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A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates

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Lossy to lossless object-based coding of 3-D MRI data.

Gloria Menegaz¹, Jean-Philippe Thiran

¹Audio-Visual Communications Laboratory, School of Computer and Communication Sciences, Swiss Federal Institute of Technology, CH-1015 Lausanne, Switzerland. Gloria.Menegaz@epfl.ch

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 6, 2008

Summary

This study introduces a 3D object-based coding system for medical imaging, prioritizing diagnostic regions for efficient data compression. The system achieves competitive performance, with one method outperforming others on specific datasets.

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Area of Science:

Medical Imaging
Data Compression
Signal Processing

Background:

Volumetric data in medical imaging requires efficient compression strategies.
Object-based coding can leverage diagnostic relevance of specific regions.
Existing methods may not optimally handle 3D volumetric data for region-specific access.

Purpose of the Study:

To develop and evaluate a fully three-dimensional (3-D) object-based coding system for volumetric medical data.
To exploit the diagnostic relevance of different data regions for efficient rate allocation.
To enable independent access and reconstruction of objects within volumetric data at up-to-lossless quality.

Main Methods:

Decorrelation of volumetric data using a 3-D discrete wavelet transform with a lifting steps scheme for integer-to-integer mapping.
Implementation of two 3-D coding strategies: embedded zerotree coding (EZW-3D) and multidimensional layered zero coding (MLZC), generalized for region of interest (ROI)-based processing.
Encoding of extra coefficients for object boundaries to prevent artifacts, leading to a slight bitstream overhead.

Main Results:

Both MLZC and EZW-3D demonstrated competitive compression performances on head magnetic resonance images.
The best MLZC mode outperformed other state-of-the-art techniques on a specific dataset.
The system allows for independent reconstruction of objects at various quality levels.

Conclusions:

The proposed 3-D object-based coding system effectively compresses volumetric medical data.
The system offers flexibility in accessing and reconstructing specific regions of interest.
MLZC shows strong potential for advanced medical image compression applications.