Evaluating mental workload of two-dimensional and three-dimensional visualization for anatomical structure localization.

Visualization of medical data in three-dimensional (3D) or two-dimensional (2D) views is a complex area of research. In many fields 3D views are used to understand the shape of an object, and 2D views are used to understand spatial relationships. It is unclear how 2D/3D views play a role in the medical field.

Colorization of CT images to improve tissue contrast for tumor segmentation.

Segmenting tumors from grayscale medical image data can be difficult due to the close intensity values between tumor and healthy tissue. This paper presents a study that demonstrates how colorizing CT images prior to segmentation can address this problem. Colorizing the data a priori accentuates the tissue density differences between tumor and healthy tissue, thereby allowing for easier identification of the tumor tissue(s).

Development of a customizable software application for medical imaging analysis and visualization.

Graphics technology has extended medical imaging tools to the hands of surgeons and doctors, beyond the radiology suite. However, a common issue in most medical imaging software is the added complexity for non-radiologists. This paper presents the development of a unique software toolset that is highly customizable and targeted at the general physicians as well as the medical specialists.

Tumor segmentation from computed tomography image data using a probabilistic pixel selection approach.

Automatic segmentation of tumors is a complicated and difficult process as most tumors are rarely clearly delineated from healthy tissues. A new method for probabilistic segmentation to efficiently segment tumors within CT data and to improve the use of digital medical data in diagnosis has been developed. Image data are first enhanced by manually setting the appropriate window center and width, and if needed a sharpening or noise removal filter is applied.

Three-dimensional segmentation of tumors from CT image data using an adaptive fuzzy system.

A new segmentation method using a fuzzy rule based system to segment tumors in a three-dimensional CT data was developed. To initialize the segmentation process, the user selects a region of interest (ROI) within the tumor in the first image of the CT study set. Using the ROI's spatial and intensity properties, fuzzy inputs are generated for use in the fuzzy rules inference system.

A collaborative interaction and visualization multi-modal environment for surgical planning.

The proliferation of virtual reality visualization and interaction technologies has changed the way medical image data is analyzed and processed. This paper presents a multi-modal environment that combines a virtual reality application with a desktop application for collaborative surgical planning. Both visualization applications can function independently but can also be synced over a network connection for collaborative work.

A virtual reality environment for patient data visualization and endoscopic surgical planning.

Visualizing patient data in a three-dimensional (3D) representation can be an effective surgical planning tool.As medical imaging technologies improve with faster and higher resolution scans, the use of virtual reality for interacting with medical images adds another level of realism to a 3D representation. The software framework presented in this paper is designed to load and display any DICOM/PACS-compatible 3D image data for visualization and interaction in an immersive virtual environment.

A framework for interactive visualization of digital medical images.

The visualization of medical images obtained from scanning techniques such as computed tomography and magnetic resonance imaging is a well-researched field. However, advanced tools and methods to manipulate these data for surgical planning and other tasks have not seen widespread use among medical professionals. Radiologists have begun using more advanced visualization packages on desktop computer systems, but most physicians continue to work with basic two-dimensional grayscale images or not work directly with the data at all.

A framework for interactive examination of automatic segmented tumors in a virtual environment.

An immersive virtual environment for viewing and interacting with three-dimensional representations of medical image data is presented. Using a newly developed automatic segmentation method, a segmented object (e.g.