Content-based compression of mammograms with fractal-based segmentation and a modified JPEG2000

Hung Yam Chan; Hamed Sari-Sarraf; Bradley I. Grinstead; Shaun S. Gleason

doi:10.1117/1.1810529

Content-based compression of mammograms with fractal-based segmentation and a modified JPEG2000

Hung Yam Chan, Hamed Sari-Sarraf, Bradley I. Grinstead, Shaun S. Gleason

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8 Scopus citations

Abstract

We describe a strategy for the content-based compression of mammograms. In this two-step strategy, the clinically important structures are first identified via a fractal-based segmentation method. Then, a modified version of JPEG2000 is applied in such a way that lossless compression is applied to the extracted structures from the first step, while a lossy compression is applied to the remaining regions. Preliminary results demonstrate that this strategy can achieve high compression ratios (up to 50:1) without compromising the diagnostic quality of the mammograms.

Original language	English
Pages (from-to)	2986-2993
Number of pages	8
Journal	Optical Engineering
Volume	43
Issue number	12
DOIs	https://doi.org/10.1117/1.1810529
State	Published - Dec 2004

Keywords

Content-based compression
Fractal-based segmentation
JPEG2000
Mammograms
Region-of-interest coding

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10.1117/1.1810529

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title = "Content-based compression of mammograms with fractal-based segmentation and a modified JPEG2000",

abstract = "We describe a strategy for the content-based compression of mammograms. In this two-step strategy, the clinically important structures are first identified via a fractal-based segmentation method. Then, a modified version of JPEG2000 is applied in such a way that lossless compression is applied to the extracted structures from the first step, while a lossy compression is applied to the remaining regions. Preliminary results demonstrate that this strategy can achieve high compression ratios (up to 50:1) without compromising the diagnostic quality of the mammograms.",

keywords = "Content-based compression, Fractal-based segmentation, JPEG2000, Mammograms, Region-of-interest coding",

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AU - Gleason, Shaun S.

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AB - We describe a strategy for the content-based compression of mammograms. In this two-step strategy, the clinically important structures are first identified via a fractal-based segmentation method. Then, a modified version of JPEG2000 is applied in such a way that lossless compression is applied to the extracted structures from the first step, while a lossy compression is applied to the remaining regions. Preliminary results demonstrate that this strategy can achieve high compression ratios (up to 50:1) without compromising the diagnostic quality of the mammograms.

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KW - Fractal-based segmentation

KW - JPEG2000

KW - Mammograms

KW - Region-of-interest coding

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Content-based compression of mammograms with fractal-based segmentation and a modified JPEG2000

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Keywords

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