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 5,518 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.
Pereira
Roberto R
RR
Centro de Ciências das Imagens e Física Médica, Hospital das Clinicas da Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo, Avenida dos Bandeirantes 3900-Campus Universitário, Ribeirão Preto, SP, Brazil. chicob@cci.fmrp.usp.br
Azevedo Marques
Paulo M
PM
Honda
Marcelo O
MO
Kinoshita
Sergio K
SK
Engelmann
Roger
R
Muramatsu
Chisako
C
Doi
Kunio
K
D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D002138:Calibration; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D008327:Mammography; D019047:Phantoms, Imaging; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted; D012044:Regression Analysis; D018709:Statistics, Nonparametric