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Hadamard-based image decomposition and compression.

I Valova¹, Y Kosugi

¹Tokyo Institute of Technology, Yokohama, Japan.

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society

|February 24, 2001

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This study introduces an efficient algorithm for grayscale image compression, particularly effective for medical imaging like magnetic resonance imaging (MRI). The method significantly reduces data size, achieving high compression rates by exploiting image redundancy.

Area of Science:

Image processing and computer vision
Medical imaging analysis
Data compression algorithms

Background:

Grayscale image compression is crucial for efficient storage and transmission of medical data.
Existing methods may not fully exploit the inherent redundancy in certain image types, such as MRI.
Developing advanced algorithms is necessary to improve compression efficiency and maintain image quality.

Purpose of the Study:

To develop a general algorithm for the decomposition and compression of grayscale images.
To enable efficient storage and transmission of half-tone medical images, specifically magnetic resonance imaging (MRI) of the human brain.
To achieve high compression rates by leveraging image decomposition and clustering techniques.

Main Methods:

Related Experiment Videos

A general algorithm for grayscale image decomposition using Hadamard waveforms.

A dynamic adaptive clustering procedure with potential functions for similarity measure and a multi-iteration reclustering phase.

Application of run-length coding for further compression of retained image information.

Main Results:

The algorithm successfully decomposes images into a functional relation with Hadamard waveforms.
Dynamic adaptive clustering efficiently groups image data, enabling significant data omission (nearly half for MRI).
Achieved high compression rates for MRI brain images, outperforming some existing literature methods.

Conclusions:

The developed algorithm offers an efficient method for grayscale image compression.
The combination of decomposition, clustering, and run-length coding provides competitive performance.
This approach is particularly beneficial for compressing redundant medical image data like MRI.