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Medipo – Medical Image Processing Online a Web Based Decision Support and Educational Tool for Medical Image Processing

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Medipo – Medical Image Processing Online a Web Based Decision Support and Educational Tool for Medical Image Processing MedIPO – Medical Image Processing Online A Web Based Decision Support and Educational Tool for Medical Image Processing Bengisu Cagiltay, Bilkent University, Turkey <[email protected]> Mert Ege Can, Bilkent University, Turkey <[email protected]> Imge Gokalp, Bilkent University, Turkey <[email protected]> Caner Sezginer, Bilkent University, Turkey <[email protected]> Abstract: Today, Magnetic Resonance Imaging (MRI) technology makes very serious contributions to medical science in order to make various decisions before surgery. However, surgeons working on MRI are required to make serious decisions about the location of tumor and their volume, size and shape. They have to make plans to minimize the damage to the patient while separating tumorous areas from the healthy tissues. This planning can be done manually by working on a large number of MRI with different sections or by means of a software. However, gaining these skills is a challenge for the surgical education programs. In this study a system is developed to analyze the patient data, detect and record abnormal masses with a semi-automated image processing tool for pre-surgery decision support and educational purposes. Through this database, this study aims to provide case-based try-and-error type practice alternatives to the surgical residents. MedIPO provides a web based system which allows its users to upload, view and analyze medical images online by providing a semi- automated image processing system. By such a practice it is aimed to improve the beginner level of surgical residents’ skills on better detecting the locations of abnormal masses to improve efficiency for the pre-surgery decision making process. This study summarizes the main concept of the system development, the used algorithms as well as the user interface for analyzing the MRI data for educational purposes. I. Introduction and Background of the Study Surgeons are required to analyze the MRI data of a patient and make a surgical plan prior to the surgeries such as brain surgery. However, there are not many educational tools to provide try-and error type of training for the surgery education programs. Today currently there exists some medical image processing systems available in the market such as Osirix Lite, 3D Slicer, AnalyzeDirect, Online DICOM Viewer and MicroDICOM. However, when compared to MedIPO, these systems have some limitations for educational purposes when their prices and functionalities are considered. For instance, Osirix Lite is a desktop application that allows to open and view DICOM [1] type images. The software has features such as image processing, image visualization, advanced post-processing techniques in 2D and 3D, fast performance, FDA & CE certification [2]. However, as it is a desktop application it has to be installed the computer for being used. Additionally, its cost for each installation might create a serious limitation for the educational purposes and it does not provide any guidance for the decisions being made. Another software 3D Slicer is a free desktop software package for image analysis and scientific visualization of DICOM images [3]. It has features like image processing, scientific visualization (3D) and image analysis. However, it does not automate image processing features, it has a lower quality user interface and since it is a desktop application this software also has limitations for educational purposes. Analyze Direct is a software that presents image processing, visualization and analysis tools [4]. However, its image processing features are manual and as it is a desktop application it has a cost for each installation, therefore it has limitations for educational purposes. Online DICOM Viewer is a web-based application that users can post medical DICOM images to view and save [5]. As it is free to use, web based and has visualization features, it is more beneficial for the educational purposes when compared to the previous products. However, it does not have any image processing features and it is not suitable for advanced processes which limits its educational use. MicroDicom is an application for primary processing and preservation of medical images in DICOM format [6]. MicroDicom includes viewing and saving medical images in DICOM format, brightness/contrast control, zooming and panning DICOM images, medical image processing operation. However, it has manual image processing features thus it is considered slow for responses when compared to automated systems. After conducting analysis of these tools, it has been decided that a system which does not require any installation of additional software and hardware, which is web-based and free of charge, which allows to store, compare, download and also provide automated image processing capabilities would allow much more accessibility to medical doctors and speed up their decision support and learning processes. Accordingly, Medical Image Processing Online (MedIPO) is developed to satisfy these requirements stated, in order to better support the medical education systems. MedIPO System MedIPO is a web based system which allows its users to upload and analyze MR images quickly and easily, online. The target audiences of this system are medical doctors. The system aims to provide an online viewer and image analyzer for medical doctors by providing a detailed and intractable interface. MedIPO can be used for pre-surgery planning, practice and educational purposes. The users are required to upload files in DICOM format [1]. For ethical reasons, the uploaded MR images will be automatically anonymized by the system. The system enables a medical doctor to specify the region of interest and it conducts a semi-automated segmentation of this region (e.g. ventricles of the brain, cortices, cerebellum, tumor, etc.). The system also allows the user to enter manual annotation and corrections in addition to the segmentation results. The final packages of the software are based on the Client-Server relations. The client and server packages share the Model-View-Controller (MVC) design pattern and the sub layers of the packages are designed according to the MVC structure. A visual representation of the package structure is presented in Figure 1. Figure 1: Package Structure Since MedIPO system is a web based application, the system does not require any direct external hardware components. The hardware-software interaction occurs between the web browser and the machine. The application requires internet connection on the machine in order to provide data transaction between the server and the client. As seen in Figure 2, the web browser interacts with Java servlets over HTTP requests. Figure 2: Hardware Software Mapping The main aim of MedIPO software is to provide a system with zero-footprint, which indicates that the system does not require any client side install or download. This allows the users to view and edit the medical images online, purely over the browser. In order to achieve zero-footprint software, all the data related to the system such as login information and DICOM images to be stored in the server’s internal disks. II. Algorithms and Design Decisions Two of the major algorithms used for the software are named as Magic Wand and Magic Grid. A brief description for both of the algorithms will be explained in this section. Magic Wand Magic Wand algorithm is a simple algorithm used for border detection of a selected region of interest. This algorithm works by taking an input from the user which specifies the region of interest. The pixels around the region of interest are collected in this algorithm and according to a specified threshold, the neighboring pixels are either selected or not. The same action is repeated for every new pixel added to the region of interest. One of the major design decisions based on the Magic Wand algorithm is due to the success rate of this algorithm. Magic Wand algorithm has a high success rate in clear, closed boundary images. However, since tumors might not always visualize clearly, in some images this algorithm did not provide highly accurate results. Therefore, one major design decision during the algorithm development of Magic Wand was to develop a separate algorithm that is less dependent on the color differences of pixels, which is the following algorithm Magic Grid. Magic Grid Magic Grid algorithm is based on the image processing algorithm of Superpixels (Slic). Magic Grid uses the Slic algorithm as a baseline, the original image is divided into Slics. Later, these Slic’s are presented to the user interface in order to interact with the user. The user selects the region of interest by clicking on the Slic’s and creates a region called “mask”. This mask is used in order to reference the neighbor images (i.e. previous and next images) or the original image itself. By using this mask, the region of interest is transferred to the previous and next images by adjusting the pixel values. The algorithm checks whether a pixel of a superpixel overlaps more than 50%, and the pixel’s color difference is less than the specified threshold. If both of these conditions are true, the new superpixel is selected in the neighboring images. As a design decision made during the algorithm development of Magic Grid, the primary algorithm consisted a different design from explained above. The primary design used the center points of Slic’s with combination of the threshold value. However, in order to provide more accurate results while using the Magic Grid algorithm, the design is changed to the approach explained above. III. System Workflow Server Workflow Server-side workflow is responsible for analyzing the process of a generated response after a request is received from the client. According to the request type, the data layer will decide on the relevant flow for the request which might be one of the following: Magic Wand, Magic Grid or Upload. In all three paths, after the request-specific processes are completed, a response is generated and returned to the server.
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