Computer‐aided detection of breast masses on full field digital mammograms

Abstract

We are developing a computer-aided detection (CAD) system for breast masses on full field digital mammographic (FFDM) images. To develop a CAD system that is independent of the FFDM manufacturer's proprietary preprocessing methods, we used the raw FFDM image as input and developed a multiresolution preprocessing scheme for image enhancement. A two-stage prescreening method that combines gradient field analysis with gray level information was developed to identify mass candidates on the processed images. The suspicious structure in each identified region was extracted by clustering-based region growing. Morphological and spatial gray-level dependence texture features were extracted for each suspicious object. Stepwise linear discriminant analysis (LDA) with simplex optimization was used to select the most useful features. Finally, rule-based and LDA classifiers were designed to differentiate masses from normal tissues. Two data sets were collected: a mass data set containing 110 cases of two-view mammograms with a total of 220 images, and a no-mass data set containing 90 cases of two-view mammograms with a total of 180 images. All cases were acquired with a GE Senographe 2000D FFDM system. The true locations of the masses were identified by an experienced radiologist. Free-response receiver operating characteristic analysis was used to evaluate the performance of the CAD system. It was found that our CAD system achieved a case-based sensitivity of 70%, 80%, and 90% at 0.72, 1.08, and 1.82 false positive (FP) marks/image on the mass data set. The FP rates on the no-mass data set were 0.85, 1.31, and 2.14 FP marks/image, respectively, at the corresponding sensitivities. This study demonstrated the usefulness of our CAD techniques for automated detection of masses on FFDM images.

Bilateral analysis based false positive reduction for computer‐aided mass detection

“…We have previously developed a mass detection system for unilateral mammograms. [23][24][25] The system is used for mass candidate detection in the current study. The system performs mass detection in two steps.…”

“…The same 13 texture measures are extracted from the central region containing the detected object and the peripheral regions within each ROI (referred to as the local texture features) at four distances and two angles with a total of 104 (13×2×4) features from the central region and 104 features as the difference of the corresponding features in the central and the peripheral regions. 25 Twelve morphological features are extracted from the object segmented within the ROI. 24 ,25 Five of them are based on the normalized radial length (NRL), defined as the Euclidean distance from the object centroid to each of its edge pixels and normalized relative to the maximum radial length for the object.…”

“…Unilateral CAD system: The unilateral linear discriminant analysis (LDA) classifier uses only the SGLD texture features as input predictor variables as described previously. 25 The stepwise LDA feature selection strategy with simplex optimization16 was used to select the best texture feature subset and reduce the dimensionality of the feature space. Twofold cross validation was used to train and test the CAD systems, as discussed below.…”

Detection of Cancerous Masses in Mammograms by Template Matching: Optimization of Template Brightness Distribution by Means of Evolutionary Algorithm

“…Region-based methods, which divide the image into homogeneous and spatially connected regions. An example of these methods is shown in the paper by Wei et al [11]. In that paper, a prescreening method identifies the mass candidates.…”

Malignant and Benign Mass Segmentation in Mammograms Using Active Contour Methods

“…-Region-based methods allow the image to be divided into homogeneous and connected regions, e.g., according to their texture properties. In the publication by Wei et al [21], potential lesions are first extracted by their clusterization based on the region-growing method. The morphological and spatial relationships of grey levels, i.e., the characteristics of texture features, are extracted for every suspicious-looking object.…”