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Increasing the speed of medical image processing in MatLab.

M Bister¹, Cs Yap, Kh Ng

¹School of Electrical and Electronic Engineering, The University of Nottingham, Malaysia Campus, Semenyih, Selangor, Malaysia.

Biomedical Imaging and Intervention Journal

|May 27, 2011

Summary

Medical image processing in MatLab(®) can be fast. Proper programming techniques like vectorization enable MatLab(®) applications to match C language performance for large datasets.

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Area of Science:

Medical image processing
Scientific computing
Algorithm development

Background:

MatLab(®) is recognized for rapid algorithm development.
It is often perceived as too slow for routine medical image processing of large datasets, such as high-resolution CT scans.
Modern medical imaging generates substantial data volumes requiring efficient processing.

Purpose of the Study:

To demonstrate that MatLab(®) can achieve performance comparable to C language for medical image processing tasks.
To showcase efficient implementation strategies for large-scale medical image analysis in MatLab(®).

Main Methods:

Implementing key medical image processing algorithms in MatLab(®) using optimized programming practices.
Techniques include vectorization, pre-allocation, and specialization for performance enhancement.

Keywords:

MatLab®image processing optimisation specialisation vectorisation

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Specific algorithms tested: bilinear interpolation, watershed segmentation, and volume rendering.

Main Results:

Optimized MatLab(®) implementations achieved performance on par with C language.
Demonstrated the feasibility of processing large medical image datasets efficiently within the MatLab(®) environment.
Successful implementation of complex algorithms like watershed segmentation and volume rendering.

Conclusions:

MatLab(®), when programmed with optimization techniques, is suitable for high-performance medical image processing.
The perceived slowness of MatLab(®) for large datasets can be overcome with proper coding practices.
This approach enables efficient analysis of modern medical imaging data within a familiar development environment.