Detection of stellate distortions in mammograms

Abstract

Malignant densities in mammograms have an irregular appearance and frequently are surrounded by a radiating pattern of linear spicules. In this paper a method is described to detect such stellate patterns. This method is based on statistical analysis of a map of pixel orientations. If an increase of pixels pointing to a region is found, this region is marked as suspicious, especially if such an increase is found in many directions. Orientations of the image intensity map are determined at each pixel using a multiscale approach. At a given scale, accurate line-based orientation estimates are obtained from the output of three-directional, second-order, Gaussian derivative operators. The orientation at the scale at which these operators have maximum response is selected. If a line-like structure is present at a given site, this method provides an estimate of the orientation of this structure, whereas in other cases the image noise will generate a random orientation. The pixel orientation map is used to construct two operators which are sensitive to radial patterns of straight lines. Combination of the output of these operators using a classifier allows for detection of stellate patterns. Different classification methods have been compared and results obtained on a common database are presented. Around 90% of the malignant cases were detected at rate of one false positive (FP) per image.

Interactive Computer-Aided Diagnosis of Breast Masses: Computerized Selection of Visually Similar Image Sets From a Reference Library

“…Unlike previously developed computer schemes that detect and classify spiculated and nonspiculated masses [18][19][20][21], our scheme uses a simple summary index to quantify spiculation levels of any suspected masses. As do most of current CAD schemes, our scheme used the low-resolution image to define the initial boundary contour of a suspected mass, thereby reducing image noise and increasing the computation efficiency of the region growth algorithm.…”

“…Several techniques have been developed and tested to detect and classify between spiculated and non-spiculated masses [19][20][21]. One group used the analysis of locally oriented edges (ALOE) and a binary decision tree (BDT) [19].…”

“…One group used the analysis of locally oriented edges (ALOE) and a binary decision tree (BDT) [19]. Since spiculation frequently appears as linear structures with a positive image contrast and the structures lie in all radial directions to the mass center, a second group used the gradient directions (orientation) at pixels on, or close to, spiculation to detect and classify spiculated masses [20]. A third group defined a 30-pixel-wide band around the segmented mass boundary contour and then used a threshold and labeling algorithm to detect and classify between spiculated and non-spiculated masses [21].…”

Review on Mammogram Mass Detection by Machine Learning Techniques

“…ROIs are preprocessed using a wavelet-based transformation method and a thresholding technique is applied to exclude microcalcifications and mass lesions. Kegelmeyer et al [25] developed a method to detect spiculated masses using a set of 5 features for each pixel. They used the standard deviation of a local edge orientation histogram (ALOE) and the output of four spatial filters which are a subset of Law's texture features.…”

Finding Orientated Line Patterns in Digital Mammographic Images

“…Various methods have been described for detecting the patterns of radiating linear structures which characterize stellate lesions [3][4][5]. They all depend however on obtaining an estimate of line strength, orientation and (sometimes) scale at each pixel.…”