MAMMOGRAM SEGMENTATION BY CONTOUR SEARCHING AND MASS LESIONS CLASSIFICATION WITH NEURAL NETWORK

Giuseppe Raso, Francesco Fauci, Donato Cascio, Rosario Magro, Bagnasco, Cascio, Cheran, Raso, Francesco De Carlo, Retico, De Nunzio, Fantacci, De Carlo, Bellotti, Masala, Oliva, Quarta, Francesco Fauci, Forni, LauriaE. Lopez Torres, Magro, Tangaro

Risultato della ricerca: Articlepeer review

90 Citazioni (Scopus)

Abstract

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 = 0.862 ± 0.007, 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.
Lingua originaleEnglish
pagine (da-a)2827-2833
Numero di pagine7
RivistaIEEE Transactions on Nuclear Science
Volume53
Stato di pubblicazionePublished - 2006

All Science Journal Classification (ASJC) codes

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  • ???subjectarea.asjc.2100.2104???
  • ???subjectarea.asjc.2200.2208???

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