A comparison of clustered microcalcifications automated detection methods in digital mammogram

Wan Mimi Diyana Wan Zaki, Julie Larcher, Rosli Besar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

This paper presents the comparison of three automated methods for an early detection of breast cancer. It specifically detects clusters of microcalcifications (MCCs), which are associated with a high probability of malignancy. The proposed methods are based on several image processing concepts, such as morphological processing, fractal analysis, adaptive wavelet transform, local maxima detection and high-order statistics (HOS) tests. We apply these methods on a set of mammograms (MIAS database) to test their efficiency and computation time. It shows that the HOS test proved to be the most efficient, and give reliable results for every mammogram tested.

Original languageEnglish
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Pages385-388
Number of pages4
Volume2
Publication statusPublished - 2003
Externally publishedYes
Event2003 IEEE International Conference on Accoustics, Speech, and Signal Processing - Hong Kong, Hong Kong
Duration: 6 Apr 200310 Apr 2003

Other

Other2003 IEEE International Conference on Accoustics, Speech, and Signal Processing
CountryHong Kong
CityHong Kong
Period6/4/0310/4/03

Fingerprint

Higher order statistics
statistics
Fractals
Wavelet transforms
Image processing
breast
wavelet analysis
image processing
fractals
Processing
cancer

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing
  • Acoustics and Ultrasonics

Cite this

Wan Zaki, W. M. D., Larcher, J., & Besar, R. (2003). A comparison of clustered microcalcifications automated detection methods in digital mammogram. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (Vol. 2, pp. 385-388)

A comparison of clustered microcalcifications automated detection methods in digital mammogram. / Wan Zaki, Wan Mimi Diyana; Larcher, Julie; Besar, Rosli.

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Vol. 2 2003. p. 385-388.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wan Zaki, WMD, Larcher, J & Besar, R 2003, A comparison of clustered microcalcifications automated detection methods in digital mammogram. in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. vol. 2, pp. 385-388, 2003 IEEE International Conference on Accoustics, Speech, and Signal Processing, Hong Kong, Hong Kong, 6/4/03.
Wan Zaki WMD, Larcher J, Besar R. A comparison of clustered microcalcifications automated detection methods in digital mammogram. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Vol. 2. 2003. p. 385-388
Wan Zaki, Wan Mimi Diyana ; Larcher, Julie ; Besar, Rosli. / A comparison of clustered microcalcifications automated detection methods in digital mammogram. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Vol. 2 2003. pp. 385-388
@inproceedings{0b7af6158b5b41f4a745819c508b3b7d,
title = "A comparison of clustered microcalcifications automated detection methods in digital mammogram",
abstract = "This paper presents the comparison of three automated methods for an early detection of breast cancer. It specifically detects clusters of microcalcifications (MCCs), which are associated with a high probability of malignancy. The proposed methods are based on several image processing concepts, such as morphological processing, fractal analysis, adaptive wavelet transform, local maxima detection and high-order statistics (HOS) tests. We apply these methods on a set of mammograms (MIAS database) to test their efficiency and computation time. It shows that the HOS test proved to be the most efficient, and give reliable results for every mammogram tested.",
author = "{Wan Zaki}, {Wan Mimi Diyana} and Julie Larcher and Rosli Besar",
year = "2003",
language = "English",
volume = "2",
pages = "385--388",
booktitle = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

}

TY - GEN

T1 - A comparison of clustered microcalcifications automated detection methods in digital mammogram

AU - Wan Zaki, Wan Mimi Diyana

AU - Larcher, Julie

AU - Besar, Rosli

PY - 2003

Y1 - 2003

N2 - This paper presents the comparison of three automated methods for an early detection of breast cancer. It specifically detects clusters of microcalcifications (MCCs), which are associated with a high probability of malignancy. The proposed methods are based on several image processing concepts, such as morphological processing, fractal analysis, adaptive wavelet transform, local maxima detection and high-order statistics (HOS) tests. We apply these methods on a set of mammograms (MIAS database) to test their efficiency and computation time. It shows that the HOS test proved to be the most efficient, and give reliable results for every mammogram tested.

AB - This paper presents the comparison of three automated methods for an early detection of breast cancer. It specifically detects clusters of microcalcifications (MCCs), which are associated with a high probability of malignancy. The proposed methods are based on several image processing concepts, such as morphological processing, fractal analysis, adaptive wavelet transform, local maxima detection and high-order statistics (HOS) tests. We apply these methods on a set of mammograms (MIAS database) to test their efficiency and computation time. It shows that the HOS test proved to be the most efficient, and give reliable results for every mammogram tested.

UR - http://www.scopus.com/inward/record.url?scp=0141478671&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0141478671&partnerID=8YFLogxK

M3 - Conference contribution

VL - 2

SP - 385

EP - 388

BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

ER -