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Image Visualization

� Motivations

� Visualization Methods

◦ Two-Dimensional Methods

◦ Volume Visualization (Surface-Based, Direct Volume)

� Volume Processing and Classification

� Visualization Examples

ITCS 6010:Biomedical Imaging and Visualization 1 Image Processing and Analysis

Image Visualization

� Visualizations have always been used to

◦ Study relationship between anatomical structure and function

◦ Detect and treat trauma and disease

� Traditional visualizations were direct, via surgery, biopsy, or indirect,via extensive mental reconstruction

� The revolution in 3D/4D biomedical imaging (CT, MRI, PET) andcomputer reconstruction and rendering provides new powerful op-portunities.

� Image Processing and Visualization can help unlock critical informa-tion of objects and their properties in medical images.


Image Visualization:Why?

� Multi-modal 3D and 4D imaging from CT, MRI, PET, MEG, Ultra-sound, Microscopy have fueled developments in data differentiation,fusion, visualization

� Datasets are generally very large (mega-giga-tera bytes).

� Management, Processing, Analysis and Visualization requires highperformance computing.

� Advances in visualization technology will provide newtools/procedures that physicians “must have” to treat patients

� Quote from a physician, “If I can see it, I can fix it”

� Ability to see anatomical structures permits surgery planning, re-hearsal, to minimize trauma, avoid critical areas.


Image Visualization:Why?

� Realtime interactive visualization, advanced display technologies in-cluding VR, open new realms into medical practice.

� Images displayed and manipulated with immediacy, sufficient detailand speed to evoke a sufficiently ”real” experience.

� Interactive 3D environments permit physicians to view anatomy andfunction from any viewpoint, make accurate online measurements.

� Visualization Scale :

◦ Structure: From individual molecules, tissues and interstitial in-terfaces to complete organs and organ systems;

◦ Functional attributes: Biophysical, Biomechanical and physio-logical properties.

� Holy Grail: Fully immersive, real-time multisensory fusion of realand virtual information streams (Example: GE Surgical MRI scan-ner)


Visualization Methods: 2D Techniques

� 2D images from 3D/4D biomedical imaging datasets can show

◦ Original data

◦ No occlusion problems

� Difficulty: Optimal image plane might not be possible:

◦ Limitations of the imaging system

◦ Need sophisticated reconstruction techniques


2D Visualiztion: Multiplanar Reformatting

� Ability to display axis-aligned images of 3D/4D volume

� If the entire volume is in memory, displaying the images along eachimage plane maps to voxel ordering problem

� Example: 3D CT, MRI Volume: Axial, Sagittal, Coronal Images


2D Visualiztion: Multiplanar Reformatting


2D Visualization: Oblique Sectioning

� Optimal image plane more likely will lie along arbitrarily orientedplane

� Need to specify orientation, and accurate computer reconstruction


2D Visualiztion: Curved Planar Reformation

� Structures may be curvilinear: spinal canal, blood vessels.

� Difficult to capture such structures completely in an planar image.

� Possible Implementation: Trace pixels along a path in an orthogonalsection; display rows of voxels corresponding to the pixels as a 2Dimage.


Volume Visualization:Voxels and Cells

� Volume Data: An array of volume elements, termed “voxels”, or anarray of “cells”.

� Voxel Model:

◦ Area around the grid point has a uniform value(s)(hexahedral,spherical, etc)

◦ Advantage: No assumptions on data behavior between grid-points

� Cell Model:

◦ Models volumes as a collection of hexahedra (or other geome-try) with gridpoints at its corners

◦ Need an estimation scheme for data variation within the cell


Volume Visualization:Grids and Lattices

� Regular: voxels, cells are axis-aligned rectangular prisms

� Rectilinear: voxels, cels are axis-aligned, but inter-voxel(cell) spac-ing varies

� Structured: Non axis-aligned hexahedra (warped bricks); Exam-ples: Spherical, Curvilinear grids

� Block-Structured: Collection of structured grids

� Hybrid, Unstructured: Collection of any set of grid types


Volume Visualization: Processing Steps

� Data acquisition: Includes processing to increase data valuerange, contrast enhancement, free of noise

� Aspect Ratio Adjustment: Volume dimensions correspond tophysical dimensions of the acquired data - may require interpola-tion, additional slice generation, estimate missing values

� Data Classification

� Mapping operations: Transformation to geometric or display primi-tives


Volume Visualization: Anatomic ModelingSteps


Volume Visualization MethodsSurface vs. Direct Volume Rendering

Simulate

Filter

Data Data

MappingGeometricPrimitives

Render

Images

Playback

Summarize

MeasuredData

� Surface-Based Volume Rendering: Data is converted to geometricprimitives and a standard graphics pipeline is used to render thevolume; Examples: Isosurfacing(Marching Cubes, Dividing Cubes),Contour connecting

� Direct Volume Rendering: Volume is directly sampled and projectedto the final raster image (rendering). Examples: Ray Casting, Pro-jection Methods (VBuffer), Volume Splatting.

� Examples from the Visible Human Project (VHP) athttp://www.nlm.nih.gov/research/visible/animations.html



Surface Based Direct Volume Render ingObject Order Image Order

Contour Connecting V-Buffer Ray CastingOpaque Cubes Splatting Cell IntegrationMarching Tetrahedra Pixar Method Frequency Domain MethodsMarching CubesDividing Cubes


Volume Visualization: Data Classification

� Process of associating voxels with useful, significant information onobjects/properties represented by the medical volume.

� Two methods typically employed in volume visualization:

◦ Transfer Functions: Volume data (scalar/vector values, gradient,other features) mapped to opacity and color and input to render-ing pipeline

◦ Segmentation: Sophisticated algorithms are employed in asso-ciated voxels with clinically significant objects (tissues, organs,tumors)


Volume Visualization:Illumination Models

� Depth Shading, Depth Gradient Shading

� Maximum Intensity Projection

� Voxel Gradient Shading

� Volumetric Compositing


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