Cascio, Donato and Fauci, Francesco and Magro, Rosario and Raso, Giuseppe and Bellotti, Roberto and De Carlo, Francesco and Tangaro, Sonia and De Nunzio, Giorgio and Quarta, Maurizio and Forni, Giustina and Lauria, Adele and Fantacci, Maria Evelina and Retico, Alessandra and Masala, Giovanni Luca Christian and Oliva, Piernicola and Bagnasco, Stefano and Cheran, Sorin Cristian and Lopez Torres, Ernesto (2006) Mammogram segmentation by contour searching and mass lesions classification with neural network. IEEE Transactions on Nuclear Science, Vol. 53 (5), p. 2827-2833. ISSN 0018-9499. Article.
The mammography is the most effective procedure for an early diagnosis of the breast cancer. In this paper, an algorithm for detecting masses in mammographic images will be presented. The database consists of 3762 digital images acquired in several hospitals belonging to the MAGIC-5 collaboration (Medical Applications on a Grid Infrastructure Connection). A reduction of the whole image's area under investigation is achieved through a segmentation process, by means of a ROI Hunter algorithm, without loss of meaningful information. In the following classification step, feature extraction plays a fundamental role: some features give geometrical information, other ones provide shape parameters. Once the features are computed for each ROI, they are used as inputs to a supervised neural network with momentum. The output neuron provides the probability that the ROI is pathological or not. Results are provided in terms of ROC and FROC curves: the area under the ROC curve was found to be Az=0.862plusmn0.007, and we get a 2.8 FP/Image at a sensitivity of 82%. This software is included in the CAD station actually working in the hospitals belonging to the MAGIC-5 Collaboration
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