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

Office of Lab Dir - Science & Technology

Research output: Contribution to journal › Article › peer-review

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

Access to Document

10.1117/1.1810529

Cite this

@article{25779f337fcf4de598bd73f182716798,

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",

author = "Chan, \{Hung Yam\} and Hamed Sari-Sarraf and Grinstead, \{Bradley I.\} and Gleason, \{Shaun S.\}",

year = "2004",

month = dec,

doi = "10.1117/1.1810529",

language = "English",

volume = "43",

pages = "2986--2993",

journal = "Optical Engineering",

issn = "0091-3286",

publisher = "Society of Photo-Optical Instrumentation Engineers",

number = "12",

}

TY - JOUR

T1 - Content-based compression of mammograms with fractal-based segmentation and a modified JPEG2000

AU - Chan, Hung Yam

AU - Sari-Sarraf, Hamed

AU - Grinstead, Bradley I.

AU - Gleason, Shaun S.

PY - 2004/12

Y1 - 2004/12

N2 - 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.

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.

KW - Content-based compression

KW - Fractal-based segmentation

KW - JPEG2000

KW - Mammograms

KW - Region-of-interest coding

UR - https://www.scopus.com/pages/publications/12844257594

U2 - 10.1117/1.1810529

DO - 10.1117/1.1810529

M3 - Article

AN - SCOPUS:12844257594

SN - 0091-3286

VL - 43

SP - 2986

EP - 2993

JO - Optical Engineering

JF - Optical Engineering

IS - 12

ER -

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

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this