Computerized characterization of masses on mammograms: The rubber band straightening transform and texture analysis

Abstract

A new rubber band straightening transform (RBST) is introduced for characterization of mammographic masses as malignant or benign. The RBST transforms a band of pixels surrounding a segmented mass onto the Cartesian plane (the RBST image). The border of a mammographic mass appears approximately as a horizontal line, and possible speculations resemble vertical lines in the RBST image. In this study, the effectiveness of a set of directional textures extracted from the images before the RBST. A database of 168 mammograms containing biopsy-proven malignant and benign breast masses was digitized at a pixel size of 100 microns x 100 microns. Regions of interest (ROIs) containing the biopsied mass were extracted from each mammogram by an experienced radiologist. A clustering algorithm was employed for automated segmentation of each ROI into a mass object and background tissue. Texture features extracted from spatial gray-level dependence matrices and run-length statistics matrices were evaluated for three different regions and representations: (i) the entire ROI; (ii) a band of pixels surrounding the segmented mass object in the ROI; and (iii) the RBST image. Linear discriminant analysis was used for classification, and receiver operating characteristic (ROC) analysis was used to evaluate the classification accuracy. Using the ROC curves as the performance measure, features extracted from the RBST images were found to be significantly more effective than those extracted from the original images. Features extracted from the RBST images yielded an area (Az) of 0.94 under the ROC curve for classification of mammographic masses as malignant and benign.

Classification of breast masses using a committee machine of artificial neural networks

“…With the objective to detect and extract the characteristics of lesions, several techniques of image processing have been proposed. [8][9][10][11][12][13][14][15] The artificial neural network ͑ANN͒ is an example of computational intelligence techniques that has been used to classify tumors related to breast cancer. 12,[16][17][18][19][20][21][22][23] Several types of network architecture, such as the multilayer perceptron ͑MLP͒, the single-layer perceptron ͑SLP͒, 19 and ra-dial basis functions ͑RBFs͒ 20 have been used for the classification of breast masses and tumors based on measures of shape, texture, and edge sharpness.…”

Computer‐aided detection of breast masses on mammograms: Dual system approach with two‐view analysis

“…Detailed definition of the RBST and the RLS texture features for mammographic masses can be found in the literature. 18 …”

Application of omni-directional texture analysis to SAR images for levee landslide detection

“…This paper investigates two different resampling patterns. The first is based on the Rubber Band Straightening Transform (RBST) [5]; since the regions are always a circle, the re-sampling pattern resembles radial spokes. RBST has been used extensively in automated mammographic image analysis, where texture features are extracted from a banded region around the perimeter of a mass.…”