Evaluation of an automated segmentation method based on performances of an automated classification method

We have developed a computerized method for the automatic segmentation of mass lesions on digitized mammograms using gray-level region-growing. This segmentation technique has been incorporated into our automated classification scheme which consists of 1) automated segmentation 2) automated feature-extraction and (3) determination of likelihood of malignancy using an automated classifier. The feature-extraction techniques extract various features from the neighborhoods of the computer-grown mass region to characterize the margin, shape and density of the mass. The automated classifier is then used to merge these computer-extracted features into a number related to the likelihood of malignancy. To evaluate quantitatively the performance of the segmentation technique, we calculate the area of overlap between the computer-grown mass regions and radiologist-identified mass regions. In addition, we substitute the computer identified margins with radiologist-identified margins in our classification scheme. The performances of individual features as well as the classification scheme in terms of their ability to differentiate between benign and malignant masses are evaluated using receiver operating characteristic (ROC) analysis. The performance obtained based on the mass regions identified by the automated segmentation technique and by radiologists are compared to evaluate the adequacy of the region growing. Results from this study show that the automated segmentation technique tends to undergrow the mass regions by approximately one quarter of the area identified by the radiologists. However, the superior performances of the computer-extracted features and the classification scheme based on the analysis of the computer-grown mass regions indicated that the computer-grown mass regions are sufficient for the subsequent techniques of feature-extraction and classification to accurately characterize mass lesions.