Paper
25 April 1997 Characterization of masses on mammograms: significance of using the rubber band straightening transform
Berkman Sahiner, Heang-Ping Chan, Nicholas Petrick, Mitchell M. Goodsitt, Mark A. Helvie M.D.
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Abstract
The rubber-band straightening transform (RBST) was developed for characterization of mammographic masses as malignant or benign. The RBST maps a region surrounding a segmented mass on a mammogram onto the Cartesian plane. In this study, the effectiveness of texture features extracted from the RBST images was compared with the effectiveness of those extracted from the original images. Texture features were extracted from (1) a region of interest (ROI) centered at the mass; (2) a 40-pixel-wide gray-scale region surrounding the perimeter of the mass; and (3) the RBST image. Two types of texture features were extracted; spatial gray level dependence (SGLD) features and run-length statistics (RLS) features. Linear discriminant analysis and leave-one-case- out methods were used for classification in the individual or combined feature spaces. The classification accuracy was evaluated by receiver operating characteristic (ROC) analysis and the area Az under the ROC curve. CLABROC analysis was used to estimate the statistical significance of the difference between features extracted using the three different approaches. On a database of 255 ROIs containing biopsy-proven masses, the Az value was 0.92 when combined SGLD and RLS features extracted from RBST images were used for classification. In comparison, the combined texture features extracted from the entire ROIs and the mass perimeter regions resulted in Az values of 0.83 and 0.85, respectively. The improvement in Az obtained by using RBST images was statistically significant (p less than 0.05). Similar levels of significance were observed when the classification was performed in the SGLD feature space alone or the RLS feature space alone.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Berkman Sahiner, Heang-Ping Chan, Nicholas Petrick, Mitchell M. Goodsitt, and Mark A. Helvie M.D. "Characterization of masses on mammograms: significance of using the rubber band straightening transform", Proc. SPIE 3034, Medical Imaging 1997: Image Processing, (25 April 1997); https://doi.org/10.1117/12.274135
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Cited by 3 scholarly publications.
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KEYWORDS
Feature extraction

Image segmentation

Mammography

Statistical analysis

Image classification

Databases

Matrices

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