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Salient features in mammograms using Gabor filters and clustering

We present a method for locating salient features in mammograms. The perceptual importance or salience of image pixels can be studied using a statistical measure of pixel-based features. The “outliers” or greatest values for this measure can be regarded as salient because in an imaging sense, the outliers tend to contribute to the local feature contrast. Our method finds important image features first by spatially decomposing the image using a process that models the human vision system. Salience maps then are created using the Mahalanobis distance, and a scalar visibility metric then is analyzed. Six mammographers each read three mammograms. Each mammogram had two views. During screening, eye position data were recorded. A K-means algorithm then was applied to identify fixation clusters. Following decomposition, Analysis of Variance (ANOVA) then was performed to examine the effects of observer experience, spatial frequency, and discrimination using the maximum value of the visibility metric. This pilot study shows statistically significant differences in true positive and true negative features, and in both the features and filters used to discriminate true negative results between expert and resident observers. This type of analysis can be useful for finding fixation tendencies that result from the available spatial features during mammogram screening.