Improvement of mammographic mass characterization using spiculation measures and morphological features

Abstract

We are developing new computer vision techniques for characterization of breast masses on mammograms. We had previously developed a characterization method based on texture features. The goal of the present work was to improve our characterization method by making use of morphological features. Toward this goal, we have developed a fully automated, three-stage segmentation method that includes clustering, active contour, and spiculation detection stages. After segmentation, morphological features describing the shape of the mass were extracted. Texture features were also extracted from a band of pixels surrounding the mass. Stepwise feature selection and linear discriminant analysis were employed in the morphological, texture, and combined feature spaces for classifier design. The classification accuracy was evaluated using the area Az under the receiver operating characteristic curve. A data set containing 249 films from 102 patients was used. When the leave-one-case-out method was applied to partition the data set into trainers and testers, the average test Az for the task of classifying the mass on a single mammographic view was 0.83 +/- 0.02, 0.84 +/- 0.02, and 0.87 +/- 0.02 in the morphological, texture, and combined feature spaces, respectively. The improvement obtained by supplementing texture features with morphological features in classification was statistically significant (p = 0.04). For classifying a mass as malignant or benign, we combined the leave-one-case-out discriminant scores from different views of a mass to obtain a summary score. In this task, the test Az value using the combined feature space was 0.91 +/- 0.02. Our results indicate that combining texture features with morphological features extracted from automatically segmented mass boundaries will be an effective approach for computer-aided characterization of mammographic masses.

Fractal Analysis of Contours of Breast Masses in Mammograms

“…Combinations of shape factors and texture measures have been shown to be more effective in the classification of breast masses 8,10 than either type of features on its own. Previous studies 7 on statistical measures of texture as proposed by Haralick et al 47 have shown that such measures are sensitive to differences in the nature of images across databases.…”

“…The methods need to be tested with contours automatically obtained by image processing methods for the detection and delineation of masses in mammograms. 8,9 It is worth noting that, in a study by Sahiner et al, 8 in which the classification performance of several shape factors and texture measures was compared with a data set of automatically extracted regions corresponding to 122 benign breast masses and 127 malignant tumors, FF was found to give the best individual performance with A z = 0.82. This result not only indicates the importance of shape in the analysis of breast masses, but also that shape factors computed from automatically extracted contours can yield good results in discriminating between benign masses and malignant tumors.…”

“…Several studies have proposed measures of texture and edge sharpness to discriminate between benign masses and malignant tumors. 5,7Y10 Sahiner et al 8 and Alto et al 10 explored several combinations of morphological and texture measures to classify breast masses.…”

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

“…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].…”

“…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]. Despite these efforts, adequate detection of mass spiculation remains a technical challenge.…”