Detection of Spiculated Lesions in Digital Mammograms Using a Novel Image Analysis Technique

We have applied novel computational image analysis algorithms to detect malignant masses in mammograms. Our analysis focuses on spiculated lesions, which are particularly challenging for computer-aided detection methods. The algorithm uses the principle of locally-normalised correlation coefficients to identify patterns of motifs representing a spiculated feature. A combination of correlation maps indicating the maximum correlation of the motif at each position relative to the mammogram, and of the pattern of angles for which this maximum is observed, are used to locate spiculated lesions in a verified test dataset. The test set of images has been annotated by an expert reader, and allows objective evaluation of computer-aided detection procedures. In a blind test using an automated procedure our method identified 54% of the lesion locations in the set of test images. This initial blind testing and comparison with expert annotated images, representing a ground truth, indicates feasibility for our approach. Optimisation of the procedure is expected to yield improved performance.

Authors and Affiliations

1. Faculty of Life Sciences, University of Manchester, Oxford Road, Manchester, M13 9PT, UK

   Ashley Seepujak, Tomas Adomavicius, Sergey Dolgobrodov & Alan M. Roseman

2. Centre for Imaging Sciences, Institute for Population Health, University of Manchester, Oxford Road, Manchester, M13 9PT, UK

   Emmanouil Moschidis, Xin Chen & Susan M. Astley

3. Nightingale Centre and Genesis Prevention Centre, University Hospital of South Manchester, Manchester, M23 9LT, UK

   Anthony Maxwell

Editors and Affiliations

1. Department of Intelligent Image Information, Gifu University Graduate School of Medicine, 1-1 Yanagido, 501-1194, Gifu, Gifu, Japan

   Hiroshi Fujita , Takeshi Hara  & Chisako Muramatsu ,  & 

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