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Usefulness of texture analysis for computerized classification of breast lesions on mammograms.

This work presents the usefulness of texture features in the classification of breast lesions in 5518 images of regions of interest, which were obtained from the Digital Database for Screening Mammography that included microcalcifications, masses, and normal cases. Sixteen texture features were used...

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Published in:Journal of Digital Imaging Vol. 20; no. 3; pp. 248 - 256
Main Authors: Pereira RR Jr., Azevedo Marques PM, Honda MO, Kinoshita SK, Engelmann R, Muramatsu C, Doi K
Format: diagnostic images equations & formulas research tables/charts Journal Article
Published: Springer Nature Sep2007
Online Access:View this record in EBSCOhost
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      dt: Sep2007
      vid: 20
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      pub: Springer Nature
      place: New York, New York
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        2009699274
        10.1007/s10278-006-9945-8
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        atl: Usefulness of texture analysis for computerized classification of breast lesions on mammograms.
      aug:
        au:
          Pereira RR Jr.
          Azevedo Marques PM
          Honda MO
          Kinoshita SK
          Engelmann R
          Muramatsu C
          Doi K
        affil: Centro de Ciências das Imagens e Física Médica, Hospital das Clinicas da Faculdade de Medicina de Ribeirao Preto da Universidade de Sao Paulo, Avenida dos Bandeirantes 3900-Campus Universitário, 14048900, Ribeirao Preto, SP, Brazil, chicob@cci.fmrp.usp.br.
      sug:
        subj:
          Breast Neoplasms Classification
          Breast Neoplasms Radiography
          Diagnosis, Computer Assisted
          Mammography
          Breast Neoplasms Diagnosis
          Evaluation Research
          Female
          Funding Source
          Neural Networks (Computer)
          Regression
          ROC Curve
          Human
          Female
      ab: This work presents the usefulness of texture features in the classification of breast lesions in 5518 images of regions of interest, which were obtained from the Digital Database for Screening Mammography that included microcalcifications, masses, and normal cases. Sixteen texture features were used, i.e., 13 were based on the spatial gray-level dependence matrix and 3 on the wavelet transform. The nonparametric K-NN classifier was used in the classification stage. The results obtained from receiver operating characteristic analysis indicated that the texture features can be used for separating normal regions and lesions with masses and microcalcifications, yielding the area under the curve (AUC) values of 0.957 and 0.859, respectively. However, the texture features were not very effective for distinguishing between malignant and benign lesions because the AUC was 0.617 for masses and 0.607 for microcalcifications. The study showed that the texture features can be used for the detection of suspicious regions in mammograms.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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