# Presentation: 443618

## Medical Image Processing, Analysis and Visualization (MIPAV)

- Application and API

- _Medical Image Processing, Analysis and Visualization (MIPAV)_

## MIPAV

- LUT adjustment

- Interactive level set

- VOI boundary modification

- Magnifying glass

- Measurement tool

## Image model
n-dimensional.
able to store: boolean, signed/unsigned byte, signed/unsigned short, int, long, float, double, ARGB.
Read/Write images in the following formats
Raw (images with a fixed header can be treated as Raw).
Analyze (NIH has a large number of legacy datasets). 
DICOM 3.0. (query/retrieve/send & file access, sanitize, modify tags)
Tiff (8, 16 bit, indexed, RGB).
JPEG, GIF, PICT, BMP, PNG, PSD, PCX, XPM, TGA ....
1D, 2D, and 3D Volumes Of Interest (VOIs)
32,767 Unique VOIs.
Can be contiguous or separate contours.
easily modified (move, add, and delete points).
Statistics of VOI can be calculated.
# of pixels, volume/area, average pixel intensity, ... 

- n-dimensional.
- able to store: boolean, signed/unsigned byte, signed/unsigned short, int, long, float, double, ARGB.
**Read/Write images in the following formats**
- Raw (images with a fixed header can be treated as Raw).
- Analyze (NIH has a large number of legacy datasets).
- DICOM 3.0. (query/retrieve/send & file access, sanitize, modify tags)
- Tiff (8, 16 bit, indexed, RGB).
- JPEG, GIF, PICT, BMP, PNG, PSD, PCX, XPM, TGA ....
**1D, 2D, and 3D Volumes Of Interest (VOIs)**
- 32,767 Unique VOIs.
- Can be contiguous or separate contours.
- easily modified (move, add, and delete points).
- Statistics of VOI can be calculated.
- # of pixels, volume/area, average pixel intensity, ...

- _Major functionality__ _

## Algorithms (2D and fully 3D)
Anisotropic diffusion 
blurs data but not across edges.
Boundary evolution
Bspline snake
Statistical region grow
Level set 
Convolution class
convolve arbitrary kernels (2D & 3D) with an image.
Morphological operators (2D and 3D)
erode, dilate, open, close, distance function ....
Reslice 3D dataset to isotropic voxels 
linear, cubic, cubic bspline.
Watershed (2D and 3D)
Semi-interactive segmentation method.
Graph of intensity profiles

- Anisotropic diffusion
- blurs data but not across edges.
- Boundary evolution
- Bspline snake
- Statistical region grow
- Level set
- Convolution class
- convolve arbitrary kernels (2D & 3D) with an image.
- Morphological operators (2D and 3D)
- erode, dilate, open, close, distance function ....
- Reslice 3D dataset to isotropic voxels
- linear, cubic, cubic bspline.
- Watershed (2D and 3D)
- Semi-interactive segmentation method.
- Graph of intensity profiles

- _Major functionality (cont.)__ _

## Major functionality (cont.) 

**Algorithms (cont)**
- Filters: Gaussian blurring, Laplacian, curvature, other higher order derivatives, median, gradient magnitude etc.
- Image math (add, subtract, multiply, divide, AND, OR, XOR)
- Linear algebra library
- Matrix multiply, divide, inversion ...
- SVD, LU and QR decomposition, Eigenvalue decomposition, ...
- Registration
- Landmark – least squares
- Image transformations or resample with nearest neighbor, tri-linear, or bSpline interpolation methods.
**View**
- LUT control
- Gray, Heated Metal, Spectrum, user defined, read/write.
- Sophisticated remapping of intensity to LUT
- Movie loop
- 3D Lightbox
- Magnifying glass
- Overlay two images with opacity control (e.g. structural MR and functional MR)

## DICOM Model

- Patient

- Database

- DICOM Server

- Images

- Imaging Workstation (MIPAV)

- Receiver

- Query

- Images

- Image

- Processing

- &

- Visualization

**Imaging Device**

**PACS**

- Send

**Internet**

## MIPAV’s DICOM Interface

- Multiple retrievals, each executing in its own thread.
- Hierarchical retrievals (patient, series, study, image)
- Host configurable (interface with any DICOM server – e.g. NIH PACS)
- Send images to PACS
- Sanitize image tags of personal information
- Modify tags, add private tags

## Computed Tomography (CT) 

- Axial CT image

## MIPAV:  Tri-Planar View of MRI Dataset

- _MIPAV:  Tri-Planar View of MRI Dataset_

## Anisotropic Diffusion(Perona, Malik)

- _Anisotropic Diffusion(Perona, Malik)_

- diffused

- blurred

- original

## MIPAV:  Example of Ansiotropic Diffusion on CT Images of the Kidney.

- _MIPAV:  Example of Ansiotropic Diffusion on CT Images of the Kidney._

- Before

- After

## Watershed Segmentation

- I(x)

- IGM(x)

- Catchment basins

- Watershed “lines”

- Find the lowest point in each basin and begin “flooding”.
- When two basins meet a watershed point (1D) is identified and a dam is formed.
- Continue flooding until all basins and watershed points are formed.
- Note: this method can produce over segmentations.

- Dam

- Gradient magnitude

- Gradient magnitude

## Watershed Segmentation: Interactive 

- Find the lowest point in each basin identified by a Region of Interest (ROI) and force the gradient magnitude to zero at all ROIs. Begin “flooding” in those regions.
- When two basins meet a watershed line (2D) is identified and a dam is formed.
- Continue flooding until all ROI basins until all regions are flooded.

- MRI image with ROIs

- Segmented basins

## 3D Watershed Segmentation of Kidney in CT Image

## CT Image With Segmented Tumor Regions(volumes calculated in 3D)

## Light Microscopy

- Rat brain cross-section with outlined lesion, generated in a _single user interaction_ using the interactive level set technique.

**Heyes, Melvyn P., Ph.D., (LNT, NIMH)**
- CNS compartments in rat brains
- Segmentation
- Quantification

## Trey Sunderland  M.D. Chief, Geriatric Psychiatry Branch
Francois Lalonde, PhD
National Institute of Mental Health
Functional MRI studies.
Segmentation, quantification and visualization.

**Francois Lalonde, PhD**
- National Institute of Mental Health
- Functional MRI studies.
- Segmentation, quantification and visualization.

## Watershed segmentation example

- A. The VOI defining the watershed basin for tissue to be removed is drawn in red.  The VOI defining the watershed basin for tissue to be retained is drawn in yellow.

- _Watershed segmentation example_

- B. The mask generated by the watershed segmentation.

- C. The resulting deskulled brain after multiplying the mask and original image.  No additional editing was required at this particular brain slice.

## Axial MR slice of the human brain with overlaid functional MR image

- A.  The opacity of the functional image is emphasized to better assess the extent of activation.

- _Axial MR slice of the human brain with overlaid functional MR image_

- B. The opacity of the functional image is reduced to better identify brain structures underlying the area of activation.

## Fluoroscopy

**Sonies, Barbara**, PhD (CC)
**    ****Chi-Fishman, Gloria**, PhD (CC)
- Swallow study
- Registration
- Quantification

## Summary

- Easily access to data (DICOM 3.0, many image file formats)
- Platform independent (PC, MAC, UNIX workstations)
- General tool to meet a broad range of requirements of researchers in the IRP.
- Flexibility to meet specific requirements (i.e. plug-ins)