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

Braz Junior, Geraldo

Cardoso de Paiva, Anselmo

Corrêa Silva, Aristófanes

Gattass, Marcelo

Oliveira Martins, Leonardo de

ELCVIA Electronic Letters on Computer Vision and Image Analysis

Revistes Catalanes amb Accés Obert

Detection of Masses in Digital Mammograms using K-means and Support Vector Machine

Diposit Digital de Documents de la UAB

Female breast cancer is a major cause of death in occidental countries. CAD/CADx systems can aidradiologists in detection and diagnostic of lesions in mammograms. In this work, we present a methodologyto detect masses from mammograms. The K-means clustering algorithm is used to split the mammogramsin regions. Each region is then classified through a Support Vector Machine (SVM) as mass or non-massregion. SVM is a machine-learning method, based on the principle of structural risk minimization, whichperforms well when applied to data outside the training set. We use a set of textural and shape measures todetect suspicious regions, as bening and malignant masses. Each textural measure (contrast, homogeneity,inverse difference moment, entropy and energy) is computed through the co-ocurrence matrix technique.The methodology obtained an accuracy of 93.11% discriminate mass from non-mass elements

de Oliveira Martins, Leonardo

Correa Silva, Aristófanes

Electronic Letters on Computer Vision and Image Analysis (ELCVIA - Universitat Autònoma de Barcelona)

Detection of Masses in Digital Mammograms using K-Means and Support Vector Machine

Female breast cancer is a major cause of death in occidental countries. CAD/CADx systems can aid
radiologists in detection and diagnostic of lesions in mammograms. In this work, we present a methodology
to detect masses from mammograms. The K-means clustering algorithm is used to split the mammograms
in regions. Each region is then classified through a Support Vector Machine (SVM) as mass or non-mass
region. SVM is a machine-learning method, based on the principle of structural risk minimization, which
performs well when applied to data outside the training set. We use a set of textural and shape measures to
detect suspicious regions, as bening and malignant masses. Each textural measure (contrast, homogeneity,
inverse difference moment, entropy and energy) is computed through the co-ocurrence matrix technique.
The methodology obtained an accuracy of 93.11% discriminate mass from non-mass elements

Leonardo de Oliveira Martins

Geraldo Braz Junior

Aristófanes Correa Silva

Anselmo Cardoso de Paiva

Marcelo Gattass

Directory of Open Access Journals

https://elcvia.cvc.uab.es/article/download/v8-n2-de-oliveira-braz-correa-et-al/235

Detection of Masses in Digital Mammograms using K-means and Support Vector Machine

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Revistes Catalanes amb Accés Obert

Diposit Digital de Documents de la UAB

Electronic Letters on Computer Vision and Image Analysis (ELCVIA - Universitat Autònoma de Barcelona)

Directory of Open Access Journals