Computerized classification of malignant and benign microcalcifications on mammograms: texture analysis using an artificial neural network.
We investigated the feasibility of using texture features extracted from mammograms to predict whether the presence of microcalcifications is associated with malignant or benign pathology. Eighty-six mammograms from 54 cases (26 benign and 28 malignant) were used as case samples. All lesions had been recommended for surgical biopsy by specialists in breast imaging. A region of interest (ROI) containing the microcalcifications was first corrected for the low-frequency background density variation. Spatial grey level dependence (SGLD) matrices at ten different pixel distances in both the axial and diagonal directions were constructed from the background-corrected ROI. Thirteen texture measures were extracted from each SGLD matrix. Using a stepwise feature selection technique, which maximized the separation of the two class distributions, subsets of texture features were selected from the multi-dimensional feature space. A backpropagation artificial neural network (ANN) classifier was trained and tested with a leave-one-case-out method to recognize the malignant or benign microcalcification clusters. The performance of the ANN was analysed with receiver operating characteristic (ROC) methodology. It was found that a subset of six texture features provided the highest classification accuracy among the feature sets studied. The ANN classifier achieved an area under the ROC curve of 0.88. By setting an appropriate decision threshold, 11 of the 28 benign cases were correctly identified (39% specificity) without missing any malignant cases (100% sensitivity) for patients who had undergone biopsy. This preliminary result indicates that computerized texture analysis can extract mammographic information that is not apparent by visual inspection. The computer-extracted texture information may be used to assist in mammographic interpretation, with the potential to reduce biopsies of benign cases and improve the positive predictive value of mammography.
Chan
H P
HP
Department of Radiology, University of Michigan, Ann Arbor 48109-0326, USA. chanhp@umich.edu
Sahiner
B
B
Petrick
N
N
Helvie
M A
MA
Lam
K L
KL
Adler
D D
DD
Goodsitt
M M
MM
D001941:Breast Diseases / Q000145:classification* / Q000000981:diagnostic imaging*; D001943:Breast Neoplasms / Q000145:classification* / Q000175:diagnosis / Q000000981:diagnostic imaging*; D002114:Calcinosis / Q000000981:diagnostic imaging* / Q000209:etiology; D005069:Evaluation Studies as Topic; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008327:Mammography / Q000379:methods* / Q000706:statistics & numerical data; D008433:Mathematics; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D012189:Retrospective Studies