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Evaluating classification strategies for detection of circumscribed masses in digital mammograms

Constantinidis, A.S. and Fairhurst, Michael and Deravi, Farzin and Hanson, M. and Wells, C.P. and Chapman-Jones, C. (1999) Evaluating classification strategies for detection of circumscribed masses in digital mammograms. In: Seventh International Conference on Image Processing And Its Applications, 1999. Conference Publications . IEEE, pp. 435-439. ISBN 0-85296-717-9. (doi:10.1049/cp:19990359) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1049/cp:19990359

Abstract

This paper reports work to investigate a computer aided diagnosis scheme for detection of circumscribed digitised masses in mammograms. The model used consists of three stages: The first segments the mammogram into regions of interest (ROIs), the second stage tries to eliminate the majority of false positives reported, and finally a third stage utilises more sophisticated processing to make a final decision on the likelihood of an ROI being a genuine mass. The paper is concerned with an evaluation of different classifiers in the difficult task of detection of masses, and the relationship between correct and incorrect detection, but also points the way to developing more sophisticated-and potentially more reliable-techniques based on the integration of multiple classifiers within a single processing structure. The study used 4 different classifiers: multivariate Gaussian classifier (MVG), radial basis function (RBF), Q-vector median (QVM), 1-nearest neighbour (1NN). Three different feature types were used: 3/sup rd/ order normalized Zernike moments, texture features, and a combined moment and textures feature vector. Various feature selection techniques have also been investigated to obtain feature sets which improve the performance of each classifier.

Item Type: Book section
DOI/Identification number: 10.1049/cp:19990359
Uncontrolled keywords: classification strategies; detection; circumscribed masses; digital mammograms; computer aided diagnosis scheme; regions of interest; false positives; likelihood; multivariate Gaussian classifier; radial basis function; Q-vector median; 1-nearest neighbour; feature types; 3/sup rd/ order normalized Zernike moments; texture features; feature selection
Subjects: Q Science
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: M. Nasiriavanaki
Date Deposited: 24 Jun 2009 07:54 UTC
Last Modified: 01 Aug 2019 10:32 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/17229 (The current URI for this page, for reference purposes)
Deravi, Farzin: https://orcid.org/0000-0003-0885-437X
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