Database Article
Isfahan MISP Dataset
Abstract
An online depository was introduced to share clinical ground truth with the public and provide open
access for researchers to evaluate their computer-aided algorithms. PHP was used for web programming
and MySQL for database managing. The website was entitled “biosigdata.com.” It was a fast, secure,
and easy-to-use online database for medical signals and images. Freely registered users could download
the datasets and could also share their own supplementary materials while maintaining their privacies
(citation and fee). Commenting was also available for all datasets, and automatic sitemap and semiautomatic SEO indexing have been set for the site. A comprehensive list of available websites for
medical datasets is also presented as a Supplementary.
Keywords: Algorithms, computers, dataset, online
Introduction
To stimulate the advancement of computeraided diagnostic research for medical image
and signals, there is need to have an online
depository to share clinical ground truth with
the public and provide open access for
researchers to evaluate their computer-aided
[1-4]
Such databases are collected
algorithms.
laboriously and are not commonly shared
with the community. New methods and
systems on medical image and signals are
mostly based on datasets by each developer;
however, such a strategy makes the performance comparison imperfect and unreliable.
The perfect performance testing demands a
solid evaluation using “case data” from
different modalities and after approval by
[4]
medical experts. To clear up the word “case
data,” an example on mammography could be
helpful; in such a case, at least two views of each
breast with digital radiography should be
provided along with magnetic resonance
[4]
imaging (MRI) or ultrasound. Furthermore,
the data classification (to normal and disease)
and possibly regions of abnormality labeled by
the expert as “ground truth” or “gold standard”
or “interpretation” are needed for a perfect
“case data.” The main idea behind designing
this website was to provide a collection
concentrated on medical datasets rather than
only a general data collection.
This is an open access article distributed under the terms of the
Creative Commons Attribution-NonCommercial-ShareAlike 3.0
License, which allows others to remix, tweak, and build upon
the work noncommercially, as long as the author is credited and
the new creations are licensed under the identical terms.
For reprints contact: reprints@medknow.com
Comprehensive collection of available
online datasets for medical image and
signal processing (MISP)
There were only a few attempts made to
establish appropriate reference datasets for
[2]
medical imaging and signal processing
until the past 10 years. The available
datasets did not include perfect “case
data” because most of them had no
ground truth and almost all of them were
not free of charge. However, undeniable
need for such datasets led to numerous
online medical image and signal databases
in recent years.
Masoud Kashefpur,
Rahele Kafieh1,
Sahar Jorjandi,
Hadis
Golmohammadi,
Zahra Khodabande,
Mohammadreza
Abbasi,
Nilufar Teifuri,
Ali Akbar
Fakharzadeh2,
Maryam
Kashefpoor3,
Hossein Rabbani1
Student Research Committee,
1
Medical Image and Signal
Processing Research Center,
School of Advanced
Technologies in Medicine,
Isfahan University of Medical
Sciences, Isfahan, Iran,
2
Department of Computer
Engineering, Higher Education
Institute of Allame Naeini,
Naein, Isfahan, Iran, 3Institute
for Cognitive and Brain
Sciences, Shahid Beheshti
University, Tehran, Iran
There are also some websites that list and/or
host multiple collections of data. Table 1 is a list
of such websites. Furthermore, we provide a full
list of databases (in mentioned websites or other
individual sources) as a Supplementary.
In this study, we introduce Isfahan MISP
dataset website and present a short description
of each dataset category. Furthermore, we
introduce a collection of available datasets
(sorted according to anatomical/physiological
information and imaging modality), which are
freely available on the internet. There are also
some websites that list and/or host multiple
collections of data, and we shortly discuss
about them.
How to cite this article: Kashefpur M, Kafieh R,
Jorjandi S, Golmohammadi H, Khodabande Z,
Abbasi M, et al. Isfahan MISP dataset. J Med Sign
Sence 2017;7:43-8.
© 2017 Journal of Medical Signals & Sensors | Published by Wolters Kluwer - Medknow
Address for correspondence: Dr.
Rahele Kafieh, Medical Image
and Signal Processing Research
Center, School of Advanced
Technologies
in
Medicine,
Isfahan University of Medical
Sciences, Isfahan, Iran.
E-mail: rkafieh@gmail.com
Website: www.jmss.mui.ac.ir
43
Kashefpur, et al.: Isfahan MISP dataset
Table 1: Some websites that list and/or host multiple collections of medical datasets
CVonline: Image Databases:
DATABASES ON MEDICINE AND MOLECULAR BIOLOGY
Database of cancer MR,CT,PET, . . . from lung, brain
National Biomedical Imaging Archive (NBIA):
• Lung Image Database Consortium (LIDC)
• Reference Image Database to Evaluate Response (RIDER)
• Breast MRI
• Lung PET/CT
• Neuro MRI
• CT Colongraphy
• Virtual Colonoscopy
• Osteoarthritis Initiative
• PET/CT phantom scan collection
BIRN / XNat
• High SNR Healthy Volunteer DTI Calibration Dataset
• Morphometry BIRN Multi-site Multi-session Structural MRI Data
• Open Access Structural Imaging Series (OASIS)
• Duke Center for In-Vivo Microscopy High Resolution MRI Images
MIDAS
• National Alliance for Medical Image Computing (NAMIC)
○ Lupus white matter lesions
○ Brain MRI: 2-4 years old
○ Prostate
• NLM: Imaging Methods Assessment and Reporting
• Liver tumors with segmentations
100 Healthy Brain MRI: 18-90 years old
UCI Machine Learning Repository: The father of internet
data achives for all forms of machine learning.
Cornell Visualization and Image Analysis (VIA) group: Provides a list of
available databases, many of which are also listed here.
UT Health Scient Center Image Collections: List of medical
images, atlases, and databases available on the web.
OmniMedicalSearch.com: Medical Image Databases & Libraries
Construction and Content
The working group in MISP research center had set one of
its goals to develop a reference medical image and signal
database for image and signal processing research and
development. The main aim of this collection was to
provide reliable datasets for researchers to compare their
algorithms on common framework and to be able to
interpret and compare the performance of their newly
developed methods. The collected databases were
devised to be in accordance with patient privacy and
copyright issues. They all have written and signed forms
by the institutional review board, also known as an
independent ethics committee. The ground truth was
provided for datasets based on evaluation of one, two,
44
http://homepages.inf.ed.ac.uk/rbf/CVonline/
Imagedbase.htm
http://www.meddb.info/index.php.en?cat=1
http://www.cancerimagingarchive.net/
https://imaging.nci.nih.gov/ncia/login.jsf
http://www.birncommunity.org/resources/data/
http://www.insight-journal.org/midas/
http://www.insight-journal.org/midas/community/
view/21
http://archive.ics.uci.edu/ml/
http://www.via.cornell.edu/databases/
http://www.library.uthscsa.edu/find/databases.cfm?
Category=Image%20Collections
http://www.omnimedicalsearch.com/
image_databases.html
or three experts. Some datasets were collected from
different modalities or from diverse anatomical structures of one patient. Most of the mentioned datasets
were available with a published paper in famous
journals to demonstrate possible application of the dataset.
Technical background
In this project, MySQL was used as the backend database.
MySQL is an open-source database management system. The
features of MySQL are listed below:
• Relational database management system. A relational
database stores information in different tables, rather
than in one giant table. These tables can be referenced
to each other, to access and maintain data easily.
Journal of Medical Signals & Sensors ¦ Volume 7 ¦ Issue 1 ¦ January-March 2017
Kashefpur, et al.: Isfahan MISP dataset
• Open source database system. The database software can
be used and modified by anyone according to their needs.
• Fast, reliable, and easy-to-use. MySQL is multithreaded
database engine, and this feature improves its performance. A multithreaded application performs many
tasks at the same time as if multiple instances of that
application were running simultaneously.
Multithreaded MySQL has many advantages. A separate
thread (which is always running) handles each incoming
connection and manages the connections. Multiple clients
can perform “read” operations simultaneously, but while
writing, only one client, who needs access to updated data,
is held up. Even though the threads share the same process
space, they execute individually, and because of this
separation, multiprocessor machines can spread the thread
across many central processing units (CPUs) as long as the
host operating system (OS) supports multiple CPUs.
Multithreading is the key feature to support MySQL’s
performance design goals, and it can be counted as the
core feature around which MySQL is built.
The proposed web programming for this website is PHP.
PHP is server side scripting language compatible with all
known OSs like Windows, Linux, etc. Nowadays, PHP
frameworks are being extensively used by programmers
to address the performance tuning issues faster and with
ease. They offer extensible architecture and features that
make source code programming easier by providing
standard templates and plug-ins. The main features of
PHP are listed below:
• Powerful database-driven functionality: PHP integrates
well with MySQL and contains a library full of useful
functions to help the usage of MySQL. There are even
many database managers written in PHP.
• Faster web applications: Because PHP does not use a
lot of a system’s resources to run, it operates much faster
than other scripting languages. Hosting PHP is also
very easy and lots of hosts provide support for PHP.
Even when used with other softwares, PHP still retains
speed without slowing down other processes. It can be
concluded that PHP is a mature language, and it is also
fairly stable because all the kinks have been worked out
over the years.
• Object oriented: PHP actually has the ability to call
Java and Windows COM objects. In addition to this,
one can create custom classes. Other classes can
actually borrow from those custom classes and this
specification extends the capabilities of PHP even further.
• High level freedom: When comparing PHP to a language
such as ASPX, the level of freedom is far superior. As
mentioned before, PHP is open-source. One can use any
text editor to code PHP such as Notebook++, jEdit,
Emacs, Bluefish, or even just Notepad if felt inclined. In
cases like development of applications with ASPX,
Microsoft Visual Studio will be a limited solution. PHP
also is not OS specific and can be run on all well-known
OS’s.
• Free of charge: There are no costs associated with
using PHP, including updates. Keeping costs down is
a goal of any business and developers as well.
Therefore, the fact that one can code programs with
PHP for free is a huge benefit that will not be provided
with JPS, ASP, or other scripting languages that require
paid hosting. There are no licenses, restrictions, or
royalty fees involved at all, and PHP is 100% free
for anyone to use.
Regarding above features, we used PHP for web programming
and MySQL for database managing. The resulted “biosigdata.
com” was a fast, secure, and easy-to-use online database for
medical signals and images. Among these features, “biosigdata.
com” provided registered users the ability to download medical
signals and images selectively and freely. It was also an
appropriate place for researchers around the world to share
their data and supplementary materials with each others,
because users could freely, easily, securely, and unlimitedly
upload their datasets while managing their privacies (citation
and fee). Uploaded datasets would be categorized and published
after verification. Commenting and replying for the comments
were available for all datasets; therefore, researchers could share
their experiences. To improve functionality in search engines
(e.g., Google), automatic sitemap and semi-automatic SEO
indexing have been set for the site.
Sample description for available sets in MISP dataset
website
To provide more description on contents of MISP dataset,
two examples of the available sets are described below. As a
long-term goal of MISP research center, the datasets would
be added to this website, and it was open to accept databases
from other researchers by convincing them to the reality that
making online free datasets can improve the research quality
of the owner and the users.
The first sample dataset was on ocular images and included
the following image sets:
tomography (OCT)
(1) MISP Isfahan optical coherence
[5]
dataset for segmentation.
This dataset contained 13 three-dimensional (3D) macular
SD-OCT images obtained from eyes without pathologies
using Topcon 3D OCT-1000 imaging system in
Ophthalmology Dept., Feiz Hospital, Isfahan, Iran.
The datasets were in mat format and were named “1” to
“13.” The x, y, z size of the obtained volumes was
512 × 650 × 128 voxels, 7 × 3.125 × 3.125 mm3, and
voxel size 13.67 × 4.81 × 24.41 μm3 [as shown in Figure 1].
The values of the proposed layer localization were
provided for 12 boundaries and were stored in another
mat file named “randomImages” (the rows corresponded
to each dataset and the columns contained the traced
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45
Kashefpur, et al.: Isfahan MISP dataset
[5]
Figure 1: The x, y, z size of the obtained volumes was 512 × 650 × 128 voxels
slices). Furthermore, the validation was based on manual
tracing by two observers, and mean value of the
mentioned tracings were provided in mat files named
“totalManual_1” to “totalManual_13.”
[6,7]
MISP
Isfahan OCT dataset for denoising.
(2)
This dataset contained six 3D OCT data using Topcon
3D OCT-1000 imaging system in Ophthalmology
Dept., Feiz Hospital, Isfahan, Iran. The datasets
were in mat format and were named “1” to “6.”
Participants in the dataset were diagnosed to have
retinal pigment epithelial detachment. The positions
of the mentioned slices were stored in another mat file
named “random_Topcon.”
of
(3) MISP Isfahan OCT dataset for vessel segmentation
[8]
OCT and Scanning Laser Ophthalmology (SLO).
This dataset contained 24 3D macular SD-OCT images
(along with SLO images) obtained from eyes without
pathologies using Heidelberg HRA OCT scanner
imaging system in Ophthalmology Dept., Noor
Hospital, Tehran, Iran. Each dataset consisted of a
SLO image and limited number of OCT scans with
size of 496 × 512 (namely, for a data with 19 selected
OCT slices, the whole data size was 496 × 512 × 19). All
pixels in the SLO images were labeled as either “vessel”
or “non-vessel” manually to make a standard in
evaluation of the proposed method. The datasets were
in mat format and were named “1” to “24.”
[9]
MISP
Isfahan OCT dataset from normal population.
(4)
This dataset contained 112 3D macular SD-OCT
images (along with SLO images) obtained from
eyes without pathologies from 76 people using
Heidelberg HRA OCT scanner imaging system in
Ophthalmology Dept., Noor Hospital, Tehran, Iran.
Each dataset consisted of a SLO image and limited
46
number of OCT scans with size of 496 × 512. The
datasets were in mat format and were named “1” to
“112.”
depth imaging OCT dataset from
(5) MISP Isfahan enhance
[10]
normal population.
This dataset contained 19 3D macular SD-OCT
images (along with SLO images) obtained using
Heidelberg HRA OCT scanner imaging system in
Ophthalmology Dept., Noor Hospital, Tehran, Iran.
Each dataset consisted of a SLO image and limited
number of OCT scans with size of 496 × 512. The
datasets were in mat format and were named “1” to
“19.”
corneal OCT dataset from normal
(6) MISP Isfahan
[11,12]
population.
This dataset contained 15 3D corneal SD-OCT
images obtained using Heidelberg HRA OCT
scanner imaging system in Ophthalmology Dept.,
Noor Hospital, Tehran, Iran. Each dataset consisted
of a limited number of OCT scans with size of
496 × 512. The datasets were in mat format and
were named “1” to “15.”
The second sample dataset was on audio and acoustic signals
and included the following datasets:
(1) Voice samples of patients with Parkinson’s[13] disease
(spontaneous swallows in Parkinson’s disease).
Data were collected from 34 patients (19 males)
who suffered from Parkinson’s disease (PD) (age =
59.85 ± 11.46 years). They also had videofluoroscopy
swallow study assessment at the same day. Patients’
demographic data, including age, sex, time from
commencement of disease, and the Hoehen and Yahr
scale score, which shows the stage of PD, were
collected via an informed interview. All participants or
their legal guardian signed a consent form to participate
in the experiments. The study had been approved by
the Biomedical Ethics Board of Isfahan University of
Medical Sciences. Each patient participated in the
following two tests: sound recording and oropharyngeal
videofluoroscopy. The two tests were performed
independently, whereas videofluoroscopy study was
done to validate which participant suffered from
aspiration generally.
Acoustic recording study: The acoustic recording
was conducted in the test room with indoor lighting,
which was at 24°C. There was ambient noise from
outside of the room but it was minimized during the
recordings. Swallowing sounds were recorded via
microphone (C417 omnidirectional condenser lavalier
microphone, AKG Acoustics, Austria) connected t
o a portable digital sound recorder (Edirol R-44,
Japan). The microphone was fixed at the center of a
1.5-cm-diameter rubber piece. This rubber piece was
attached to the surface of the skin using double-sided
Journal of Medical Signals & Sensors ¦ Volume 7 ¦ Issue 1 ¦ January-March 2017
Kashefpur, et al.: Isfahan MISP dataset
adhesive tape. The recorder amplified and digitized the
received signal at a sampling rate of 44.1 kHz. The
microphone was positioned over the laryngopharynx
of each patient for 15 min when the participant was in
a seated position watching a nature movie. Because no
food was used in this test, spontaneous swallows
that were registered were innate saliva swallows. The
number of swallows that each participant had within
the 15-min time period was varied. All data recordings
were carried out by a speech language pathologist and a
biomedical engineer.
(2) Voice samples of patients with internal nasal[14]valve
collapse before and after functional rhinoplasty.
This dataset contained voice recordings of participants
who had internal nasal valve collapse. These voice
samples were corresponding to the following two
groups: before and after functional rhinoplasty. The
initial voice samples were recorded 1–15 days
before the surgical operation. Inclusion criteria were
the lack of cold, hoarseness in all participants, and
menstruation in females on the voice recording day.
A demographic questionnaire was completed by the
participants. The participants were familiarized with
the method of the test before recordings. To minimize
the environmental noise, recordings were done in an
acoustic room (with noise of 28 dB based on sound level
meter [TES-1351]). The voice samples were recorded
separately for each participant. The participants were
in a seated position and a microphone (Somic SENIC
ST-818 3.5 mm on-ear stereo headphones with
adjustable microphone and 2.5 m cable) was located
5 cm from the center of their lips on the right corner
of mouth.
The participants were guided to generate the
vowels /a/ and /i/ in three tests for a period of 5 s.
The reason for choosing these vowels was their
different location of articulation in the vocal tract.
/i/ is the highest front vowel, and /a/ is the lowest
back vowel.
Vocal intensity during vowel prolongation was held
at 75 ± 2 dB (measured with sound level meter) by
asking each participant in person. The patients’ voice
sample was recorded at the same time using
[15]
at a sampling
Praat (version 5.0.23) software
frequency of 44.1 kHz using a laptop equipped with
a sound card. The surgical operation in all patients was
performed by the same surgery team. Voice signals
were obtained again 3 months after the surgery
under the same conditions. Prior to recording the
second voice samples, the participants underwent
examinations by an ENT specialist and also were
examined again in terms of inclusion and exclusion
criteria of the study. The collected voice samples
were encoded by a person who was not involved in
the analysis of voice signals.
Conclusion
The proposed online dataset aims at filling the gap in medical
image and signal depository, which can provide the sets to be
downloaded and uploaded with different privacies set by the
owners. The “biosigdata.com” is a free service, and the
structure makes it a fast, secure, and easy-to-use online
database.
Acknowledgements
The authors thank the following people (arranged
alphabetically) for gathering parts of datasets in the
website or for providing web addresses for databases:
Dr. Mohammadreza Akhlagi, Dr. Zahra Amini, Dr. Majid
Barekatein, Dr. Nasim Dadashi, Hajar Danesh, Dr. Mahdad
Esmaili, Iman Fartah Abadi, Amin Farah Abadi, Maria
Farahi, Marzieh Golabbakhsh, Shirimn Hajeb, Dr. Fedra
Hajizade, Jalil Jalili, Mehdi Kazemian, Dr. Saeed
Kermani, Tahere Mahmoodi, Dr. Alireza Mehri,
Mohammadreza Momenzadeh, Dr. Alireza Peyman, Atie
Purfarmanbar, Farhad Rahimi, Zahra Saeedi Zadeh,
Nilufar Salehi, Dr. Omid Sarrafzadeh, Dr. Mohammadreza
Sehhati, Ramin Soltanzadeh, Dr. Ardeshir Talebi, Dr. Alireza
Vard, and Hossein Yusefi.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
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