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NiftyView: A Zero-Footprint Web Application for viewing DICOM and NIfTI files

Introduction

The purpose of developing yet another web-based image viewer, NiftyView, is to use WebGL to take advantage of the parallel computing power in Graphics Processing Units (GPU) hardware for the accleration of rendering and processing of medical images in web applications. Although several web-based medical image viewers such as Papaya 1, BrainBrowser 2 and Slice:Drop 3 are currently available, the slow performance of the web-based applications is still one of the major limitations of web-based image viewers. NiftyView is a free web application developed in JavaScript. It has zero footprint; only a web browser and an Internet connection are needed to run NiftyView. It’s advantageous over conventional desktop applications in that NiftyView doesn’t require installation and constant updates. The current version supports NIfTI4 and DICOM5 format. As a minimal image viewer, it’s a convenient tool for users who need a quick and easy tool for viewing medical images. Currently, the beta version of NiftyView is freely available at http://www2.hawaii.edu/\~weiran/NiftyView.html.

Approach

NiftyView is developed in JavaScript with jQuery6 for HTML document manipulation and event handling, jQueryUI7 for user interface, and DicomParser8 for parsing DICOM files. It’s compatible with popular web browsers including Internet Explorer, Safari, Firefox, and Opera. Either DICOM or NIfTI files can be loaded by dragging files into the browser window. Loaded images can be displayed in single-slice mode or tiled mode. After loading, images are automatically arranged according to the scan IDs for DICOM files and the file name for NIfTI files, respectively. Current functions include image zooming and adjustment of image brightness and contrast. The number of image columns can be adjusted in the tiled mode to maximize the use of the display space. The contrast and brightness of images can be adjusted by clicking and holding the right mouse button or using a double slider widget in the horizontal tool bar at the top of the window. For proof-of-concept, functions such as pixel windowing and scaling are programmed using WebGL by translating the arithmetic operations in image processing to 3D graphics primitives using WebGL’s programmable shaders. The pixel values of an image is loaded into a frame buffer. A vertex shader is programmed to define vertices corresponding to the coorddinates of the image, and a fragment shader is programmed to perfrom arithmetic operations, which are performed in parallel to a massive number of image pixels.

Results

![Image of NiftyView] (https://github.com/weirandeng/niftyview_brainhack_report/blob/master/niftyview_ui.png) Figure-1 shows a few sagittal MRI images displayed in titled mode after loaidng approximately 1,500 DICOM files from 11 MRI scans. It took approximately ten seconds to load all the DICOM files into NiftyView. The images are organized into different vertical tabs by the sequence names stored in the DICOM files.

![Image of WebGL vs. Canvas Comparison] (https://github.com/weirandeng/niftyview_brainhack_report/blob/master/2d_vs_gl_processing_time.png) Figure-2 shows a comparison of processing time as a function of the number of image pixels in JavaScript(blue) and WebGL (red). WebGL shows a factor of six to eight accelerations.

Discussion

One of the major limitations of current web-based image viewers is the slow performance compared to their desktop counterparts. There are collective efforts in industry to develop new technologies such as WebAssmebly and WebGL to narrow this performance gap. The highly parallel nature in processing image pixels independently allows the use of WebGL to achieve a signficant speedup, as shown in this abstract. Currently, there are several similar existing web applications such as Papaya, BrainViewer, and slicedrop.com, which are more mature and offer varieties of features. However, the main goal of the continuing effor in the development of NiftyView is to achieve a high performance for image processing using GPU via WebGL. NiftyView has a minimal biolerplate and can handle a large number of files with relative ease. Future work will be focused on developing a WebGL-accelerated version, adding more image processing features, and adding support of accessing files stored in HIPAA (Health Insurance Portability and Accountability Act) compliant cloud storage servies such as Box and Amazon S3. The stable version of NiftyView will be released under a General Public License that allows end users to freely run, modify, and share the program.

Conclusion

NiftyView is a free and convenient web application for quick and easy viewing of NIfTI and DICOM medical images. We have shown that a factor of six to eight acceleration can be achieved using WebGL for image processing.

References

Footnotes

  1. Papaya, https://github.com/rii-mango/Papaya. ↩

  2. BrainBrowser, https://brainbrowser.cbrain.mcgill.ca. ↩

  3. Slie:Drop, http://slicedrop.com. ↩

  4. NIfTI Data Format Working Group, NIfTI: Neuroimaging Informatics Technology Initiative, http://nifti.nimh.nih.gov. ↩

  5. The association of Electrical Equipment and Medical Imaging Manufacturers, DICOM: Digital Imaging and Communications in Medicine, http://dicom.nema.org. ↩

  6. jQuery JavaScript Library, http://jquery.com. ↩

  7. jQuery User Interface, http://jqueryui.com. ↩

  8. Chris Hafey, https://github.com/chafey/dicomParser. ↩