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Systematical Parameterization, Storage and Representation of Volumetric DICOM Data for Visualization:

3D Presentation States (3DPR)

• Dr. M. Alper SELVER

Dokuz Eylul University Electrical and Electronics Engineering

Research Group

• Prof. Dr. rer. nat.

Walter Hillen

• Dr. Felix Fischer
• Prof. Dr. Oğuz Dicle
• Dr. Sinem Gezer
• Dr. Alper Selver

FH-Juelich Medical Informatics

DEU Radiology DEU Electrical & Electronics Engineering

Ultimate Goal

Snapshots Videos Movie files Automatic Rendering + all interactions + possible refinements

Part I: Compression of Segmented Data

• Archiving result of a segmentation task allows the

representation of the segmented volume at a later time.

• The segmented volume can be stored in a binary format,

which can be restored by a simple combination of the

• riginal data with this binary information.
• Since, the sizes of the segmented binary data have high

memory requirements; a lossless compression method should be employed for efficient archiving.

• this study examines different approaches for compression

and their suitability for restoring binary segmented data.

• To evaluate the compressive properties, multiple test cases

with diverse spatial structures and acquired with different modalities from clinical practice have been used.

Methods: (All 2D except octree)

• Losless storage of segmented data slice by slice in uncompressed bitmap (BMP), Portable Bitmap (PBM)
• Run-length encoding (RLE): effective when a symbol repeatedly occurs in the data. (Java based Birle).
• CCITT T.4/T.6: CCITT T.4 consists of a combination of binary run-length coding and a modified Huffman
• coding. Its disadvantage in short run lengths is corrected in CCITT T.6 by using a Modified Modified Read

(MMR) process. (IrfanView 3.98 CCITT Fax 3 and CCITT Fax 4 were selected, respectively).

• JBIG2: (Joint Bi-level Image Processing Group) a standard specially designed for binary. JBIG2 is the

current version of the standard established by the ISO (International Organization for Standardization) and is also responsible for the JPEG and JPEG2000 standard. (C++ program, jbig2enc).

• JPEG 2000: is used for both lossy and lossless compression. The compression process consists of several

steps mainly based on the wavelet transform. (The free command-line program GeoJasper)

• ZIP: based on the Deflate algorithm, which is composed of two encoding methods: LZ77 and Huffman.

With the LZ77 method identical symbol sequences are determined and coded. Then, the coded symbol sequences are compressed using the Huffman method. (IrfanView: by lossless conversion of the PBM file in the graphic format PNG data, images were automatically compressed using the ZIP method).

• LZW: uses a sliding window over the data set. The window consists of two buffers: search and preview.

The search buffer contains the last coded symbols and is used as a dictionary for symbols from the preview

• buffer. The LZW method uses separate windows instead of sliding ones. (IrfanView, TIFF format with LZW).
• Octree: carried out in two separate files: The values of the voxels are consecutively written to one file. In

a second file, the dimensions of the VOI can be stored. With the help of these two files, it is possible to restore the binarized segmentation result as a volume.

+ Aorta (CT with contrast medium)

• 250 slices, slice thickness: 1.5 mm
• Segmentation: Connected Threshold
• VOI: 139x322x288 voxels

Kidney (CT, MR) (different results for interior kidney)

• MR coronal, 72 slices ST: 1.4 mm (smoothing: MR-2)
• CT series with 238 slices, ST: 1 mm
• Segmentation: Fast Marching
• VOI (MR:121x52x205) (CT: 114x101x112) voxels.

+ Skull (CT) data set consists

• 61 slices –Slice thickness 0.7 mm
• Segmentation: Connected Threshold
• VOI: 175x214x302 voxels

+ Skeleton (CT): ribs + hip

• 250 slices, slice thickness: 1.5 mm
• Segmentation: Connected Threshold
• VOI: 239x146x288 voxels.

Datasets

Results:

Computational Performance

Conclusion

• For compression of binary volume data, the JBIG2 method is well suited;

however, it is actually optimized for the data reduction of 2D binary images.

• It would be better to use a method that can be applied directly to the

volume of data without having to split them beforehand into individual slices.

• By this way, correlations in the data may be used not only in two, but also

in three dimensions.

• This requirement could be achieved by modification of the JBIG2 method

as follows:

– The high compression factor is partly due to the use of a context- based coding, in which the context of the neighbors of the pixel to be encoded. – In current version of JBIG2, the neighbors to be encoded are always the pixels in the same 2D plane (i.e. slice). – By enlarging the vicinity in 3D (i.e. to the adjacent slices), the JBIG2 method could be used without a change in the actual encoding process with a presumably more efficient compression of the binary volume data.

Part II: Parameterization of Segmentation

• Final goal: Use segmentation parameters in

teleradiology

In this study, the goal is:

• not to present yet another liver segmentation

algorithm

• but to report parametric analysis and detailed

evaluation of two widely used 3-D segmentation strategies implemented in Insight Toolkit (i.e. under optimized programming conditions)

Segmentation Process

Pre-processing

• VOI Selection
• Filtering

Gaussian Median Anisotropic - diffusion Segmentation Connected Threshold (CoT) Seed points Upper-Lower limits Fast Marching (FM) Seed points Maximum gradient Number of iterations Post-processing Filtering

(for surface smoothing)

Gaussian Median Anisotropic - diffusion 3D-Visualization Volume measurements 3D-rendering

DATASETS

liver-heart boundary

Challanges

right kidney appearance atypical liver shape liver composed of multiple components in 2-D 24 Datasets Average 90 slices 3.2mm Slice thickness

Connected Threshold (CoT)

• a member of region

growing (RG) process

• Initialize seed points
• Determine upper and

lower threshold levels

ROI from image Gaussian anisotropic diffusion k=5 k=9 k=15 leakage Median

Fast Marching

• Active contour based (edge-oriented methods)

level set equation velocity in the normal direction FM equation

Java Based Object Oriented Implementation

Application Example (Fast Marching)

Evaluation

• Volume
• Surface (Symmetric Surface Distance-SSD)

Each surface voxel of reference volume (VR) Closest surface voxel of the segmented volume (VS) SSD: the set of surface voxels VS are given by S(VS), to the shortest distance of an arbitrary surface voxel of VR

Evaluation

Results