Keir Bovis

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In this paper we investigate a new approach to the classification of mammographic images according to breast type. The classification of breast density in this study is motivated by its use as prior knowledge in the image processing pipeline. By utilising this knowledge at different stages including enhancement, segmen-tation and feature extraction, its(More)
The main aim of this paper is to propose a novel set of metrics that measure the quality of the image enhancement of mammographic images in a computer-aided detection framework aimed at automatically finding masses using machine learning techniques. Our methodology includes a novel mechanism for the combination of the metrics proposed into a single(More)
In this paper we study the identification of masses in digital mammograms using texture analysis. A number of texture measures are calculated for bilateral difference images showing regions of interest. The measurements are made on co-occurrence matrices in four different direction giving a total of seventy features. These features include the ones proposed(More)
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