Original Research

Mammographic CAD: Correlation of regions in ipsilateral views - a pilot study

J Padayachee, M J Alport, W ID Rae
South African Journal of Radiology | Vol 13, No 3 | a497 | DOI: https://doi.org/10.4102/sajr.v13i3.497 | © 2009 J Padayachee, M J Alport, W ID Rae | This work is licensed under CC Attribution 4.0
Submitted: 24 February 2009 | Published: 20 August 2009

About the author(s)

J Padayachee,, South Africa
M J Alport, Department of Physics, University of KwaZulu-Natal, South Africa
W ID Rae, Department of Medical Physics, University of the Free State, South Africa

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Background: Radiologists analyse both standard mammographic 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 cranio-caudal 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=0.80±0.17 with an average C=0.46±0.26. The second algorithm (using GLCMs and a mutual information similarity metric) achieved an average AUC=0.77±0.25 with an average C=0.50±0.42. The latter algorithm also performed remarkably well with the matching of malignant masses and achieved an average AUC=0.96±0.05 with an average C=0.90±0.21.


mammography, computer-aided diagnosis, image processing


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Crossref Citations

1. Dependence of Shape-Based Descriptors and Mass Segmentation Areas on Initial Contour Placement Using the Chan-Vese Method on Digital Mammograms
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