Abstract
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.
Highlights
According to the Cancer Association of South Africa, breast cancer is currently the most common cancer among women worldwide, and is second to cervical cancer among South African women
Radiologists consider the distance from the nipple to the centroid of a suspicious feature in one mammographic view, and search an annular region in another mammographic view at about the same radial distance from the nipple corresponding to the suspicious feature
This paper presents an algorithm that finds a suspicious feature in one standard mammographic view and uses the position and characteristics of the feature to find it in another standard mammographic view
Summary
According to the Cancer Association of South Africa, breast cancer is currently the most common cancer among women worldwide, and is second to cervical cancer among South African women. Most importantly in these dual-view algorithms, the suspicious features are identified in both standard mammographic views and information is correlated between pairs of suspicious features to identify matches and thereby reduce the number of false-positives All these methods rely on some form of training (e.g. linear discriminant analysis, artificial neural networks) and only perform as well as the data set that was used for the training. Any algorithms based on training generally perform very poorly when applied to situations outside the scope of the training data Radiologists analyse both standard mammographic views of a breast to confirm the presence of abnormalities and reduce false-positives. 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
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