Potential improvement of computerized classification for malignant versus benign mammographic microcalcification clusters with the use of special views

We are investigating the use of special mammographic views, i.e. magnification and spot compression views, into computerized classification schemes for malignant versus benign microcalcification clusters. Radiologists often recall patients with suspicious lesions for additional views. We expect that the fusion, or combination, of information extracted from special views and conventional mammograms will improve the performances of computer-aided diagnosis (CAD) schemes for classification of malignant versus benign microcalcification clusters. It has been shown that reading with CAD improves radiologists' performances. The CAD scheme is applied separately to conventional mammograms and special views. The scheme consists of segmentation of manually-identified microcalcifications, followed by extraction of microcalcification-based geometrical and textural features in addition to cluster-based features. Linear discriminant analysis (LDA) is then applied to each image to classify a cluster as malignant or benign. The leave-one-out technique is used for training and testing the classifier. The resulting likelihoods of malignancy output from the LDA applied separately to the conventional mammograms and special views are combined using the maximum classifier output. We applied the proposed technique to a database of 75 biopsy-proven patients (31 malignant and 44 benign). The case-based performances for classification of malignant versus benign microcalcification clusters resulted in an area under the receiver operating characteristic (ROC) curves, Az, of: 0.771 on conventional mammograms, 0.845 on special views, and 0.908 when merging likelihoods of malignancy from conventional mammograms and special views. These preliminary results indicate that the proposed technique of combining information from special views with that from conventional mammograms can improve computerized classification of microcalcification clusters.

[1]  Yulei Jiang,et al.  An ROC comparison of four methods of combining information from multiple images of the same patient. , 2004, Medical physics.

[2]  D. Kopans,et al.  Comparison of two screen-film combinations in contact and magnification mammography: detectability of microcalcifications. , 1988, Radiology.

[3]  M. Säbel,et al.  Recent developments in breast imaging. , 1996, Physics in medicine and biology.

[4]  Laura M. Yarusso,et al.  Radial gradient-based segmentation of mammographic microcalcifications: observer evaluation and effect on CAD performance. , 2004, Medical physics.

[5]  E A Sickles,et al.  Further experience with microfocal spot magnification mammography in the assessment of clustered breast microcalcifications. , 1980, Radiology.

[6]  N. Petrick,et al.  Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces. , 1998, Medical physics.

[7]  N. Petrick,et al.  Improvement of radiologists' characterization of mammographic masses by using computer-aided diagnosis: an ROC study. , 1999, Radiology.

[8]  Kunio Doi,et al.  Magnification film mammography: image quality and clinical studies. , 1977 .

[9]  K Doi,et al.  Improvement in radiologists' detection of clustered microcalcifications on mammograms. The potential of computer-aided diagnosis. , 1990, Investigative radiology.

[10]  Maryellen L. Giger,et al.  Computerized analysis of multiple-mammographic views: potential usefulness of special view mammograms in computer-aided diagnosis , 2001, IEEE Transactions on Medical Imaging.

[11]  A. Jones,et al.  Reduction in mortality from breast cancer , 2005, BMJ : British Medical Journal.

[12]  L. Tabár,et al.  REDUCTION IN MORTALITY FROM BREAST CANCER AFTER MASS SCREENING WITH MAMMOGRAPHY Randomised Trial from the Breast Cancer Screening Working Group of the Swedish National Board of Health and Welfare , 1985, The Lancet.

[13]  J. Swets,et al.  Enhanced interpretation of diagnostic images. , 1988, Investigative radiology.

[14]  L. Tabár,et al.  Potential contribution of computer-aided detection to the sensitivity of screening mammography. , 2000, Radiology.

[15]  M. Giger,et al.  Improving breast cancer diagnosis with computer-aided diagnosis. , 1999, Academic radiology.

[16]  M. Giger,et al.  Computerized analysis of mammographic parenchymal patterns for breast cancer risk assessment: feature selection. , 2000, Medical physics.