Dissimilarity Application in Digitized Mammographic Images Classification

Bottigli, U., Golosio, B., Masala, Giovanni Luca, Oliva, P., Stumbo, S., Cascio, D., Fauci, F., Magro, R., Raso, G., Vasile, M, and others. (2006) Dissimilarity Application in Digitized Mammographic Images Classification. Journal of Systemics, Cybernetics and Informatics, 4 (3). pp. 18-22. ISSN 1690-4524. E-ISSN 1690-4524. (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:92189)

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: http://www.iiisci.org/Journal/SCI/FullText.asp?var...

Abstract

Purpose of this work is the development of an automatic classification system which could be useful for radiologists in the investigation of breast cancer. The software has been designed in the framework of the MAGIC-5 collaboration. In the traditional way of learning from examples of objects the classifiers are built in a feature space. However, an alternative ways can be found by constructing decision rules on dissimilarity (distance) representations. In such a recognition process a new object is described by its distances to (a subset of) the training samples. The use of the dissimilarities is especially of interest when features are difficult to obtain or when they have a little discriminative power. In the automatic classification system the suspicious regions with high probability to include a lesion are extracted from the image as regions of interest (ROIs). Each ROI is characterized by some features extracted from co-occurrence matrix containing spatial statistics information on ROI pixel grey tones. A dissimilarity representation of these features is made before the classification. A feed-forward neural network is employed to distinguish pathological records, from nonpathological ones by the new features. The results obtained in terms of sensitivity and specificity will be presented.

Item Type:	Article
Uncontrolled keywords:	Dissimilarity; Breast Cancer; Neural Network; Cooccurrence matrix; Computer Aided Detection
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:	07 Dec 2021 15:23 UTC
Last Modified:	05 Nov 2024 12:57 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/92189 (The current URI for this page, for reference purposes)

University of Kent Author Information

Masala, Giovanni Luca.

Creator's ORCID:	https://orcid.org/0000-0001-6734-9424
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