To cite this record:
Detection of Masses in Digital Mammograms using K-means and Support Vector Machine
Oliveira Martins, Leonardo de (Federal University of Maranhao (Sao Luis, Brasil))
Braz Junior, Geraldo (Pontifical Catholic University of Rio de Janeiro)
Corrêa Silva, Aristófanes (Pontifical Catholic University of Rio de Janeiro)
Cardoso de Paiva, Anselmo (Pontifical Catholic University of Rio de Janeiro)
Gattass, Marcelo (Federal University of Maranhao (Sao Luis, Brasil))

Date: 2009
Abstract: Breast cancer is a serious public health problem in several countries. Computer Aided Detection/Diagnosis systems (CAD/CADx) have been used with relative success aiding health care professionals. The goal of such systems is contribute on the specialist task aiding in the detection of different types of cancer at an early stage. This work presents a methodology for masses detection on digitized mammograms using the K-means algorithm for image segmentation and co-occurrence matrix to describe the texture of segmented structures. Classification of these structures is accomplished through Support Vector Machines, which separate them in two groups, using shape and texture descriptors: masses and non-masses. The methodology obtained 85% of accuracy.
Rights: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Language: Anglès.
Document: article ; recerca ; publishedVersion
Subject: Mamografia ; Detecció assistida per ordinador ; Co-ocurrència de matriu ; Detección asistida por ordenador ; Co-ocurrencia de matriz ; Mammogram ; Computer-Aided Detection ; Co-occurrence matrix ; K-means ; Support Vector Machine
Published in: ELCVIA : Electronic Letters on Computer Vision and Image Analysis, V. 8 n. 2 (2009) p. 39-50, ISSN 1577-5097

12 p, 272.3 KB

The record appears in these collections:
Articles > Published articles > ELCVIA : Electronic Letters on Computer Vision and Image Analysis
Articles > Research articles

 Record created 2010-01-18, last modified 2015-04-02

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