- Email: [email protected]
Medical Image Processor and Repository – MIPAR
Informatics in Medicine Unlocked xxx (xxxx) xxx
Contents lists available at ScienceDirect
Informatics in Medicine Unlocked journal homepage: http://www.elsevier.com/locate/imu
Medical Image Processor and Repository – MIPAR Benjamin Aribisala a, b, *, Olusola Olabanjo a a b
Department of Computer Science, Lagos State University, Lagos, Nigeria Brain Imaging Centre, University of Edinburgh, Edinburgh, United Kingdom
A R T I C L E I N F O
A B S T R A C T
Keywords Medical image Image analysis Repository E-infrastructure Open source
Background and objectives: The recent progress in medical image analysis has resulted in image-guided therapy, virtual reality, and augmented reality, other innovations that have greatly improved healthcare delivery, improved the quality of life, and saved lives. The advances in Internet and network technology have produced web technologies which make it possible to offer platform or software as a service via the web, making it possible for end users to access computer resources or specialized computer tools remotely. However, the high cost of image acquisition, the limited availability of medical image analysts, and the limited collaborative efforts be tween medical experts and scientists are major challenges for medical image analysis in the developing world. The aim of this project was to devise a medical image e-infrastructure called Medical Image Processor and Analysis (MIPAR) to contain a repository of medical images acquired from Africa and a platform for processing medical images. Methods: The backend of MIPAR which is resident on a High-Performance Computing infrastructure was built using FutureGateway, a framework for building science gateway. The image upload and download module was built upon the framework of Open Access Repository with the front end developed using HTML, CSS and BootStrap. JavaScript and JQuery were used for scripting. User’s access to the server is controlled with HTTP response and a Client-Server Architecture, while the image processing tools on the server side communicate with PHP using Representational State Architecture (REST) API. Results: MIPAR was tested using brain MRI images. Images were submitted remotely via MIPAR’s web interface and kept in the repository. A 3D image of approximately 43MB was uploaded within 43 seconds. Downloading of the same image took approximately 30 seconds. To test the image processing facility, our request for brain extraction of the same image was successfully completed within 60 seconds. Conclusion: MIPAR allows users to donate, download and process medical images at no cost. It is our hope that such useful and unique tools will encourage collaboration, improve diagnosis, improve patient management, and promote open science in Africa.
1. Introduction Biomedical images are acquired from humans or animals, and they are used for clinical diagnosis, treatment and patient management. Medical imaging makes it possible to observe the internal organs of humans without surgery. There are many methods of acquiring medical images and each technique has its own application area, strengths and weaknesses [1,2]. Some of the common imaging techniques are X-ray, ultrasound, Computed Tomography (CT) and Magnetic Resonance Im aging (MRI). Medical image analysis focuses on development of computational and mathematical techniques pertaining to medical
images to make them useful for diagnosis, treatment and management of medical conditions. The recent advancements in medical image analysis have given birth to image-guided therapy [3], virtual reality, and augmented reality [4]; all these among other innovations have greatly improved health delivery, improved quality of life and also saved lives. The high cost of some imaging equipment, e.g. MRI, makes them non-affordable in the developing world and this greatly reduces the benefit of such devices, most especially in Africa [5]. Also, image acquisition is very expensive globally, but particularly in Africa. Another challenge for medical imaging in the developing world is the shortage of medical image analysts. These coupled with limited collaboration
DOIs of original article: https://doi.org/10.1016/j.imu.2019.100246, https://doi.org/10.1016/j.imu.2018.06.005. * Corresponding author. Department of Computer Science, Lagos State University, Lagos, Lagos State, Nigeria. E-mail address: [email protected] (B. Aribisala). https://doi.org/10.1016/j.imu.2019.100235 Available online 27 August 2019 2352-9148/© 2019 Published by Elsevier Ltd.
Please cite this article as: Benjamin Aribisala, Olusola Olabanjo, Informatics in Medicine Unlocked, https://doi.org/10.1016/j.imu.2019.100235
B. Aribisala and O. Olabanjo
Informatics in Medicine Unlocked xxx (xxxx) xxx
Fig. 1. Use case diagram description of MIPAR
between scientists and medical experts in the makes e-infrastructure very desirable. These amongst other factors have led to the recent idea of creating medical image repositories. Such repositories, even though they are available in some developed countries, are not generally available anywhere in Africa. Some of the common medical image repositories in the developed world are OASIS (http://www.oasis-brains.org/), ADNI (http://adni.loni.usc.edu/), NBIA (https://imaging.nci.nih.gov/ncia/ login.jsf), and Brainweb (http://mouldy.bic.mni.mcgill.ca/brainweb/). The Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC, https://www.nitrc.org/) and aylward (http://www.aylward. org/notes/open-access-medical-image-repositories) contain a more comprehensive list of the most popular medical image analysis re positories. A common limitation of all the existing repositories is that they were developed for sharing medical images, and hence they only have medical images, but they cannot be used for processing images. The implication of this is that laboratories without any image analyst or without access to the standard image analysis tools may not be able to obtain the best result from their medical images. Another limitation is that all of the images in the existing repositories were acquired from non-African individuals. These imply that those interested in conducting research work on medical images acquired from Africans living in Africa will not be able to carry out their studies. To the best of our knowledge, there is no repository that contains medical images acquired from Africa. The advent of a science gateway plus the recent advancement in data communication and network technology has produced web technologies which make it possible to offer Software as a Service (SaaS), thus making it possible for end users to access computer resources or specialized software tools remotely irrespective of their physical location. It is also possible to offer Platform as a Service (PaaS), providing users the priv ilege to exploit computing power remotely [6,7]. The focus of this project was to solve some of the aforementioned problems in medical imaging in the developing world by creating a
Fig. 3. The Federated Identity Login page.
platform that will allow investigators to share medical images from Africa, and also allow them to process medical images. In view of this, the aim of this project was to develop an e-infrastructure for sharing and processing medical images. The e-infrastructure, christened Medical Image Processor and Repository (MIPAR), is web-based and free for users. It combines the power of SaaS and PaaS because users will have access to some special image processing tools which run on a dedicated powerful server. MIPAR will be useful for clinicians and researchers interested in obtaining specific information from medical images. Postgraduate students will also have access to the tool, and can use it in their research work, either for guidance or as a tool for analyzing their im ages. The community will benefit greatly from this resource, because diagnosis can be faster and more accurate, and also patient management can be improved because experts will have a common platform to share expertise and experience, and case reports can also be shared. Medics and researchers at remote areas would not need to travel before having access to additional expertise; this could also lead to timely and improved diagnosis. It is our hope that such a useful and unique tool could encourage collaboration, improve diagnosis, improve patient management, and could also lead to increased life expectancy.
Fig. 2. The homepage of MIPAR 2
B. Aribisala and O. Olabanjo
Informatics in Medicine Unlocked xxx (xxxx) xxx
Fig. 4. Donate Image page.
Fig. 5. Download Image page. (a) The user selected the type of images to download. (b) The system returned the list of images in the repository that meet the search criteria\.
2. Materials and methods
future to include other types of medical images. The second step is to select the anatomy. Currently only brain, liver, lung, and chest im ages are allowed, but this will be extended in the future. Note that all four anatomic-based options are available for each of the types of images in the first step. In the third and final step, the user just clicks the donate button and a file browser is launched, the browser will allow the selection and uploading of any of the following types of images -.gz,.zip,.nii.gz and.nii. The user is then able to upload the image(s). The uploaded images are stored in the repository accessible to all users for free download. 2. Download Image Module. In this module, users will be able to download any of the images available in the repository. Similar to the Donate Image Module, users can select the types of images and the body anatomy (Fig. 5). This will initiate a query to list all of the images that satisfy the body anatomy and image type criteria. The users will then be able to download any of the images listed.
2.1. System description MIPAR is an open source platform, but it requires Federated Identity login access, which can be obtained by registering with any of the Federated Identity Provider [8]. After login, users can use any of the functionalities of MIPAR. MIPAR is accessible on the web (https: //mipar.sci-gaia.eu) and has five modules – donate images, download images, process images, download outputs and analyze data (Fig. 1). 1. Donate Image Module. This module will allow users to upload medical images. All donated images are protected by the open access policy to allow others to use them freely. The first step in this module is for the user to choose the type of image to donate (Fig. 4), currently there are only four types of images that can be uploaded – CT, MRI, ultrasound and Doppler images; this will be extended in the near 3
B. Aribisala and O. Olabanjo
Informatics in Medicine Unlocked xxx (xxxx) xxx
Fig. 6. Process Image page.
3. Process Image Module. This module will allow the user to imple ment image processing on the system. The user will be able to select the type of processing to do and the images to process. The first step is for the user to select the processing component (Fig. 6). The second step is the selection of the images to use. At the current time, only brain MRI images can be processed on MIPAR, but this will be extended in the near future to include other image types and other anatomies. The available image processing operations at the current time are: brain extraction, image segmentation and image registra tion. In the third step, the user will be required to upload the image (s) to process. The fourth step is the submission step; here the user will click the proceed button, and the system will use the selected operation to process the image(s) uploaded. 4. Download Output Module. This module will allow the user to download any of the processed images. The user has only 24 hours to download the images. The system automatically deletes any pro cessed images after 24 hours. This is to allow us manage the limited disk storage efficiently. 5. Analyze Data Module. This module will allow the user to perform some simple statistical analysis, e.g. comparison of data between two groups, comparison of measurements within the same group, corre lation analysis and regression analysis. Comparison between two groups utilizes the independent t-test while comparison of mea surements uses the paired t-test. Currently we implement only parametric tests for comparison, but non parametric tests will be included once more data is obtained. Correlation analysis includes both Pearson and Spearman while regression analysis enables both simple and multiple regression. Additional statistical tests will be included over time.
Table 1 Technologies used for MIPAR development. Technologies used in the front end.
HTML, CSS, JavaScript, JQuery & Twitter BootStrap
Modules and tools used in the back end Image formats supported Processing facilities supported in MIPAR Primary modules of MIPAR
PHP, Cþþ, Shell Scripting
Code Size
Analyze Format (.hdr/.img) OAR, FutureGateway, LINUX and FSL Image Download Image Donation Image Processing Download Output Data Analyses 63MB
technologies used, the image format, and the modules of MIPAR. In summary, MIPAR comprises of the backend and frontend (or interface). The backend consists of the clone of a data repository called the Open Access Repository (OAR, http://oar.sci-gaia.eu/). MYSQL was used to develop the database because of familiarity and its efficiency in handling structured data. The backend also contains some image analysis tools written in Cþþ and Futuregateway (https://github.com/futuregateway). The image analysis tools include the brain extraction tool [9], image registration tool [10,11] and image segmentation tool [12] from the FSL library (FMRIB Software Library, www.fmrib.ox.ac. uk/fsl/). The web interface was developed using HTML, CSS, BootStrap, Twitter BootStrap, Java Script and jQuery while the server-side scripting was done using PHP. JSON format was utilized for Application Programming Interface (API) interoperability through the Representational State (REST) technology. Processing was done on a Linux server, which was communicated to the client through HTTP Client-Server Architecture. The choice of the front-end tools employed in this system is supported by the
2.2. System implementation MIPAR was developed using many technologies. Table 1 contains the 4
B. Aribisala and O. Olabanjo
Informatics in Medicine Unlocked xxx (xxxx) xxx
Fig. 7. Processed image page.
cross-platform and dynamic nature that their combination allows. PHP was used on the server-side because of its easy-to-learn nature, ability to interact with many different databases, freedom of use and its variety framework of functions for the developers’ use. Given that MIPAR was intended to be free to all users, all technologies used were open source.
4. Discussion Imaging equipment is very expensive in developing world, and this makes the cost of image acquisition very high. These, plus the limited availability of medical image analysts in the developing world, and the need to share medical images and expertise amongst clinical experts, have led to the development of the Medical Image Processor and Re pository (MIPAR). MIPAR is an e-infrastructure, open source and web-based tool which enables users to donate, download, and process medical images. MIPAR also enables users to perform statistical analysis. It is our hope that MIPAR will provide investigators the opportunity to access medical images at no cost, for research purposes. This could improve medical diagnosis, treatment, and patient management. To the best of our knowledge, MIPAR is the first e-infrastructure that contains medical images acquired largely from Africa, and that also contains image analysis tools. MIPAR is user-friendly and easy to use. Another strength of MIPAR is that it contains image processing tools running on a dedicated high-power server, which implies that users can have images processed quickly and with the right tools. We compared MIPAR with other similar tools such as the Open Ac cess Series of Imaging Studies (OASIS, http://www.oasis-brains.org/), aylward (http://www. aylward.org/notes/open-access-medical-imagerepositories), National Biomedical Image Archive (NBIA, https://im aging.nci. nih.gov/ncia/login.jsf), Alzheimer Disease Neuroimaging Initiative (ADNI, http://adni.loni.usc.edu), SIMBA (http://www.via.co rnell.edu/visionx/simba/), Brainweb (http://mouldy.bic.mni. mcgill. ca/brainweb/), u-KoMIPS [13] and RayPlus [14]. OASIS, aylward, NBIA, ADNI and Brainweb are repositories for medical images and they do not have an image processing platform, whereas MIPAR doubles as a repository and an image processing platform. SIMBA, u-KoMIPS and RayPlus are image processing platforms but do not have an image re pository. Also, SIMB, u-KoMIPS and RayPlus allow upload of DICOM (Digital Imaging and Communications in Medicine) data only whereas MIPAR allows upload of 3D images, e.g. to analyze format. MIPAR does not use the DICOM data format because the DICOM format contains
3. Results 3.1. System testing and validation MIPAR was tested and validated using some sample anonymized brain MRI images. Fig. 2 shows the homepage of MIPAR while Fig. 3 is the Federated identity login page. The donate image page is depicted in Fig. 4 while the download image page is depicted in Fig. 5. Figs. 6 and 7 are image processing page and the processed image pages, respectively. In all of the figures, the red circled buttons were the actions taken by the user during testing. Test sample data were uploaded, downloaded and processed. Images were compressed before uploading and the images downloaded were in compressed format. Upload and download were fast, as they benefitted from the use of AJAX (Asynchronous JavaScript and HTML). However, the speed may depend on the Internet speed of the user. Using a 4GLTE internet modem, a 46MB image was uploaded within 43 seconds. Image processing was equally fast, within 60 seconds per image, and outputs were downloaded within 38 seconds. For experimentation, images were uploaded in Lagos, Nigeria, and the server is currently resident in Catania, Italy, a distance of 5356 km apart. Note that the speed was computed as the average speed after 50 ex periments. We experimented with skull stripping, image segmentation of brain images into three tissue classes, and image registration from one brain anatomical image to another. Skull stripping utilized the brain extraction tool of FSL [9] while image segmentation utilized FAST (FMRIB’s Automated Segmentation Tool) [12]. Image registration uti lized 12 parameters with affine transformation of FLIRT (FMRIB’s Linear Image Registration Tool) [10,11].
5
B. Aribisala and O. Olabanjo
Informatics in Medicine Unlocked xxx (xxxx) xxx
implementation stage of MIPAR and for their involvement during system testing and validation. Finally, we appreciate Omo Oiya of WACREN (West and Central African Research and Education Network) and Owen Iyoha of EKO KONNECT (Lagos State based research and education initiative) for facilitating the collaboration between Lagos State Uni versity and SCI-GAIA.
confidential information about the subjects. By using the analyze format for 3D images, confidential information is automatically removed. Additionally, MIPAR was developed largely for Africa, where the Internet is very expensive and upload or download speeds are very slow. Using the DICOM format implies large data will have to be uploaded or downloaded, which may not be cost-effective for Africans. Furthermore and to the best of our knowledge, MIPAR is the only e-infrastruture that has processing platforms and repositories containing medical images acquired from Africa. One of the major limitations of MIPAR is that the repository contains only brain MRI images. This is because we do not have other types of images at the current time. It is our hope that as MIPAR becomes more popular, more types of images will become available in MIPAR’s re pository. The other limitation is the fact that MIPAR can only process brain MRI images at the current time. In the near future, and as more types of images become available, more image processing tools will be added to MIPAR to increase the processing functionality.
References [1] Brekel M, Castelijns J, Stel H, Golding R, Meyer C, Snow G. Modern imaging techniques and ultrasound-guided aspiration cytology for the assessment of neck node metastases: a prospective comparative study. Eur Arch Oto-Rhino-Laryngol 1993;250:11–7. [2] Cho Z-H. Foundations of medical imaging. Wiley-Interscience; 1993. [3] Marien A, Gill I, Ukimura O, Nacim B, Villers A. Target ablation—image-guided therapy in prostate cancer. Urologic oncology: seminars and original investigations, vol. 32. Elsevier; 2014. p. 912–23. [4] Ong SK, Nee AYC. Virtual and augmented reality applications in manufacturing. Springer Science & Business Media; 2013. [5] Ademola A. An appraisal of the cost benefit of magnetic resonance imaging in Nigeria. Niger Postgrad Med J 2003;10:251–3. [6] Tian W, Su S, Lu G. A framework for implementing and managing platform as a service in a virtual cloud computing lab. Education technology and computer science (ETCS), 2010 second international workshop on, vol. 2. IEEE; 2010. p. 273–6. [7] Jadeja Y, Modi K. Cloud computing-concepts, architecture and challenges. In: Computing, electronics and electrical technologies (ICCEET), 2012 international conference on. IEEE; 2012. p. 877–80. [8] Chadwick DW. Federated identity management. In: Foundations of security analysis and design V. Springer; 2009. p. 96–120. [9] Jenkinson M, Pechaud M, Smith S. BET2: MR-based estimation of brain, skull and scalp surfaces. Eleventh annual meeting of the organization for human brain mapping, vol. 17; 2005. p. 167 [Toronto]. [10] Jenkinson M, Smith S. A global optimisation method for robust affine registration of brain images. Med Image Anal 2001;5:143–56. [11] Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 2002;17:825–41. [12] Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 2001;20:45–57. [13] Jin S, Kim L-h. U-KoMIPS: a medical image processing system in a ubiquitous environment. Lect Notes Comput Sci December 2005;3823:278–87. [14] Yuan R, Luo M, Sun Z, Shi S, Xiao P, Xie Q. RayPlus: a web-based platform for medical image processing. J Digit Imaging 2017;30:197–203.
5. Conclusion We proposed an e-infrastructure termed Medical Image Processor and Repository (MIPAR). MIPAR enables users to donate, download, and process medical images at no cost. It is our hope that such a useful and unique tool will encourage collaboration, improve diagnosis, improve patient management, and promote open science in Africa. Conflicts of interest The authors do not have any conflict of interest. Acknowledgements We are grateful to the SCI-GAIA team under the dual leadership of Simon Taylor of Brunel University and Roberto Barbera of the University of Catania for providing us the opportunity to participate in the summer hackfest in Catania in July 2016. We also appreciate Roberto Barbera, Mario Torris and Bruce Becker for their technical support during the
6
Contents lists available at ScienceDirect
Informatics in Medicine Unlocked journal homepage: http://www.elsevier.com/locate/imu
Medical Image Processor and Repository – MIPAR Benjamin Aribisala a, b, *, Olusola Olabanjo a a b
Department of Computer Science, Lagos State University, Lagos, Nigeria Brain Imaging Centre, University of Edinburgh, Edinburgh, United Kingdom
A R T I C L E I N F O
A B S T R A C T
Keywords Medical image Image analysis Repository E-infrastructure Open source
Background and objectives: The recent progress in medical image analysis has resulted in image-guided therapy, virtual reality, and augmented reality, other innovations that have greatly improved healthcare delivery, improved the quality of life, and saved lives. The advances in Internet and network technology have produced web technologies which make it possible to offer platform or software as a service via the web, making it possible for end users to access computer resources or specialized computer tools remotely. However, the high cost of image acquisition, the limited availability of medical image analysts, and the limited collaborative efforts be tween medical experts and scientists are major challenges for medical image analysis in the developing world. The aim of this project was to devise a medical image e-infrastructure called Medical Image Processor and Analysis (MIPAR) to contain a repository of medical images acquired from Africa and a platform for processing medical images. Methods: The backend of MIPAR which is resident on a High-Performance Computing infrastructure was built using FutureGateway, a framework for building science gateway. The image upload and download module was built upon the framework of Open Access Repository with the front end developed using HTML, CSS and BootStrap. JavaScript and JQuery were used for scripting. User’s access to the server is controlled with HTTP response and a Client-Server Architecture, while the image processing tools on the server side communicate with PHP using Representational State Architecture (REST) API. Results: MIPAR was tested using brain MRI images. Images were submitted remotely via MIPAR’s web interface and kept in the repository. A 3D image of approximately 43MB was uploaded within 43 seconds. Downloading of the same image took approximately 30 seconds. To test the image processing facility, our request for brain extraction of the same image was successfully completed within 60 seconds. Conclusion: MIPAR allows users to donate, download and process medical images at no cost. It is our hope that such useful and unique tools will encourage collaboration, improve diagnosis, improve patient management, and promote open science in Africa.
1. Introduction Biomedical images are acquired from humans or animals, and they are used for clinical diagnosis, treatment and patient management. Medical imaging makes it possible to observe the internal organs of humans without surgery. There are many methods of acquiring medical images and each technique has its own application area, strengths and weaknesses [1,2]. Some of the common imaging techniques are X-ray, ultrasound, Computed Tomography (CT) and Magnetic Resonance Im aging (MRI). Medical image analysis focuses on development of computational and mathematical techniques pertaining to medical
images to make them useful for diagnosis, treatment and management of medical conditions. The recent advancements in medical image analysis have given birth to image-guided therapy [3], virtual reality, and augmented reality [4]; all these among other innovations have greatly improved health delivery, improved quality of life and also saved lives. The high cost of some imaging equipment, e.g. MRI, makes them non-affordable in the developing world and this greatly reduces the benefit of such devices, most especially in Africa [5]. Also, image acquisition is very expensive globally, but particularly in Africa. Another challenge for medical imaging in the developing world is the shortage of medical image analysts. These coupled with limited collaboration
DOIs of original article: https://doi.org/10.1016/j.imu.2019.100246, https://doi.org/10.1016/j.imu.2018.06.005. * Corresponding author. Department of Computer Science, Lagos State University, Lagos, Lagos State, Nigeria. E-mail address: [email protected] (B. Aribisala). https://doi.org/10.1016/j.imu.2019.100235 Available online 27 August 2019 2352-9148/© 2019 Published by Elsevier Ltd.
Please cite this article as: Benjamin Aribisala, Olusola Olabanjo, Informatics in Medicine Unlocked, https://doi.org/10.1016/j.imu.2019.100235
B. Aribisala and O. Olabanjo
Informatics in Medicine Unlocked xxx (xxxx) xxx
Fig. 1. Use case diagram description of MIPAR
between scientists and medical experts in the makes e-infrastructure very desirable. These amongst other factors have led to the recent idea of creating medical image repositories. Such repositories, even though they are available in some developed countries, are not generally available anywhere in Africa. Some of the common medical image repositories in the developed world are OASIS (http://www.oasis-brains.org/), ADNI (http://adni.loni.usc.edu/), NBIA (https://imaging.nci.nih.gov/ncia/ login.jsf), and Brainweb (http://mouldy.bic.mni.mcgill.ca/brainweb/). The Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC, https://www.nitrc.org/) and aylward (http://www.aylward. org/notes/open-access-medical-image-repositories) contain a more comprehensive list of the most popular medical image analysis re positories. A common limitation of all the existing repositories is that they were developed for sharing medical images, and hence they only have medical images, but they cannot be used for processing images. The implication of this is that laboratories without any image analyst or without access to the standard image analysis tools may not be able to obtain the best result from their medical images. Another limitation is that all of the images in the existing repositories were acquired from non-African individuals. These imply that those interested in conducting research work on medical images acquired from Africans living in Africa will not be able to carry out their studies. To the best of our knowledge, there is no repository that contains medical images acquired from Africa. The advent of a science gateway plus the recent advancement in data communication and network technology has produced web technologies which make it possible to offer Software as a Service (SaaS), thus making it possible for end users to access computer resources or specialized software tools remotely irrespective of their physical location. It is also possible to offer Platform as a Service (PaaS), providing users the priv ilege to exploit computing power remotely [6,7]. The focus of this project was to solve some of the aforementioned problems in medical imaging in the developing world by creating a
Fig. 3. The Federated Identity Login page.
platform that will allow investigators to share medical images from Africa, and also allow them to process medical images. In view of this, the aim of this project was to develop an e-infrastructure for sharing and processing medical images. The e-infrastructure, christened Medical Image Processor and Repository (MIPAR), is web-based and free for users. It combines the power of SaaS and PaaS because users will have access to some special image processing tools which run on a dedicated powerful server. MIPAR will be useful for clinicians and researchers interested in obtaining specific information from medical images. Postgraduate students will also have access to the tool, and can use it in their research work, either for guidance or as a tool for analyzing their im ages. The community will benefit greatly from this resource, because diagnosis can be faster and more accurate, and also patient management can be improved because experts will have a common platform to share expertise and experience, and case reports can also be shared. Medics and researchers at remote areas would not need to travel before having access to additional expertise; this could also lead to timely and improved diagnosis. It is our hope that such a useful and unique tool could encourage collaboration, improve diagnosis, improve patient management, and could also lead to increased life expectancy.
Fig. 2. The homepage of MIPAR 2
B. Aribisala and O. Olabanjo
Informatics in Medicine Unlocked xxx (xxxx) xxx
Fig. 4. Donate Image page.
Fig. 5. Download Image page. (a) The user selected the type of images to download. (b) The system returned the list of images in the repository that meet the search criteria\.
2. Materials and methods
future to include other types of medical images. The second step is to select the anatomy. Currently only brain, liver, lung, and chest im ages are allowed, but this will be extended in the future. Note that all four anatomic-based options are available for each of the types of images in the first step. In the third and final step, the user just clicks the donate button and a file browser is launched, the browser will allow the selection and uploading of any of the following types of images -.gz,.zip,.nii.gz and.nii. The user is then able to upload the image(s). The uploaded images are stored in the repository accessible to all users for free download. 2. Download Image Module. In this module, users will be able to download any of the images available in the repository. Similar to the Donate Image Module, users can select the types of images and the body anatomy (Fig. 5). This will initiate a query to list all of the images that satisfy the body anatomy and image type criteria. The users will then be able to download any of the images listed.
2.1. System description MIPAR is an open source platform, but it requires Federated Identity login access, which can be obtained by registering with any of the Federated Identity Provider [8]. After login, users can use any of the functionalities of MIPAR. MIPAR is accessible on the web (https: //mipar.sci-gaia.eu) and has five modules – donate images, download images, process images, download outputs and analyze data (Fig. 1). 1. Donate Image Module. This module will allow users to upload medical images. All donated images are protected by the open access policy to allow others to use them freely. The first step in this module is for the user to choose the type of image to donate (Fig. 4), currently there are only four types of images that can be uploaded – CT, MRI, ultrasound and Doppler images; this will be extended in the near 3
B. Aribisala and O. Olabanjo
Informatics in Medicine Unlocked xxx (xxxx) xxx
Fig. 6. Process Image page.
3. Process Image Module. This module will allow the user to imple ment image processing on the system. The user will be able to select the type of processing to do and the images to process. The first step is for the user to select the processing component (Fig. 6). The second step is the selection of the images to use. At the current time, only brain MRI images can be processed on MIPAR, but this will be extended in the near future to include other image types and other anatomies. The available image processing operations at the current time are: brain extraction, image segmentation and image registra tion. In the third step, the user will be required to upload the image (s) to process. The fourth step is the submission step; here the user will click the proceed button, and the system will use the selected operation to process the image(s) uploaded. 4. Download Output Module. This module will allow the user to download any of the processed images. The user has only 24 hours to download the images. The system automatically deletes any pro cessed images after 24 hours. This is to allow us manage the limited disk storage efficiently. 5. Analyze Data Module. This module will allow the user to perform some simple statistical analysis, e.g. comparison of data between two groups, comparison of measurements within the same group, corre lation analysis and regression analysis. Comparison between two groups utilizes the independent t-test while comparison of mea surements uses the paired t-test. Currently we implement only parametric tests for comparison, but non parametric tests will be included once more data is obtained. Correlation analysis includes both Pearson and Spearman while regression analysis enables both simple and multiple regression. Additional statistical tests will be included over time.
Table 1 Technologies used for MIPAR development. Technologies used in the front end.
HTML, CSS, JavaScript, JQuery & Twitter BootStrap
Modules and tools used in the back end Image formats supported Processing facilities supported in MIPAR Primary modules of MIPAR
PHP, Cþþ, Shell Scripting
Code Size
Analyze Format (.hdr/.img) OAR, FutureGateway, LINUX and FSL Image Download Image Donation Image Processing Download Output Data Analyses 63MB
technologies used, the image format, and the modules of MIPAR. In summary, MIPAR comprises of the backend and frontend (or interface). The backend consists of the clone of a data repository called the Open Access Repository (OAR, http://oar.sci-gaia.eu/). MYSQL was used to develop the database because of familiarity and its efficiency in handling structured data. The backend also contains some image analysis tools written in Cþþ and Futuregateway (https://github.com/futuregateway). The image analysis tools include the brain extraction tool [9], image registration tool [10,11] and image segmentation tool [12] from the FSL library (FMRIB Software Library, www.fmrib.ox.ac. uk/fsl/). The web interface was developed using HTML, CSS, BootStrap, Twitter BootStrap, Java Script and jQuery while the server-side scripting was done using PHP. JSON format was utilized for Application Programming Interface (API) interoperability through the Representational State (REST) technology. Processing was done on a Linux server, which was communicated to the client through HTTP Client-Server Architecture. The choice of the front-end tools employed in this system is supported by the
2.2. System implementation MIPAR was developed using many technologies. Table 1 contains the 4
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Fig. 7. Processed image page.
cross-platform and dynamic nature that their combination allows. PHP was used on the server-side because of its easy-to-learn nature, ability to interact with many different databases, freedom of use and its variety framework of functions for the developers’ use. Given that MIPAR was intended to be free to all users, all technologies used were open source.
4. Discussion Imaging equipment is very expensive in developing world, and this makes the cost of image acquisition very high. These, plus the limited availability of medical image analysts in the developing world, and the need to share medical images and expertise amongst clinical experts, have led to the development of the Medical Image Processor and Re pository (MIPAR). MIPAR is an e-infrastructure, open source and web-based tool which enables users to donate, download, and process medical images. MIPAR also enables users to perform statistical analysis. It is our hope that MIPAR will provide investigators the opportunity to access medical images at no cost, for research purposes. This could improve medical diagnosis, treatment, and patient management. To the best of our knowledge, MIPAR is the first e-infrastructure that contains medical images acquired largely from Africa, and that also contains image analysis tools. MIPAR is user-friendly and easy to use. Another strength of MIPAR is that it contains image processing tools running on a dedicated high-power server, which implies that users can have images processed quickly and with the right tools. We compared MIPAR with other similar tools such as the Open Ac cess Series of Imaging Studies (OASIS, http://www.oasis-brains.org/), aylward (http://www. aylward.org/notes/open-access-medical-imagerepositories), National Biomedical Image Archive (NBIA, https://im aging.nci. nih.gov/ncia/login.jsf), Alzheimer Disease Neuroimaging Initiative (ADNI, http://adni.loni.usc.edu), SIMBA (http://www.via.co rnell.edu/visionx/simba/), Brainweb (http://mouldy.bic.mni. mcgill. ca/brainweb/), u-KoMIPS [13] and RayPlus [14]. OASIS, aylward, NBIA, ADNI and Brainweb are repositories for medical images and they do not have an image processing platform, whereas MIPAR doubles as a repository and an image processing platform. SIMBA, u-KoMIPS and RayPlus are image processing platforms but do not have an image re pository. Also, SIMB, u-KoMIPS and RayPlus allow upload of DICOM (Digital Imaging and Communications in Medicine) data only whereas MIPAR allows upload of 3D images, e.g. to analyze format. MIPAR does not use the DICOM data format because the DICOM format contains
3. Results 3.1. System testing and validation MIPAR was tested and validated using some sample anonymized brain MRI images. Fig. 2 shows the homepage of MIPAR while Fig. 3 is the Federated identity login page. The donate image page is depicted in Fig. 4 while the download image page is depicted in Fig. 5. Figs. 6 and 7 are image processing page and the processed image pages, respectively. In all of the figures, the red circled buttons were the actions taken by the user during testing. Test sample data were uploaded, downloaded and processed. Images were compressed before uploading and the images downloaded were in compressed format. Upload and download were fast, as they benefitted from the use of AJAX (Asynchronous JavaScript and HTML). However, the speed may depend on the Internet speed of the user. Using a 4GLTE internet modem, a 46MB image was uploaded within 43 seconds. Image processing was equally fast, within 60 seconds per image, and outputs were downloaded within 38 seconds. For experimentation, images were uploaded in Lagos, Nigeria, and the server is currently resident in Catania, Italy, a distance of 5356 km apart. Note that the speed was computed as the average speed after 50 ex periments. We experimented with skull stripping, image segmentation of brain images into three tissue classes, and image registration from one brain anatomical image to another. Skull stripping utilized the brain extraction tool of FSL [9] while image segmentation utilized FAST (FMRIB’s Automated Segmentation Tool) [12]. Image registration uti lized 12 parameters with affine transformation of FLIRT (FMRIB’s Linear Image Registration Tool) [10,11].
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implementation stage of MIPAR and for their involvement during system testing and validation. Finally, we appreciate Omo Oiya of WACREN (West and Central African Research and Education Network) and Owen Iyoha of EKO KONNECT (Lagos State based research and education initiative) for facilitating the collaboration between Lagos State Uni versity and SCI-GAIA.
confidential information about the subjects. By using the analyze format for 3D images, confidential information is automatically removed. Additionally, MIPAR was developed largely for Africa, where the Internet is very expensive and upload or download speeds are very slow. Using the DICOM format implies large data will have to be uploaded or downloaded, which may not be cost-effective for Africans. Furthermore and to the best of our knowledge, MIPAR is the only e-infrastruture that has processing platforms and repositories containing medical images acquired from Africa. One of the major limitations of MIPAR is that the repository contains only brain MRI images. This is because we do not have other types of images at the current time. It is our hope that as MIPAR becomes more popular, more types of images will become available in MIPAR’s re pository. The other limitation is the fact that MIPAR can only process brain MRI images at the current time. In the near future, and as more types of images become available, more image processing tools will be added to MIPAR to increase the processing functionality.
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5. Conclusion We proposed an e-infrastructure termed Medical Image Processor and Repository (MIPAR). MIPAR enables users to donate, download, and process medical images at no cost. It is our hope that such a useful and unique tool will encourage collaboration, improve diagnosis, improve patient management, and promote open science in Africa. Conflicts of interest The authors do not have any conflict of interest. Acknowledgements We are grateful to the SCI-GAIA team under the dual leadership of Simon Taylor of Brunel University and Roberto Barbera of the University of Catania for providing us the opportunity to participate in the summer hackfest in Catania in July 2016. We also appreciate Roberto Barbera, Mario Torris and Bruce Becker for their technical support during the
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