Article Sidebar
Article Details
The author(s) retain copyright on work published by AOSIS unless specified otherwise.
Licensing and publishing rights
Author(s) of work published by AOSIS are required to grant AOSIS the unlimited rights to publish the definitive work in any format, language and medium, for any lawful purpose. AOSIS requires journal authors to publish their work in open access under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence.
Read more here: http://creativecommons.org/licenses/by/4.0/.
The authors retain the non-exclusive right to do anything they wish with the published article(s), provided attribution is given to the applicable journal with details of the original publication, as set out in the official citation of the article published in the journal. The retained right specifically includes the right to post the article on the authors’ or their institution’s websites or in institutional repositories.
Previously published work may have been published under a different licence. We advise the community that if they would like to reuse the work to consult the applicable licence at article level.
Main Article Content
Mammographic CAD: Correlation of regions in ipsilateral views – a pilot study
Abstract
views of a breast to confirm the presence of abnormalities and reduce false-positives. However, at present, no computer-aided diagnosis
system uses ipsilateral mammograms to confirm the presence of suspicious features.
Aim. The aim of this study was to develop image-processing algorithms
that can be used to match a suspicious feature from one mammographic view to the same feature in another mammographic view of the same breast. This algorithm can be incorporated into a computer-aided diagnosis package to confirm the presence of suspicious features.
Method. The algorithms were applied to 68 matched pairs of craniocaudal
and mediolateral-oblique mammograms. The results of this pilot study take the form of maps of similarity. A novel method of evaluating the similarity maps is presented, using the area under the receiver operating characteristic curve (AUC) and the contrast (C) between the area of the matched region and the background of the similarity map.
Results and conclusions. The first matching algorithm (using texture measures extracted from a grey-level co-occurrence matrix (GLCM) and a Euclidean distance similarity metric) achieved an average AUC of 0.80±0.17 with an average C of 0.46±0.26. The second algorithm (using GLCMs and a mutual information similarity metric) achieved an average AUC of 0.77±0.25 with an average C of 0.50±0.42. The latter algorithm also performed remarkably well with the matching of malignant masses and achieved an average AUC of 0.96±0.05 with an average C of 0.90±0.21. In conclusion, texture analysis methods used with suitable similarity metrics allow a suspicious feature from one mammographic view to be matched with the same suspicious feature in other mammographic views of the same breast.
