1. Integrated preoperative planning system modules.
3. METHODS Improving the algorithm of bone segmentation and reduction
bring significant progress to the feasibility of 3D preoperative
planning for fractured bones and provide more valuable
information prior to decision making for surgery practitioners. We
provide a technique to model and segment bone tissue from CT
images, with different bone segments being modeled individually.
A multi-region segmentation algorithm is proposed to determine
seed points automatically, and propagate all regions
simultaneously. In addition, a bone reduction process allows
recovering the original status of broken bones. We provide a
manual and a semi-automatic bone reduction algorithm to deal
with different kinds of situation. The semi-automatic bone
CT/MRI images
Voxel-based
Representation
2D Segmentation
3D Region Growing
3D Fractured bones
View
3D Fractured bones
Modeling
3D
Reco
nstru
ction
Mo
delin
g an
d A
pp
lication
Volumetric
Representation
Contour-based
Generation
Volume RenderingVolumetric
Representation
3D Imaging
Representation
CAD-based Medical
Modeling
13
reduction algorithm can be implemented when the CT images of
the un-broken side is available. Otherwise, the manual bone
reduction algorithm can be implemented. Several realistic
examples by using real patients’ CT images are provided to
illustrate the feasibility of the proposed method.
3.1 Fractured Bone Segmentation The proposed bone segmentation algorithm includes three
procedures: multiple region growing, region re-segmentation and
region re-combination. The overall procedures are implemented
interactively through the user interface and can be performed
iteratively. The system accepts both X-ray and CT images which
are stored in DICOM format. Each slice of CT image has
512512 pixels and a gray value of 12 bits. The image data are
converted into volume data. It is essentially 3D matrices that
record the gray value of each voxel on the images. A volume
rendering technique is employed to show the ISO-surface of the
volume data. The skin, bone and soft tissue can be displayed more
realistically by shifting a threshold. Two kinds of threshold, initial
threshold (Ti) and target threshold (Tt) are determined first. The
procedure of the proposed algorithm for segmenting fractured
bone is depicted in Figure 2.
Figure 2. Flow diagram of bone segmentation.
Initial threshold is assigned for initial seed region growing. As the
initial threshold is increased, more seed regions are generated. It
means more possibilities to separate unclear boundaries but will
lead to over segmentation. Meanwhile, the target threshold is
provided to determine surface boundaries of the final region. If
target threshold is huge, the region growing will be stopped before
reaching the desired boundaries. It may result tiny and incomplete
region. If the target threshold is too small, the region growing will
become over flow. It means several regions may be wrongly
connected and part of the surface boundary is over grown.
Iterative 3D region growing aims to grow multiple bones
simultaneously and emphasize accurate segmentation of the
closed surface to the joint area. Two steps of multi region
segmentation procedures are performed in pairs: initial seed
region growing and simultaneous multiple regions growing.
Whenever a seed point is found, it follows the propagation of the
seed points to obtain a seed region. The procedure is fully
explained in Algorithm 1.
Algorithm 1, multiple region growth:
1. All seed regions are given
2. Find all the voxels on the region boundary (fronts/Fj) for all
seed regions (m=1)
3. The gray value of the bone is larger than its surrounding
tissues (Tm = Ti - m T)
4. Compute the average gray value of all voxels on each fronts
(Gavg, i)
5. Arrange the front in terms of Gavg, i, from maximum to
minimum (j=1)
6. Take the voxels in Fj
7. Expand each voxel along its six neighborhoods, stop the
expansion if the gray value of its neighborhoods (G) < Tm
8. Once the voxels on the front have been test, continuing by
test the voxel of next front
9. When all front has been tested, the inner loop of the iteration
is completed (j-j+1)
10. The index m is increased by 1 to reduce the intermediate
target threshold (Tm) for T
11. If Tm Tt, the process go back to first step for the next cycle
of expansion with an upgraded Tm
12. If Tm Tt, the process stop (m-m+1)
13. Region growing finished.
3.2 Fractured Bone Reduction The purposed bone reduction is to relocated fracture bone into
original position. The 3D bone reduction is used to shift the 2D
images approach limitation in term of fractured fragments,
fractured lines and assembling illustration. Manual and semi-
automatic bone reduction methods are provided to recover the
displacement of fractured bones by transforming to the original
positions and orientation in space. In manual bone reduction,
every single bone fragment is transformed through a user interface.
Three kinds of transformation is applied such as translating the
part on the view plane, rotating the movable part along the surface
normal to the viewing plane and rotating the movable part relative
to the part coordinate. The manual reduction result is judged
visually.
In semi-automatic bone reduction, two algorithms are employed:
multi-point positioning and mirror positioning. For the multi-point
positioning method, a number pairs of points along the fractured
contour are selected in sequence. A coordinate transformation
algorithm is used for aligning a piece of fragment respect to its
counterpart [11]. The accuracy of the alignment depends mainly
on the accuracy of the point pairs selected. The main factors affect
to this algorithm is the feature points on the fractured contour may
not be visible to distinct and selected. For the mirror positioning
method, the symmetry property of the bone is used to align the
broken fragment with respect to the normal half of bone. A mirror
plane is determined automatically based on the middle plane of
the broken fragment. The normal half of the bone is mirrored onto
the broken side and serves as a reference. The alignment of each
broken fragment is performed regarding to reference bone. The
entire of broken fragments are continuously transformed to
recover the original position in space. The accuracy of this
algorithm is affected by two factors, symmetry property
influencing the error of transformation and the algorithm does not
CT Images
Multiple region
growing
Generation of automatic
seed regions
Simultaneously growth of
multiple regions
Post-processing
Region re-combination Region re-segmentation
Determination of
thresholds, Ti and Tt
ISO-surface from CT
Image
STL model
14
work for tiny fragments. In serious injuries with comminuted
fracture bone, both manual and semi-automatic bone reduction
could be permitted applied simultaneously. The manual bone
reduction is required to deal with cases which cannot be dealt with
by the semi-automatic algorithms. In common practice, the mirror
positioning is first conducted following by multi-pairs points and
adjusting manually. The procedure of the proposed algorithm for
reduction fractured bone is shown in Figure 3.
Figure 3. Flow diagram of bone reduction.
Determining position and orientation of an object in 3D space
may refer to the alignment of the part coordinates to match the
model coordinates. To determine the coordinate transformation
matrix between the reference point and the target point,
the singular value decomposition (SVD) method is applied. A
schematic illustration describes the generating steps of three
measurement points method to evaluate the surface normal is
shown in Figure 4.
Figure 4. Three measurement points method. (a) The unit disc
with the three canonical unit vectors, (b) Unit disc
transformed by rotating axis [11].
Three points P1, P2 and P3 are assigned in a circle on the XY
plane. The center is on the origin of the circle and a small radius
assigned freely (). Any point on the circle is described (cos,
sin, 0)T, where =120.
The coordinate transformation matrix T can be decomposed into a
translation vector D and a rotation matrix R, as shown below [11]:
[
]
if Pt = (px, py, pz)
T, the translation vector D = (px, py, pz)T.
The rotation matrix R, can be obtained by computing the Z axis
( rotating to reverse direction . The steps of computation
rotation matrix R:
- The back-off direction = (nx, ny, nz)
- The rotation axis = ( = (-ny, nx, 0)T
- The angle between ( and
- The rotation matrix:
[
]
- The homogeneous transformation matrix:
[
]
The coordinate transformation is performed to convert three
points P1, P2 and P3 into the 3D space using the homogeneous
transformation matrix T. The new three points are denoted as P’1,
P’2 and P’3.
The part coordinate setup in the manufacturing process is adopted
to solve the positioning problem in reduction broken bone
fragments. The proposed algorithm consists in two stages: rough
and fine positioning. The rough positioning is implemented first to
obtain a series of coordinate transformations between the part
coordinates and model coordinates. First, the part coordinates are
brought into the neighborhood of the model coordinate. Next, the
fine positioning is implemented. It is an iterative measurement
and organized as automatic procedure. The procedure for part
registration process is fully explained in Algorithm 2.
Algorithm 2, part registration:
1. Find the initial coordinate transformation matrix by defining
the original measurement and the reference points.
-Transform part coordinate first
-Transform measurement point to the new part coordinate
2. Optimize of the surface points and the part coordinate
3. Upgrade the part coordinate
4. Re-measure the part
5. Compute the transformation matrix T
6. Upgrade the part coordinate with T
7. If the error (
[∑ ‖ ‖
] is not
satisfied, the process go back to re-measure the part step.
8. Once the error satisfied, the process stops.
9. Part registration finished
Manual bone reduction
Manually
transformation
Fractured Bone Reduction
Semi-automatic bone
reduction
Visually
validation
Multi point
positioning
Mirror
positioning
Selecting reference
points
Alignment fragment
(Coordinate Transform Algorithm)
Recognizing
symmetry properties
Determine
mirror plane
Selecting the normal
bone as reference
Alignment fragment
(Registration Algorithm)
CT Images
ISO-surface from CT
Image
Fractured Bone
Segmentation
InterfaceImplant/screw
model
Database
CAD model
(2)
(3)
(1)
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4. DISCUSSION In this study, several actual clinical cases by using real patients’
CT images are provided to illustrate the feasibility of the proposed
method. In segmentation of multi-part of bones, the most difficult
problem is to separate clearly bones near the joint area. Most
methods are not efficient enough as each bone must be processed
one by one. We provide a technique to model and segment bone
tissue from CT images, with different bone segments being
modeled individually.
In Figure 5, a B1 type of fractured pelvic pointed to illustrate the
segmentation process, which indicates that the target threshold is
grown independently. The common target threshold assigned is Tt
= 600, Figure 5(a) shows that the regions are not grown
completely. Then, the target threshold value is reduced to Tt = 185,
Figure 5(b) shows that all region are completely grown. A
displacement of hip bone near to sacroiliac joint can be observed
respect to 2D of CT image. The target threshold for bone
segmentation is set by the user interface and initial threshold is
fixed on 180. However, the overflow occurs in many regions that
make the broken fragments are not displayed clearly. For
example, the broken pubis is only displayed in a single color.
Therefore, re-segmentation is required to visible recognize the
broken fragments.
Figure 5. Pelvic case (Male 18Y), the fractured bone
segmentation, (a) in-complete segmented, (b) a completed
segmented of 3D image.
In order to show all particular fractured bones, the re-
segmentation is applied. The region re-segmentation procedure
applied semi-automatically, where the user only specifies the
target region and initial threshold. The re-segmentation can be
implemented repeatedly until all regions are properly segmented.
Region re-combination procedure is employed to combine
separated regions into a single region. The region combination
algorithm blends the adjacent region into another. It can be
employed continually until all undesired regions are blended
properly. The accuracy of the segmentation is evaluated by
observing the number of colors in the bone region. Figure 6
depicts bone region segmentation result that shows incorrect
separation process. Each of regions is identified by its own color.
A single region of sacrum separated into four regions and five
regions of hip bone. This occurs after simultaneous multiple
region growing, where all segmented regions are displayed in
different colors. It means one particular region of the normal bone
ideally should have only one color.
Figure 6. The semi-automatic Pelvic segmentation process, (a)
re- segmentation and (b) re- combination.
The integrity of broken fragments is mainly concerned. In this
case, a pelvic is unstable fracture due to lateral compression. To
recover this fracture, pelvic is pushed inward. The left hip bone
around the sacroiliac joint has displaced from the original position.
Manual and automatic reduction is used simultaneously as
depicted in Figure 7. The mirror positioning employed a normal
bone or a larger part of bone as a reference and mirrored it onto
the broken side. The displaced fragment of the pubis is aligned
along its displaced line and the right hip bone is aligned to
become symmetric with the left hip bone. In Figure 7(a), manual
reduction is required to adjust the positions and angles of the
displaced parts. The part of broken bone is move piece by piece
through a user interface with respect to the viewing plane. The
translation is either along the XY, XZ or YZ plane. In addition,
for rotation is rotation in X-axis, rotation in Y-axis and rotation in
Z-axis. The result of the manual bone reduction is judged visually.
A mirror plane is determined from the sacrum. Normal bone (right
hip bone) is mirrored to the broken bone (left hip bone) to serve as
a reference bone. Then, each of the broken bones is aligned with
respect to the reference bone, as shown in Figure 7(b). All broken
bone fragments are transformed to fit the original position in
space.
Figure 7. The Pelvic reduction, (a) repositioning displaced hip
joint, (b) mirror positioning technique.
Figure 8 depicts the result of pelvic reduction process and
alignment using the registration algorithm. The integrity of the
pelvic rings is the primary concerns. The hip bone is adjusted to
become symmetric with the reference.
(a)
(b)
Displaced area
before reduction
(a)
(1)
(2)
(3)(4)
(1)(2)
(3)
(4)(5)
(1)
(2)
(3)(4)
(5)
(1) (1)(1)
(b)
Displaced area
14 mm 5.5 mm
(a) (b)
(a)
(b)
(c)
(d)(e)
(a’)
(b’)
(c’)
(d’)
(e’)
16
Figure 8. The Pelvic registration
The other clinical of fractured bone cases examined to present the
performance of method proposed. Diagnosing of fractured bone
classification refers to the AO classification [12]. Figure 9
presents the Tibia Plateau with the fracture type 41-B1. The
fractured bone registration split of the lateral surface in 2
fragments recovered. Figure 10 depicts the lateral Tibia Plateau
split depression. The fracture type classification is 41-B3,
separated fractured bone into 5 fragments. The Tibia Proximal
with complete articular and multi-fragmentary is diagnosed 41-
C3.2 as shown in Figure 11.
Figure 9. Ilustration of the reduction results, Tibia Plateau
type 41-B1 FR (Male 16Y), the fractured bone registration
with 2 fragments recovered.
Figure 10. Ilustration of the reduction results, Tibia Plateau
type 41-B3 FR (Male 60Y), the fractured bone registration
with 5 fragments recovered.
Figure 11. Ilustration of the reduction results, Tibia Proximal
type 41-C3.2 FR (Male 48Y), the registration with 6 fragments
recovered.
The broken Distal Femur is diagnosed type 33-C2 with intact
wedge. Fractured femur has complete articular fractures and
multi-fragmentary. Figure 12 presents the registration of fractured
bone split within 4 fragments.
Figure 12. Ilustration of the reduction results, Distal Femur
type 33-C2 FR (Male 17Y), the registration with 4 fragments
recovered.
The comminuted fracture calcaneus is diagnosed 82-C type.
Figure 13 depicts the fractured calcaneus that registered 5
fragments for recovery to original position.
Figure 13. Ilustration of the reduction results, Calcaneus type
82-C FR (Male 42Y), the fractured bone registration with 5
fragments recovered.
5. CONCLUSION This paper presents a technique to model, segment and recover
fractured bone from CT images. A multi-region segmentation
algorithm has been used to reduce processing time and get
efficiency. In region growing, initial threshold and target
threshold are played to determine boundary of region. The target
threshold value should be controlled on common range value to
ensure all region growth completely. A manual and a semi-
automatic bone reduction algorithm have been performed to deal
with different kinds of fractured bone. Two semi-automatic bone
reduction algorithm, multi point and mirror positioning, were
applied to improve the efficiency of manual reduction. Multi-point
positioning, a number pairs of points is selected sequentially along
the fractured contour. The accuracy of the transformation highly
depends on the accuracy of the point pairs selected. The manual
reduction still required to deal with cases which cannot be caught
by semi-automatic algorithm. The proposed algorithm of bone
segmentation and reduction have contributed to improve the
feasibility of 3D preoperative planning for fractured bones and
provide more valuable information prior to clinician decision
making.
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Displaced area
after reduction
118.5 mm
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