{"id":40,"date":"2012-06-15T13:32:05","date_gmt":"2012-06-15T18:32:05","guid":{"rendered":"http:\/\/projects.vrac.iastate.edu\/ewiner\/?page_id=40"},"modified":"2013-07-17T17:14:10","modified_gmt":"2013-07-17T22:14:10","slug":"medical","status":"publish","type":"page","link":"https:\/\/projects.vrac.iastate.edu\/ewiner\/medical\/","title":{"rendered":"Medical"},"content":{"rendered":"

Medical Imaging<\/h1>\n

Graphics technology has extended medical imaging tools to the hands of surgeons and doctors, far beyond the radiology suite. However, a common issue in most medical imaging software is the complexity for non-radiologists to access the patient\u2019s information. Therefore, the Isis project was started to research the area of building medical imaging software for non-radiologists. There are many different sub-projects focusing to improve the quality of medical technology available to surgeons and doctors.<\/p>\n

The software package Isis is broken into three key components: CAVE, Desktop and Tablet.<\/p>\n

Current Work<\/h1>\n

Vipre<\/h2>\n

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Vipre is a cross-platform engine for all three platforms (Desktop, Mobile, and Immersive), which allows a seamless transition from one environment to the next depending on the needs of medical professions.\u00a0 The same dataset can be viewed in real-time on the surgeon\u2019s desktop computer, tablet or in an immersive CAVE. Vipre can perform real time volume rendering of digital medical images on iOS devices. This difficult task is made possible using custom developed GPU shaders for orthogonal texture slicing. To ensure smooth interaction novel techniques were required, such as, dynamically modifying render resolutions, an incremental render loop, a shader-based clipping algorithm to support OpenGL ES 2.0, and an internal backface culling algorithm for properly sorting rendered geometry with alpha blending. The application was developed using OpenSceneGraph (OSG) as the primary graphics renderer coupled with iOS Cocoa Touch for user interaction, and DCMTK for DICOM I\/O. The application renders volume datasets over 450 slices up to 50-60 frames per second, depending on the specific model of the iOS device. All rendering is done locally on the device hardware, so no Internet connection is required unlike other techniques using HPC clusters to compute the visualization and send an image back to the device over the network.<\/p>\n

http:\/\/youtu.be\/i6rJ1juDzhY<\/p>\n

Desktop<\/h2>\n

The desktop project is a unique software application that is highly customizable and targeted at the general physicians as well as medical specialists. This platform serves as a testing environment for medical imaging functionality such as viewing medical images in two- and three-dimensional representations, clipping, tissue windowing, coloring and interaction. Additional features can be loaded in the form of \u2018plug-ins\u2019 such as tumor segmentation, tissue deformation, and surgical planning. This allows the software to be lightweight and easy to use while still giving the user the flexibility of adding the necessary features, this catering to a wide range of user populations. The desktop version is built on Qt, VTK, and DCMTK open-source libraries.\u00a0 The primary function for the desktop version is to study and evaluate the design of medical imaging for the user.\u00a0 Isis has been used to: Evaluate two-dimensional vs. three-dimensional representations for localizing anatomical features.<\/p>\n

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Previous Work<\/h1>\n

Tumor Segmentation<\/h2>\n

Using techniques such as probability generation, fuzzy logic, and colorization; tumors can be automatically segmented from digital medical images (CT\/MRI). Current research has produced methods that can segment tumors with 55%-96% accuracy.<\/p>\n