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Mammogram Segmentation by Contour Searching and Mass Lesions Classification With Neural Network

Cascio, D., Fauci, F., Magro, R., Raso, G., Bellotti, R., De Carlo, F., Tangaro, S., De Nunzio, G., Quarta, M., Forni, G., and others. (2006) Mammogram Segmentation by Contour Searching and Mass Lesions Classification With Neural Network. IEEE Transactions on Nuclear Science, 53 (5). pp. 2827-2833. ISSN 0018-9499. E-ISSN 1558-1578. (doi:10.1109/TNS.2006.878003) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:92315)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL
https://doi.org/10.1109/TNS.2006.878003

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 A Z =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

Item Type: Article
DOI/Identification number: 10.1109/TNS.2006.878003
Uncontrolled keywords: Lesions; Neural networks; Image segmentation; Hospitals; Collaborative software; Mammography; Image databases; Digital images; Collaboration; Breast cancer; image processing; neural network
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Amy Boaler
Date Deposited: 14 Dec 2021 10:09 UTC
Last Modified: 16 Dec 2021 15:47 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/92315 (The current URI for this page, for reference purposes)
Masala, Giovanni Luca: https://orcid.org/0000-0001-6734-9424
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