Comparative study of feature classification methods for mass lesion recognition in digitized mammograms

Masala, Giovanni Luca, Tangaro, S., Golosio, B., Oliva, P., Stumbo, S., Bellotti, R., De Carlo, F, Gargano, G., Cascio, D., Fauci, F., and others. (2007) Comparative study of feature classification methods for mass lesion recognition in digitized mammograms. Nuovo Cimento - Societa Italiana di Fisica Sezione C, 30 (3). pp. 305-316. ISSN 0390-5551. E-ISSN 1826-9885. (doi:10.1393/ncc/i2007-10241-y) (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:92187)

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://dx.doi.org/10.1393/ncc/i2007-10241-y

Abstract

In this work a comparison of different classification methods for the identification of mass lesions in digitized mammograms is performed. These methods, used in order to develop Computer Aided Detection (CAD) systems, have been implemented in the framework of the MAGIC-5 Collaboration. The system for identification of mass lesions is based on a three-step procedure: a) preprocessing and segmentation, b) region of interest (ROI) searching, c) feature extraction and classification. It was tested on a very large mammographic database (3369 mammographic images from 967 patients). Each ROI is characterized by eight features extracted from a co-occurrence matrix containing spatial statistics information on the ROI pixel grey tones.

Item Type:	Article
DOI/Identification number:	10.1393/ncc/i2007-10241-y
Uncontrolled keywords:	mass lesion recognition, mammogram
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 14:35 UTC
Last Modified:	05 Nov 2024 12:57 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/92187 (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
CReDIT Contributor Roles:

Depositors only (login required):

Altmetric

Download Statistics

Total unique views for this document in KAR since July 2020. For more details click on the image.